• Have you ever wondered how creativity and innovation come together to create magic? The launch of Switch 2 has made incredible things possible, and one of the most exciting outcomes is the development of Donkey Kong Bananza!

    It's not just about the stunning details; it's about the destruction and the freedom it gives developers to push boundaries and explore new heights! This is a reminder that embracing change and new technology can lead to extraordinary adventures and breakthroughs!

    Let’s celebrate the power of innovation and the joy it brings to our gaming experiences! Keep dreaming big and pushing forward!

    #Switch2 #DonkeyKong #GameDevelopment #Innovation #GamingCommunity
    🌟✨ Have you ever wondered how creativity and innovation come together to create magic? 🎮💥 The launch of Switch 2 has made incredible things possible, and one of the most exciting outcomes is the development of Donkey Kong Bananza! 🐵🎉 It's not just about the stunning details; it's about the destruction and the freedom it gives developers to push boundaries and explore new heights! 🚀💪 This is a reminder that embracing change and new technology can lead to extraordinary adventures and breakthroughs! Let’s celebrate the power of innovation and the joy it brings to our gaming experiences! Keep dreaming big and pushing forward! 💖🎊 #Switch2 #DonkeyKong #GameDevelopment #Innovation #GamingCommunity
    WWW.CREATIVEBLOQ.COM
    How Switch 2 made Donkey Kong Bananza possible – for a surprising reason
    It's not only the detail – it's the destruction, developers say.
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  • What a world we live in when scientists finally unlock the secrets to the axolotls' ability to regenerate limbs, only to reveal that the key lies not in some miraculous regrowth molecule, but in its controlled destruction! Seriously, what kind of twisted logic is this? Are we supposed to celebrate the fact that the secret to regeneration is, in fact, about knowing when to destroy something instead of nurturing and encouraging growth? This revelation is not just baffling; it's downright infuriating!

    In an age where regenerative medicine holds the promise of healing wounds and restoring functionality, we are faced with the shocking realization that the science is not about building up, but rather about tearing down. Why would we ever want to focus on the destruction of growth molecules instead of creating an environment where regeneration can bloom unimpeded? Where is the inspiration in that? It feels like a slap in the face to anyone who believes in the potential of science to improve lives!

    Moreover, can we talk about the implications of this discovery? If the key to regeneration involves a meticulous dance of destruction, what does that say about our approach to medical advancements? Are we really expected to just stand by and accept that we must embrace an idea that says, "let's get rid of the good stuff to allow for growth"? This is not just a minor flaw in reasoning; it's a fundamental misunderstanding of what regeneration should mean for us!

    To make matters worse, this revelation could lead to misguided practices in regenerative medicine. Instead of developing therapies that promote healing and growth, we could end up with treatments that focus on the elimination of beneficial molecules. This is absolutely unacceptable! How dare the scientific community suggest that the way forward is through destruction rather than cultivation? We should be demanding more from our researchers, not less!

    Let’s not forget the ethical implications. If the path to regeneration is paved with the controlled destruction of vital components, how can we trust the outcomes? We’re putting lives in the hands of a process that promotes destruction. Just imagine the future of medicine being dictated by a philosophy that sounds more like a dystopian nightmare than a beacon of hope.

    It is high time we hold scientists accountable for the direction they are taking in regenerative research. We need a shift in focus that prioritizes constructive growth, not destructive measures. If we are serious about advancing regenerative medicine, we must reject this flawed notion and demand a commitment to genuine regeneration—the kind that nurtures life, rather than sabotages it.

    Let’s raise our voices against this madness. We deserve better than a science that advocates for destruction as the means to an end. The axolotls may thrive on this paradox, but we, as humans, should expect far more from our scientific endeavors.

    #RegenerativeMedicine #Axolotl #ScienceFail #MedicalEthics #Innovation
    What a world we live in when scientists finally unlock the secrets to the axolotls' ability to regenerate limbs, only to reveal that the key lies not in some miraculous regrowth molecule, but in its controlled destruction! Seriously, what kind of twisted logic is this? Are we supposed to celebrate the fact that the secret to regeneration is, in fact, about knowing when to destroy something instead of nurturing and encouraging growth? This revelation is not just baffling; it's downright infuriating! In an age where regenerative medicine holds the promise of healing wounds and restoring functionality, we are faced with the shocking realization that the science is not about building up, but rather about tearing down. Why would we ever want to focus on the destruction of growth molecules instead of creating an environment where regeneration can bloom unimpeded? Where is the inspiration in that? It feels like a slap in the face to anyone who believes in the potential of science to improve lives! Moreover, can we talk about the implications of this discovery? If the key to regeneration involves a meticulous dance of destruction, what does that say about our approach to medical advancements? Are we really expected to just stand by and accept that we must embrace an idea that says, "let's get rid of the good stuff to allow for growth"? This is not just a minor flaw in reasoning; it's a fundamental misunderstanding of what regeneration should mean for us! To make matters worse, this revelation could lead to misguided practices in regenerative medicine. Instead of developing therapies that promote healing and growth, we could end up with treatments that focus on the elimination of beneficial molecules. This is absolutely unacceptable! How dare the scientific community suggest that the way forward is through destruction rather than cultivation? We should be demanding more from our researchers, not less! Let’s not forget the ethical implications. If the path to regeneration is paved with the controlled destruction of vital components, how can we trust the outcomes? We’re putting lives in the hands of a process that promotes destruction. Just imagine the future of medicine being dictated by a philosophy that sounds more like a dystopian nightmare than a beacon of hope. It is high time we hold scientists accountable for the direction they are taking in regenerative research. We need a shift in focus that prioritizes constructive growth, not destructive measures. If we are serious about advancing regenerative medicine, we must reject this flawed notion and demand a commitment to genuine regeneration—the kind that nurtures life, rather than sabotages it. Let’s raise our voices against this madness. We deserve better than a science that advocates for destruction as the means to an end. The axolotls may thrive on this paradox, but we, as humans, should expect far more from our scientific endeavors. #RegenerativeMedicine #Axolotl #ScienceFail #MedicalEthics #Innovation
    Scientists Discover the Key to Axolotls’ Ability to Regenerate Limbs
    A new study reveals the key lies not in the production of a regrowth molecule, but in that molecule's controlled destruction. The discovery could inspire future regenerative medicine.
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  • Sharpen the story – a design guide to start-up’s pitch decks

    In early-stage start-ups, the pitch deck is often the first thing investors see. Sometimes, it’s the only thing. And yet, it rarely gets the same attention as the website or the socials. Most decks are pulled together last minute, with slides that feel rushed, messy, or just off.
    That’s where designers can really make a difference.
    The deck might seem like just another task, but it’s a chance to work on something strategic early on and help shape how the company is understood. It offers a rare opportunity to collaborate closely with copywriters, strategists and the founders to turn their vision into a clear and convincing story.
    Founders bring the vision, but more and more, design and brand teams are being asked to shape how that vision is told, and sold. So here are five handy things we’ve learned at SIDE ST for the next time you’re asked to design a deck.
    Think in context
    Designers stepping into pitch work should begin by understanding the full picture – who the deck is for, what outcomes it’s meant to drive and how it fits into the broader brand and business context. Their role isn’t just to make things look good, but to prioritise clarity over surface-level aesthetics.
    It’s about getting into the founders’ mindset, shaping visuals and copy around the message, and connecting with the intended audience. Every decision, from slide hierarchy to image selection, should reinforce the business goals behind the deck.
    Support the narrative
    Visuals are more subjective than words, and that’s exactly what gives them power. The right image can suggest an idea, reinforce a value, or subtly shift perception without a single word.
    Whether it’s hinting at accessibility, signalling innovation, or grounding the product in context, design plays a strategic role in how a company is understood. It gives designers the opportunity to take centre stage in the storytelling, shaping how the company is understood through visual choices.
    But that influence works both ways. Used thoughtlessly, visuals can distort the story, suggesting the wrong market, implying a different stage of maturity, or confusing people about the product itself. When used with care, they become a powerful design tool to sharpen the narrative and spark interest from the very first slide.
    Keep it real
    Stock photos can be tempting. They’re high-quality and easy to drop in, especially when the real images a start-up has can be grainy, unfinished, or simply not there yet.
    But in early-stage pitch decks, they often work against your client. Instead of supporting the story, they flatten it, and rarely reflect the actual team, product, or context.
    This is your chance as a designer to lean into what’s real, even if it’s a bit rough. Designers can elevate even scrappy assets with thoughtful framing and treatment, turning rough imagery into a strength. In early-stage storytelling, “real” often resonates more than “perfect.”
    Pay attention to the format
    Even if you’re brought in just to design the deck, don’t treat it as a standalone piece. It’s often the first brand touchpoint investors will see—but it won’t be the last. They’ll go on to check the website, scroll through social posts, and form an impression based on how it all fits together.
    Early-stage startups might not have full brand guidelines in place yet, but that doesn’t mean there’s no need for consistency. In fact, it gives designers a unique opportunity to lay the foundation. A strong, thoughtful deck can help shape the early visual language and give the team something to build on as the brand grows.
    Before you hit export
    For designers, the deck isn’t just another deliverable. It’s an early tool that shapes and impacts investor perception, internal alignment and founder confidence. It’s a strategic design moment to influence the trajectory of a company before it’s fully formed.
    Designers who understand the pressure, pace and uncertainty founders face at this stage are better equipped to deliver work that resonates. This is about more than simply polishing slides, it’s about helping early-stage teams tell a sharper, more human story when it matters most.
    Maor Ofek is founder of SIDE ST, a brand consultancy that works mainly with start-ups. 
    #sharpen #story #design #guide #startups
    Sharpen the story – a design guide to start-up’s pitch decks
    In early-stage start-ups, the pitch deck is often the first thing investors see. Sometimes, it’s the only thing. And yet, it rarely gets the same attention as the website or the socials. Most decks are pulled together last minute, with slides that feel rushed, messy, or just off. That’s where designers can really make a difference. The deck might seem like just another task, but it’s a chance to work on something strategic early on and help shape how the company is understood. It offers a rare opportunity to collaborate closely with copywriters, strategists and the founders to turn their vision into a clear and convincing story. Founders bring the vision, but more and more, design and brand teams are being asked to shape how that vision is told, and sold. So here are five handy things we’ve learned at SIDE ST for the next time you’re asked to design a deck. Think in context Designers stepping into pitch work should begin by understanding the full picture – who the deck is for, what outcomes it’s meant to drive and how it fits into the broader brand and business context. Their role isn’t just to make things look good, but to prioritise clarity over surface-level aesthetics. It’s about getting into the founders’ mindset, shaping visuals and copy around the message, and connecting with the intended audience. Every decision, from slide hierarchy to image selection, should reinforce the business goals behind the deck. Support the narrative Visuals are more subjective than words, and that’s exactly what gives them power. The right image can suggest an idea, reinforce a value, or subtly shift perception without a single word. Whether it’s hinting at accessibility, signalling innovation, or grounding the product in context, design plays a strategic role in how a company is understood. It gives designers the opportunity to take centre stage in the storytelling, shaping how the company is understood through visual choices. But that influence works both ways. Used thoughtlessly, visuals can distort the story, suggesting the wrong market, implying a different stage of maturity, or confusing people about the product itself. When used with care, they become a powerful design tool to sharpen the narrative and spark interest from the very first slide. Keep it real Stock photos can be tempting. They’re high-quality and easy to drop in, especially when the real images a start-up has can be grainy, unfinished, or simply not there yet. But in early-stage pitch decks, they often work against your client. Instead of supporting the story, they flatten it, and rarely reflect the actual team, product, or context. This is your chance as a designer to lean into what’s real, even if it’s a bit rough. Designers can elevate even scrappy assets with thoughtful framing and treatment, turning rough imagery into a strength. In early-stage storytelling, “real” often resonates more than “perfect.” Pay attention to the format Even if you’re brought in just to design the deck, don’t treat it as a standalone piece. It’s often the first brand touchpoint investors will see—but it won’t be the last. They’ll go on to check the website, scroll through social posts, and form an impression based on how it all fits together. Early-stage startups might not have full brand guidelines in place yet, but that doesn’t mean there’s no need for consistency. In fact, it gives designers a unique opportunity to lay the foundation. A strong, thoughtful deck can help shape the early visual language and give the team something to build on as the brand grows. Before you hit export For designers, the deck isn’t just another deliverable. It’s an early tool that shapes and impacts investor perception, internal alignment and founder confidence. It’s a strategic design moment to influence the trajectory of a company before it’s fully formed. Designers who understand the pressure, pace and uncertainty founders face at this stage are better equipped to deliver work that resonates. This is about more than simply polishing slides, it’s about helping early-stage teams tell a sharper, more human story when it matters most. Maor Ofek is founder of SIDE ST, a brand consultancy that works mainly with start-ups.  #sharpen #story #design #guide #startups
    WWW.DESIGNWEEK.CO.UK
    Sharpen the story – a design guide to start-up’s pitch decks
    In early-stage start-ups, the pitch deck is often the first thing investors see. Sometimes, it’s the only thing. And yet, it rarely gets the same attention as the website or the socials. Most decks are pulled together last minute, with slides that feel rushed, messy, or just off. That’s where designers can really make a difference. The deck might seem like just another task, but it’s a chance to work on something strategic early on and help shape how the company is understood. It offers a rare opportunity to collaborate closely with copywriters, strategists and the founders to turn their vision into a clear and convincing story. Founders bring the vision, but more and more, design and brand teams are being asked to shape how that vision is told, and sold. So here are five handy things we’ve learned at SIDE ST for the next time you’re asked to design a deck. Think in context Designers stepping into pitch work should begin by understanding the full picture – who the deck is for, what outcomes it’s meant to drive and how it fits into the broader brand and business context. Their role isn’t just to make things look good, but to prioritise clarity over surface-level aesthetics. It’s about getting into the founders’ mindset, shaping visuals and copy around the message, and connecting with the intended audience. Every decision, from slide hierarchy to image selection, should reinforce the business goals behind the deck. Support the narrative Visuals are more subjective than words, and that’s exactly what gives them power. The right image can suggest an idea, reinforce a value, or subtly shift perception without a single word. Whether it’s hinting at accessibility, signalling innovation, or grounding the product in context, design plays a strategic role in how a company is understood. It gives designers the opportunity to take centre stage in the storytelling, shaping how the company is understood through visual choices. But that influence works both ways. Used thoughtlessly, visuals can distort the story, suggesting the wrong market, implying a different stage of maturity, or confusing people about the product itself. When used with care, they become a powerful design tool to sharpen the narrative and spark interest from the very first slide. Keep it real Stock photos can be tempting. They’re high-quality and easy to drop in, especially when the real images a start-up has can be grainy, unfinished, or simply not there yet. But in early-stage pitch decks, they often work against your client. Instead of supporting the story, they flatten it, and rarely reflect the actual team, product, or context. This is your chance as a designer to lean into what’s real, even if it’s a bit rough. Designers can elevate even scrappy assets with thoughtful framing and treatment, turning rough imagery into a strength. In early-stage storytelling, “real” often resonates more than “perfect.” Pay attention to the format Even if you’re brought in just to design the deck, don’t treat it as a standalone piece. It’s often the first brand touchpoint investors will see—but it won’t be the last. They’ll go on to check the website, scroll through social posts, and form an impression based on how it all fits together. Early-stage startups might not have full brand guidelines in place yet, but that doesn’t mean there’s no need for consistency. In fact, it gives designers a unique opportunity to lay the foundation. A strong, thoughtful deck can help shape the early visual language and give the team something to build on as the brand grows. Before you hit export For designers, the deck isn’t just another deliverable. It’s an early tool that shapes and impacts investor perception, internal alignment and founder confidence. It’s a strategic design moment to influence the trajectory of a company before it’s fully formed. Designers who understand the pressure, pace and uncertainty founders face at this stage are better equipped to deliver work that resonates. This is about more than simply polishing slides, it’s about helping early-stage teams tell a sharper, more human story when it matters most. Maor Ofek is founder of SIDE ST, a brand consultancy that works mainly with start-ups. 
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  • Monitoring and Support Engineer at Keyword Studios

    Monitoring and Support EngineerKeyword StudiosPasig City Metro Manila Philippines2 hours agoApplyWe are seeking an experienced Monitoring and Support Engineer to support the technology initiatives of the IT Infrastructure team at Keywords. The Monitoring and Support Engineer will be responsible for follow-the-sun monitoring of IT infrastructure, prompt reaction on all infrastructure incident, primary resolution of infrastructure incidents and support requests.ResponsibilitiesFull scope of tasks including but not limited to:Ensure that all incidents are handled within SLAs.Initial troubleshooting of Infrastructure incidents.Ensure maximum network & service availability through proactive monitoring.Ensure all the incident and alert tickets contain detailed technical information.Initial troubleshooting of Infrastructure incidents, restoration of services and escalation to level 3 experts if necessary.Participate in Problem management processes.Ensure that all incidents and critical alerts are documented and escalated if necessary.Ensure effective communication to customers about incidents and outages.Identify opportunities for process improvement and efficiency enhancements.Participate in documentation creation to reduce BAU support activities by ensuring that the Service Desks have adequate knowledge articles to close support tickets as level 1.Participate in reporting on monitored data and incidents on company infrastructure.Implement best practices and lessons learned from initiatives and projects to optimize future outcomes.RequirementsBachelor's degree in a relevant technical field or equivalent experience.Understanding of IT Infrastructure technologies, standards and trends.Technical background with 3+ years’ experience in IT operations role delivering IT infrastructure support, monitoring and incident management.Technical knowledge of the Microsoft Stack, Windows networking, Active Directory, ExchangeTechnical knowledge of Network, Storage and Server equipment, virtualization and production setupsExceptional communication and presentation skills, with the ability to articulate technical concepts to non-technical audiences.Strong analytical and problem-solving skills.Strong customer service orientation.BenefitsGreat Place to Work certified for 4 consecutive yearsFlexible work arrangementGlobal exposure
    Create Your Profile — Game companies can contact you with their relevant job openings.
    Apply
    #monitoring #support #engineer #keyword #studios
    Monitoring and Support Engineer at Keyword Studios
    Monitoring and Support EngineerKeyword StudiosPasig City Metro Manila Philippines2 hours agoApplyWe are seeking an experienced Monitoring and Support Engineer to support the technology initiatives of the IT Infrastructure team at Keywords. The Monitoring and Support Engineer will be responsible for follow-the-sun monitoring of IT infrastructure, prompt reaction on all infrastructure incident, primary resolution of infrastructure incidents and support requests.ResponsibilitiesFull scope of tasks including but not limited to:Ensure that all incidents are handled within SLAs.Initial troubleshooting of Infrastructure incidents.Ensure maximum network & service availability through proactive monitoring.Ensure all the incident and alert tickets contain detailed technical information.Initial troubleshooting of Infrastructure incidents, restoration of services and escalation to level 3 experts if necessary.Participate in Problem management processes.Ensure that all incidents and critical alerts are documented and escalated if necessary.Ensure effective communication to customers about incidents and outages.Identify opportunities for process improvement and efficiency enhancements.Participate in documentation creation to reduce BAU support activities by ensuring that the Service Desks have adequate knowledge articles to close support tickets as level 1.Participate in reporting on monitored data and incidents on company infrastructure.Implement best practices and lessons learned from initiatives and projects to optimize future outcomes.RequirementsBachelor's degree in a relevant technical field or equivalent experience.Understanding of IT Infrastructure technologies, standards and trends.Technical background with 3+ years’ experience in IT operations role delivering IT infrastructure support, monitoring and incident management.Technical knowledge of the Microsoft Stack, Windows networking, Active Directory, ExchangeTechnical knowledge of Network, Storage and Server equipment, virtualization and production setupsExceptional communication and presentation skills, with the ability to articulate technical concepts to non-technical audiences.Strong analytical and problem-solving skills.Strong customer service orientation.BenefitsGreat Place to Work certified for 4 consecutive yearsFlexible work arrangementGlobal exposure Create Your Profile — Game companies can contact you with their relevant job openings. Apply #monitoring #support #engineer #keyword #studios
    Monitoring and Support Engineer at Keyword Studios
    Monitoring and Support EngineerKeyword StudiosPasig City Metro Manila Philippines2 hours agoApplyWe are seeking an experienced Monitoring and Support Engineer to support the technology initiatives of the IT Infrastructure team at Keywords. The Monitoring and Support Engineer will be responsible for follow-the-sun monitoring of IT infrastructure, prompt reaction on all infrastructure incident, primary resolution of infrastructure incidents and support requests.ResponsibilitiesFull scope of tasks including but not limited to:Ensure that all incidents are handled within SLAs.Initial troubleshooting of Infrastructure incidents.Ensure maximum network & service availability through proactive monitoring.Ensure all the incident and alert tickets contain detailed technical information.Initial troubleshooting of Infrastructure incidents, restoration of services and escalation to level 3 experts if necessary.Participate in Problem management processes.Ensure that all incidents and critical alerts are documented and escalated if necessary.Ensure effective communication to customers about incidents and outages.Identify opportunities for process improvement and efficiency enhancements.Participate in documentation creation to reduce BAU support activities by ensuring that the Service Desks have adequate knowledge articles to close support tickets as level 1.Participate in reporting on monitored data and incidents on company infrastructure.Implement best practices and lessons learned from initiatives and projects to optimize future outcomes.RequirementsBachelor's degree in a relevant technical field or equivalent experience.Understanding of IT Infrastructure technologies, standards and trends.Technical background with 3+ years’ experience in IT operations role delivering IT infrastructure support, monitoring and incident management.Technical knowledge of the Microsoft Stack, Windows networking, Active Directory, ExchangeTechnical knowledge of Network, Storage and Server equipment, virtualization and production setupsExceptional communication and presentation skills, with the ability to articulate technical concepts to non-technical audiences.Strong analytical and problem-solving skills.Strong customer service orientation.BenefitsGreat Place to Work certified for 4 consecutive yearsFlexible work arrangementGlobal exposure Create Your Profile — Game companies can contact you with their relevant job openings. Apply
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  • The AI execution gap: Why 80% of projects don’t reach production

    Enterprise artificial intelligence investment is unprecedented, with IDC projecting global spending on AI and GenAI to double to billion by 2028. Yet beneath the impressive budget allocations and boardroom enthusiasm lies a troubling reality: most organisations struggle to translate their AI ambitions into operational success.The sobering statistics behind AI’s promiseModelOp’s 2025 AI Governance Benchmark Report, based on input from 100 senior AI and data leaders at Fortune 500 enterprises, reveals a disconnect between aspiration and execution.While more than 80% of enterprises have 51 or more generative AI projects in proposal phases, only 18% have successfully deployed more than 20 models into production.The execution gap represents one of the most significant challenges facing enterprise AI today. Most generative AI projects still require 6 to 18 months to go live – if they reach production at all.The result is delayed returns on investment, frustrated stakeholders, and diminished confidence in AI initiatives in the enterprise.The cause: Structural, not technical barriersThe biggest obstacles preventing AI scalability aren’t technical limitations – they’re structural inefficiencies plaguing enterprise operations. The ModelOp benchmark report identifies several problems that create what experts call a “time-to-market quagmire.”Fragmented systems plague implementation. 58% of organisations cite fragmented systems as the top obstacle to adopting governance platforms. Fragmentation creates silos where different departments use incompatible tools and processes, making it nearly impossible to maintain consistent oversight in AI initiatives.Manual processes dominate despite digital transformation. 55% of enterprises still rely on manual processes – including spreadsheets and email – to manage AI use case intake. The reliance on antiquated methods creates bottlenecks, increases the likelihood of errors, and makes it difficult to scale AI operations.Lack of standardisation hampers progress. Only 23% of organisations implement standardised intake, development, and model management processes. Without these elements, each AI project becomes a unique challenge requiring custom solutions and extensive coordination by multiple teams.Enterprise-level oversight remains rare Just 14% of companies perform AI assurance at the enterprise level, increasing the risk of duplicated efforts and inconsistent oversight. The lack of centralised governance means organisations often discover they’re solving the same problems multiple times in different departments.The governance revolution: From obstacle to acceleratorA change is taking place in how enterprises view AI governance. Rather than seeing it as a compliance burden that slows innovation, forward-thinking organisations recognise governance as an important enabler of scale and speed.Leadership alignment signals strategic shift. The ModelOp benchmark data reveals a change in organisational structure: 46% of companies now assign accountability for AI governance to a Chief Innovation Officer – more than four times the number who place accountability under Legal or Compliance. This strategic repositioning reflects a new understanding that governance isn’t solely about risk management, but can enable innovation.Investment follows strategic priority. A financial commitment to AI governance underscores its importance. According to the report, 36% of enterprises have budgeted at least million annually for AI governance software, while 54% have allocated resources specifically for AI Portfolio Intelligence to track value and ROI.What high-performing organisations do differentlyThe enterprises that successfully bridge the ‘execution gap’ share several characteristics in their approach to AI implementation:Standardised processes from day one. Leading organisations implement standardised intake, development, and model review processes in AI initiatives. Consistency eliminates the need to reinvent workflows for each project and ensures that all stakeholders understand their responsibilities.Centralised documentation and inventory. Rather than allowing AI assets to proliferate in disconnected systems, successful enterprises maintain centralised inventories that provide visibility into every model’s status, performance, and compliance posture.Automated governance checkpoints. High-performing organisations embed automated governance checkpoints throughout the AI lifecycle, helping ensure compliance requirements and risk assessments are addressed systematically rather than as afterthoughts.End-to-end traceability. Leading enterprises maintain complete traceability of their AI models, including data sources, training methods, validation results, and performance metrics.Measurable impact of structured governanceThe benefits of implementing comprehensive AI governance extend beyond compliance. Organisations that adopt lifecycle automation platforms reportedly see dramatic improvements in operational efficiency and business outcomes.A financial services firm profiled in the ModelOp report experienced a halving of time to production and an 80% reduction in issue resolution time after implementing automated governance processes. Such improvements translate directly into faster time-to-value and increased confidence among business stakeholders.Enterprises with robust governance frameworks report the ability to many times more models simultaneously while maintaining oversight and control. This scalability lets organisations pursue AI initiatives in multiple business units without overwhelming their operational capabilities.The path forward: From stuck to scaledThe message from industry leaders that the gap between AI ambition and execution is solvable, but it requires a shift in approach. Rather than treating governance as a necessary evil, enterprises should realise it enables AI innovation at scale.Immediate action items for AI leadersOrganisations looking to escape the ‘time-to-market quagmire’ should prioritise the following:Audit current state: Conduct an assessment of existing AI initiatives, identifying fragmented processes and manual bottlenecksStandardise workflows: Implement consistent processes for AI use case intake, development, and deployment in all business unitsInvest in integration: Deploy platforms to unify disparate tools and systems under a single governance frameworkEstablish enterprise oversight: Create centralised visibility into all AI initiatives with real-time monitoring and reporting abilitiesThe competitive advantage of getting it rightOrganisations that can solve the execution challenge will be able to bring AI solutions to market faster, scale more efficiently, and maintain the trust of stakeholders and regulators.Enterprises that continue with fragmented processes and manual workflows will find themselves disadvantaged compared to their more organised competitors. Operational excellence isn’t about efficiency but survival.The data shows enterprise AI investment will continue to grow. Therefore, the question isn’t whether organisations will invest in AI, but whether they’ll develop the operational abilities necessary to realise return on investment. The opportunity to lead in the AI-driven economy has never been greater for those willing to embrace governance as an enabler not an obstacle.
    #execution #gap #why #projects #dont
    The AI execution gap: Why 80% of projects don’t reach production
    Enterprise artificial intelligence investment is unprecedented, with IDC projecting global spending on AI and GenAI to double to billion by 2028. Yet beneath the impressive budget allocations and boardroom enthusiasm lies a troubling reality: most organisations struggle to translate their AI ambitions into operational success.The sobering statistics behind AI’s promiseModelOp’s 2025 AI Governance Benchmark Report, based on input from 100 senior AI and data leaders at Fortune 500 enterprises, reveals a disconnect between aspiration and execution.While more than 80% of enterprises have 51 or more generative AI projects in proposal phases, only 18% have successfully deployed more than 20 models into production.The execution gap represents one of the most significant challenges facing enterprise AI today. Most generative AI projects still require 6 to 18 months to go live – if they reach production at all.The result is delayed returns on investment, frustrated stakeholders, and diminished confidence in AI initiatives in the enterprise.The cause: Structural, not technical barriersThe biggest obstacles preventing AI scalability aren’t technical limitations – they’re structural inefficiencies plaguing enterprise operations. The ModelOp benchmark report identifies several problems that create what experts call a “time-to-market quagmire.”Fragmented systems plague implementation. 58% of organisations cite fragmented systems as the top obstacle to adopting governance platforms. Fragmentation creates silos where different departments use incompatible tools and processes, making it nearly impossible to maintain consistent oversight in AI initiatives.Manual processes dominate despite digital transformation. 55% of enterprises still rely on manual processes – including spreadsheets and email – to manage AI use case intake. The reliance on antiquated methods creates bottlenecks, increases the likelihood of errors, and makes it difficult to scale AI operations.Lack of standardisation hampers progress. Only 23% of organisations implement standardised intake, development, and model management processes. Without these elements, each AI project becomes a unique challenge requiring custom solutions and extensive coordination by multiple teams.Enterprise-level oversight remains rare Just 14% of companies perform AI assurance at the enterprise level, increasing the risk of duplicated efforts and inconsistent oversight. The lack of centralised governance means organisations often discover they’re solving the same problems multiple times in different departments.The governance revolution: From obstacle to acceleratorA change is taking place in how enterprises view AI governance. Rather than seeing it as a compliance burden that slows innovation, forward-thinking organisations recognise governance as an important enabler of scale and speed.Leadership alignment signals strategic shift. The ModelOp benchmark data reveals a change in organisational structure: 46% of companies now assign accountability for AI governance to a Chief Innovation Officer – more than four times the number who place accountability under Legal or Compliance. This strategic repositioning reflects a new understanding that governance isn’t solely about risk management, but can enable innovation.Investment follows strategic priority. A financial commitment to AI governance underscores its importance. According to the report, 36% of enterprises have budgeted at least million annually for AI governance software, while 54% have allocated resources specifically for AI Portfolio Intelligence to track value and ROI.What high-performing organisations do differentlyThe enterprises that successfully bridge the ‘execution gap’ share several characteristics in their approach to AI implementation:Standardised processes from day one. Leading organisations implement standardised intake, development, and model review processes in AI initiatives. Consistency eliminates the need to reinvent workflows for each project and ensures that all stakeholders understand their responsibilities.Centralised documentation and inventory. Rather than allowing AI assets to proliferate in disconnected systems, successful enterprises maintain centralised inventories that provide visibility into every model’s status, performance, and compliance posture.Automated governance checkpoints. High-performing organisations embed automated governance checkpoints throughout the AI lifecycle, helping ensure compliance requirements and risk assessments are addressed systematically rather than as afterthoughts.End-to-end traceability. Leading enterprises maintain complete traceability of their AI models, including data sources, training methods, validation results, and performance metrics.Measurable impact of structured governanceThe benefits of implementing comprehensive AI governance extend beyond compliance. Organisations that adopt lifecycle automation platforms reportedly see dramatic improvements in operational efficiency and business outcomes.A financial services firm profiled in the ModelOp report experienced a halving of time to production and an 80% reduction in issue resolution time after implementing automated governance processes. Such improvements translate directly into faster time-to-value and increased confidence among business stakeholders.Enterprises with robust governance frameworks report the ability to many times more models simultaneously while maintaining oversight and control. This scalability lets organisations pursue AI initiatives in multiple business units without overwhelming their operational capabilities.The path forward: From stuck to scaledThe message from industry leaders that the gap between AI ambition and execution is solvable, but it requires a shift in approach. Rather than treating governance as a necessary evil, enterprises should realise it enables AI innovation at scale.Immediate action items for AI leadersOrganisations looking to escape the ‘time-to-market quagmire’ should prioritise the following:Audit current state: Conduct an assessment of existing AI initiatives, identifying fragmented processes and manual bottlenecksStandardise workflows: Implement consistent processes for AI use case intake, development, and deployment in all business unitsInvest in integration: Deploy platforms to unify disparate tools and systems under a single governance frameworkEstablish enterprise oversight: Create centralised visibility into all AI initiatives with real-time monitoring and reporting abilitiesThe competitive advantage of getting it rightOrganisations that can solve the execution challenge will be able to bring AI solutions to market faster, scale more efficiently, and maintain the trust of stakeholders and regulators.Enterprises that continue with fragmented processes and manual workflows will find themselves disadvantaged compared to their more organised competitors. Operational excellence isn’t about efficiency but survival.The data shows enterprise AI investment will continue to grow. Therefore, the question isn’t whether organisations will invest in AI, but whether they’ll develop the operational abilities necessary to realise return on investment. The opportunity to lead in the AI-driven economy has never been greater for those willing to embrace governance as an enabler not an obstacle. #execution #gap #why #projects #dont
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    The AI execution gap: Why 80% of projects don’t reach production
    Enterprise artificial intelligence investment is unprecedented, with IDC projecting global spending on AI and GenAI to double to $631 billion by 2028. Yet beneath the impressive budget allocations and boardroom enthusiasm lies a troubling reality: most organisations struggle to translate their AI ambitions into operational success.The sobering statistics behind AI’s promiseModelOp’s 2025 AI Governance Benchmark Report, based on input from 100 senior AI and data leaders at Fortune 500 enterprises, reveals a disconnect between aspiration and execution.While more than 80% of enterprises have 51 or more generative AI projects in proposal phases, only 18% have successfully deployed more than 20 models into production.The execution gap represents one of the most significant challenges facing enterprise AI today. Most generative AI projects still require 6 to 18 months to go live – if they reach production at all.The result is delayed returns on investment, frustrated stakeholders, and diminished confidence in AI initiatives in the enterprise.The cause: Structural, not technical barriersThe biggest obstacles preventing AI scalability aren’t technical limitations – they’re structural inefficiencies plaguing enterprise operations. The ModelOp benchmark report identifies several problems that create what experts call a “time-to-market quagmire.”Fragmented systems plague implementation. 58% of organisations cite fragmented systems as the top obstacle to adopting governance platforms. Fragmentation creates silos where different departments use incompatible tools and processes, making it nearly impossible to maintain consistent oversight in AI initiatives.Manual processes dominate despite digital transformation. 55% of enterprises still rely on manual processes – including spreadsheets and email – to manage AI use case intake. The reliance on antiquated methods creates bottlenecks, increases the likelihood of errors, and makes it difficult to scale AI operations.Lack of standardisation hampers progress. Only 23% of organisations implement standardised intake, development, and model management processes. Without these elements, each AI project becomes a unique challenge requiring custom solutions and extensive coordination by multiple teams.Enterprise-level oversight remains rare Just 14% of companies perform AI assurance at the enterprise level, increasing the risk of duplicated efforts and inconsistent oversight. The lack of centralised governance means organisations often discover they’re solving the same problems multiple times in different departments.The governance revolution: From obstacle to acceleratorA change is taking place in how enterprises view AI governance. Rather than seeing it as a compliance burden that slows innovation, forward-thinking organisations recognise governance as an important enabler of scale and speed.Leadership alignment signals strategic shift. The ModelOp benchmark data reveals a change in organisational structure: 46% of companies now assign accountability for AI governance to a Chief Innovation Officer – more than four times the number who place accountability under Legal or Compliance. This strategic repositioning reflects a new understanding that governance isn’t solely about risk management, but can enable innovation.Investment follows strategic priority. A financial commitment to AI governance underscores its importance. According to the report, 36% of enterprises have budgeted at least $1 million annually for AI governance software, while 54% have allocated resources specifically for AI Portfolio Intelligence to track value and ROI.What high-performing organisations do differentlyThe enterprises that successfully bridge the ‘execution gap’ share several characteristics in their approach to AI implementation:Standardised processes from day one. Leading organisations implement standardised intake, development, and model review processes in AI initiatives. Consistency eliminates the need to reinvent workflows for each project and ensures that all stakeholders understand their responsibilities.Centralised documentation and inventory. Rather than allowing AI assets to proliferate in disconnected systems, successful enterprises maintain centralised inventories that provide visibility into every model’s status, performance, and compliance posture.Automated governance checkpoints. High-performing organisations embed automated governance checkpoints throughout the AI lifecycle, helping ensure compliance requirements and risk assessments are addressed systematically rather than as afterthoughts.End-to-end traceability. Leading enterprises maintain complete traceability of their AI models, including data sources, training methods, validation results, and performance metrics.Measurable impact of structured governanceThe benefits of implementing comprehensive AI governance extend beyond compliance. Organisations that adopt lifecycle automation platforms reportedly see dramatic improvements in operational efficiency and business outcomes.A financial services firm profiled in the ModelOp report experienced a halving of time to production and an 80% reduction in issue resolution time after implementing automated governance processes. Such improvements translate directly into faster time-to-value and increased confidence among business stakeholders.Enterprises with robust governance frameworks report the ability to many times more models simultaneously while maintaining oversight and control. This scalability lets organisations pursue AI initiatives in multiple business units without overwhelming their operational capabilities.The path forward: From stuck to scaledThe message from industry leaders that the gap between AI ambition and execution is solvable, but it requires a shift in approach. Rather than treating governance as a necessary evil, enterprises should realise it enables AI innovation at scale.Immediate action items for AI leadersOrganisations looking to escape the ‘time-to-market quagmire’ should prioritise the following:Audit current state: Conduct an assessment of existing AI initiatives, identifying fragmented processes and manual bottlenecksStandardise workflows: Implement consistent processes for AI use case intake, development, and deployment in all business unitsInvest in integration: Deploy platforms to unify disparate tools and systems under a single governance frameworkEstablish enterprise oversight: Create centralised visibility into all AI initiatives with real-time monitoring and reporting abilitiesThe competitive advantage of getting it rightOrganisations that can solve the execution challenge will be able to bring AI solutions to market faster, scale more efficiently, and maintain the trust of stakeholders and regulators.Enterprises that continue with fragmented processes and manual workflows will find themselves disadvantaged compared to their more organised competitors. Operational excellence isn’t about efficiency but survival.The data shows enterprise AI investment will continue to grow. Therefore, the question isn’t whether organisations will invest in AI, but whether they’ll develop the operational abilities necessary to realise return on investment. The opportunity to lead in the AI-driven economy has never been greater for those willing to embrace governance as an enabler not an obstacle.(Image source: Unsplash)
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  • Ansys: R&D Engineer II (Remote - East Coast, US)

    Requisition #: 16890 Our Mission: Powering Innovation That Drives Human Advancement When visionary companies need to know how their world-changing ideas will perform, they close the gap between design and reality with Ansys simulation. For more than 50 years, Ansys software has enabled innovators across industries to push boundaries by using the predictive power of simulation. From sustainable transportation to advanced semiconductors, from satellite systems to life-saving medical devices, the next great leaps in human advancement will be powered by Ansys. Innovate With Ansys, Power Your Career. Summary / Role Purpose The R&D Engineer II contributes to the development of software products and supporting systems. In this role, the R&D Engineer II will collaborate with a team of expert professionals to understand customer requirements and accomplish development objectives. Key Duties and Responsibilities Performs moderately complex development activities, including the design, implementation, maintenance, testing and documentation of software modules and sub-systems Understands and employs best practices Performs moderately complex bug verification, release testing and beta support for assigned products. Researches problems discovered by QA or product support and develops solutions Understands the marketing requirements for a product, including target environment, performance criteria and competitive issues Works under the general supervision of a development manager Minimum Education/Certification Requirements and Experience BS in Computer Science, Applied Mathematics, Engineering, or other natural science disciplines with 3-5 years' experience or MS with minimum 2 years experience Working experience within technical software development proven by academic, research, or industry projects. Good understanding and skills in object-oriented programming Experience with Java and C# / .NET Role can be remote, must be based on the East Coast due to timezone Preferred Qualifications and Skills Experience with C++, Python, in addition to Java and C# / .NET Knowledge of Task-Based Asynchronous design patternExposure to model-based systems engineering concepts Working knowledge of SysML Know-how on cloud computing technologies like micro-service architectures, RPC frameworks, REST APIs, etc. Knowledge of software security best practices Experience working on an Agile software development team Technical knowledge and experience with various engineering tools and methodologies, such as Finite Element simulation, CAD modeling, and Systems Architecture modelling is a plus Ability to assist more junior developers on an as-needed basis Ability to learn quickly and to collaborate with others in a geographically distributed team Excellent communication and interpersonal skills At Ansys, we know that changing the world takes vision, skill, and each other. We fuel new ideas, build relationships, and help each other realize our greatest potential. We are ONE Ansys. We operate on three key components: our commitments to stakeholders, our values that guide how we work together, and our actions to deliver results. As ONE Ansys, we are powering innovation that drives human advancement Our Commitments:Amaze with innovative products and solutionsMake our customers incredibly successfulAct with integrityEnsure employees thrive and shareholders prosper Our Values:Adaptability: Be open, welcome what's nextCourage: Be courageous, move forward passionatelyGenerosity: Be generous, share, listen, serveAuthenticity: Be you, make us stronger Our Actions:We commit to audacious goalsWe work seamlessly as a teamWe demonstrate masteryWe deliver outstanding resultsVALUES IN ACTION Ansys is committed to powering the people who power human advancement. We believe in creating and nurturing a workplace that supports and welcomes people of all backgrounds; encouraging them to bring their talents and experience to a workplace where they are valued and can thrive. Our culture is grounded in our four core values of adaptability, courage, generosity, and authenticity. Through our behaviors and actions, these values foster higher team performance and greater innovation for our customers. We're proud to offer programs, available to all employees, to further impact innovation and business outcomes, such as employee networks and learning communities that inform solutions for our globally minded customer base. WELCOME WHAT'S NEXT IN YOUR CAREER AT ANSYS At Ansys, you will find yourself among the sharpest minds and most visionary leaders across the globe. Collectively, we strive to change the world with innovative technology and transformational solutions. With a prestigious reputation in working with well-known, world-class companies, standards at Ansys are high - met by those willing to rise to the occasion and meet those challenges head on. Our team is passionate about pushing the limits of world-class simulation technology, empowering our customers to turn their design concepts into successful, innovative products faster and at a lower cost. Ready to feel inspired? Check out some of our recent customer stories, here and here . At Ansys, it's about the learning, the discovery, and the collaboration. It's about the "what's next" as much as the "mission accomplished." And it's about the melding of disciplined intellect with strategic direction and results that have, can, and do impact real people in real ways. All this is forged within a working environment built on respect, autonomy, and ethics.CREATING A PLACE WE'RE PROUD TO BEAnsys is an S&P 500 company and a member of the NASDAQ-100. We are proud to have been recognized for the following more recent awards, although our list goes on: Newsweek's Most Loved Workplace globally and in the U.S., Gold Stevie Award Winner, America's Most Responsible Companies, Fast Company World Changing Ideas, Great Place to Work Certified.For more information, please visit us at Ansys is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, and other protected characteristics.Ansys does not accept unsolicited referrals for vacancies, and any unsolicited referral will become the property of Ansys. Upon hire, no fee will be owed to the agency, person, or entity.Apply NowLet's start your dream job Apply now Meet JobCopilot: Your Personal AI Job HunterAutomatically Apply to Remote Full-Stack Programming JobsJust set your preferences and Job Copilot will do the rest-finding, filtering, and applying while you focus on what matters. Activate JobCopilot
    #ansys #rampampd #engineer #remote #east
    Ansys: R&D Engineer II (Remote - East Coast, US)
    Requisition #: 16890 Our Mission: Powering Innovation That Drives Human Advancement When visionary companies need to know how their world-changing ideas will perform, they close the gap between design and reality with Ansys simulation. For more than 50 years, Ansys software has enabled innovators across industries to push boundaries by using the predictive power of simulation. From sustainable transportation to advanced semiconductors, from satellite systems to life-saving medical devices, the next great leaps in human advancement will be powered by Ansys. Innovate With Ansys, Power Your Career. Summary / Role Purpose The R&D Engineer II contributes to the development of software products and supporting systems. In this role, the R&D Engineer II will collaborate with a team of expert professionals to understand customer requirements and accomplish development objectives. Key Duties and Responsibilities Performs moderately complex development activities, including the design, implementation, maintenance, testing and documentation of software modules and sub-systems Understands and employs best practices Performs moderately complex bug verification, release testing and beta support for assigned products. Researches problems discovered by QA or product support and develops solutions Understands the marketing requirements for a product, including target environment, performance criteria and competitive issues Works under the general supervision of a development manager Minimum Education/Certification Requirements and Experience BS in Computer Science, Applied Mathematics, Engineering, or other natural science disciplines with 3-5 years' experience or MS with minimum 2 years experience Working experience within technical software development proven by academic, research, or industry projects. Good understanding and skills in object-oriented programming Experience with Java and C# / .NET Role can be remote, must be based on the East Coast due to timezone Preferred Qualifications and Skills Experience with C++, Python, in addition to Java and C# / .NET Knowledge of Task-Based Asynchronous design patternExposure to model-based systems engineering concepts Working knowledge of SysML Know-how on cloud computing technologies like micro-service architectures, RPC frameworks, REST APIs, etc. Knowledge of software security best practices Experience working on an Agile software development team Technical knowledge and experience with various engineering tools and methodologies, such as Finite Element simulation, CAD modeling, and Systems Architecture modelling is a plus Ability to assist more junior developers on an as-needed basis Ability to learn quickly and to collaborate with others in a geographically distributed team Excellent communication and interpersonal skills At Ansys, we know that changing the world takes vision, skill, and each other. We fuel new ideas, build relationships, and help each other realize our greatest potential. We are ONE Ansys. We operate on three key components: our commitments to stakeholders, our values that guide how we work together, and our actions to deliver results. As ONE Ansys, we are powering innovation that drives human advancement Our Commitments:Amaze with innovative products and solutionsMake our customers incredibly successfulAct with integrityEnsure employees thrive and shareholders prosper Our Values:Adaptability: Be open, welcome what's nextCourage: Be courageous, move forward passionatelyGenerosity: Be generous, share, listen, serveAuthenticity: Be you, make us stronger Our Actions:We commit to audacious goalsWe work seamlessly as a teamWe demonstrate masteryWe deliver outstanding resultsVALUES IN ACTION Ansys is committed to powering the people who power human advancement. We believe in creating and nurturing a workplace that supports and welcomes people of all backgrounds; encouraging them to bring their talents and experience to a workplace where they are valued and can thrive. Our culture is grounded in our four core values of adaptability, courage, generosity, and authenticity. Through our behaviors and actions, these values foster higher team performance and greater innovation for our customers. We're proud to offer programs, available to all employees, to further impact innovation and business outcomes, such as employee networks and learning communities that inform solutions for our globally minded customer base. WELCOME WHAT'S NEXT IN YOUR CAREER AT ANSYS At Ansys, you will find yourself among the sharpest minds and most visionary leaders across the globe. Collectively, we strive to change the world with innovative technology and transformational solutions. With a prestigious reputation in working with well-known, world-class companies, standards at Ansys are high - met by those willing to rise to the occasion and meet those challenges head on. Our team is passionate about pushing the limits of world-class simulation technology, empowering our customers to turn their design concepts into successful, innovative products faster and at a lower cost. Ready to feel inspired? Check out some of our recent customer stories, here and here . At Ansys, it's about the learning, the discovery, and the collaboration. It's about the "what's next" as much as the "mission accomplished." And it's about the melding of disciplined intellect with strategic direction and results that have, can, and do impact real people in real ways. All this is forged within a working environment built on respect, autonomy, and ethics.CREATING A PLACE WE'RE PROUD TO BEAnsys is an S&P 500 company and a member of the NASDAQ-100. We are proud to have been recognized for the following more recent awards, although our list goes on: Newsweek's Most Loved Workplace globally and in the U.S., Gold Stevie Award Winner, America's Most Responsible Companies, Fast Company World Changing Ideas, Great Place to Work Certified.For more information, please visit us at Ansys is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, and other protected characteristics.Ansys does not accept unsolicited referrals for vacancies, and any unsolicited referral will become the property of Ansys. Upon hire, no fee will be owed to the agency, person, or entity.Apply NowLet's start your dream job Apply now Meet JobCopilot: Your Personal AI Job HunterAutomatically Apply to Remote Full-Stack Programming JobsJust set your preferences and Job Copilot will do the rest-finding, filtering, and applying while you focus on what matters. Activate JobCopilot #ansys #rampampd #engineer #remote #east
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    Ansys: R&D Engineer II (Remote - East Coast, US)
    Requisition #: 16890 Our Mission: Powering Innovation That Drives Human Advancement When visionary companies need to know how their world-changing ideas will perform, they close the gap between design and reality with Ansys simulation. For more than 50 years, Ansys software has enabled innovators across industries to push boundaries by using the predictive power of simulation. From sustainable transportation to advanced semiconductors, from satellite systems to life-saving medical devices, the next great leaps in human advancement will be powered by Ansys. Innovate With Ansys, Power Your Career. Summary / Role Purpose The R&D Engineer II contributes to the development of software products and supporting systems. In this role, the R&D Engineer II will collaborate with a team of expert professionals to understand customer requirements and accomplish development objectives. Key Duties and Responsibilities Performs moderately complex development activities, including the design, implementation, maintenance, testing and documentation of software modules and sub-systems Understands and employs best practices Performs moderately complex bug verification, release testing and beta support for assigned products. Researches problems discovered by QA or product support and develops solutions Understands the marketing requirements for a product, including target environment, performance criteria and competitive issues Works under the general supervision of a development manager Minimum Education/Certification Requirements and Experience BS in Computer Science, Applied Mathematics, Engineering, or other natural science disciplines with 3-5 years' experience or MS with minimum 2 years experience Working experience within technical software development proven by academic, research, or industry projects. Good understanding and skills in object-oriented programming Experience with Java and C# / .NET Role can be remote, must be based on the East Coast due to timezone Preferred Qualifications and Skills Experience with C++, Python, in addition to Java and C# / .NET Knowledge of Task-Based Asynchronous design pattern (TAP) Exposure to model-based systems engineering concepts Working knowledge of SysML Know-how on cloud computing technologies like micro-service architectures, RPC frameworks (e.g., gRPC), REST APIs, etc. Knowledge of software security best practices Experience working on an Agile software development team Technical knowledge and experience with various engineering tools and methodologies, such as Finite Element simulation, CAD modeling, and Systems Architecture modelling is a plus Ability to assist more junior developers on an as-needed basis Ability to learn quickly and to collaborate with others in a geographically distributed team Excellent communication and interpersonal skills At Ansys, we know that changing the world takes vision, skill, and each other. We fuel new ideas, build relationships, and help each other realize our greatest potential. We are ONE Ansys. We operate on three key components: our commitments to stakeholders, our values that guide how we work together, and our actions to deliver results. As ONE Ansys, we are powering innovation that drives human advancement Our Commitments:Amaze with innovative products and solutionsMake our customers incredibly successfulAct with integrityEnsure employees thrive and shareholders prosper Our Values:Adaptability: Be open, welcome what's nextCourage: Be courageous, move forward passionatelyGenerosity: Be generous, share, listen, serveAuthenticity: Be you, make us stronger Our Actions:We commit to audacious goalsWe work seamlessly as a teamWe demonstrate masteryWe deliver outstanding resultsVALUES IN ACTION Ansys is committed to powering the people who power human advancement. We believe in creating and nurturing a workplace that supports and welcomes people of all backgrounds; encouraging them to bring their talents and experience to a workplace where they are valued and can thrive. Our culture is grounded in our four core values of adaptability, courage, generosity, and authenticity. Through our behaviors and actions, these values foster higher team performance and greater innovation for our customers. We're proud to offer programs, available to all employees, to further impact innovation and business outcomes, such as employee networks and learning communities that inform solutions for our globally minded customer base. WELCOME WHAT'S NEXT IN YOUR CAREER AT ANSYS At Ansys, you will find yourself among the sharpest minds and most visionary leaders across the globe. Collectively, we strive to change the world with innovative technology and transformational solutions. With a prestigious reputation in working with well-known, world-class companies, standards at Ansys are high - met by those willing to rise to the occasion and meet those challenges head on. Our team is passionate about pushing the limits of world-class simulation technology, empowering our customers to turn their design concepts into successful, innovative products faster and at a lower cost. Ready to feel inspired? Check out some of our recent customer stories, here and here . At Ansys, it's about the learning, the discovery, and the collaboration. It's about the "what's next" as much as the "mission accomplished." And it's about the melding of disciplined intellect with strategic direction and results that have, can, and do impact real people in real ways. All this is forged within a working environment built on respect, autonomy, and ethics.CREATING A PLACE WE'RE PROUD TO BEAnsys is an S&P 500 company and a member of the NASDAQ-100. We are proud to have been recognized for the following more recent awards, although our list goes on: Newsweek's Most Loved Workplace globally and in the U.S., Gold Stevie Award Winner, America's Most Responsible Companies, Fast Company World Changing Ideas, Great Place to Work Certified (China, Greece, France, India, Japan, Korea, Spain, Sweden, Taiwan, and U.K.).For more information, please visit us at Ansys is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, and other protected characteristics.Ansys does not accept unsolicited referrals for vacancies, and any unsolicited referral will become the property of Ansys. Upon hire, no fee will be owed to the agency, person, or entity.Apply NowLet's start your dream job Apply now Meet JobCopilot: Your Personal AI Job HunterAutomatically Apply to Remote Full-Stack Programming JobsJust set your preferences and Job Copilot will do the rest-finding, filtering, and applying while you focus on what matters. Activate JobCopilot
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  • Why Designers Get Stuck In The Details And How To Stop

    You’ve drawn fifty versions of the same screen — and you still hate every one of them. Begrudgingly, you pick three, show them to your product manager, and hear: “Looks cool, but the idea doesn’t work.” Sound familiar?
    In this article, I’ll unpack why designers fall into detail work at the wrong moment, examining both process pitfalls and the underlying psychological reasons, as understanding these traps is the first step to overcoming them. I’ll also share tactics I use to climb out of that trap.
    Reason #1 You’re Afraid To Show Rough Work
    We designers worship detail. We’re taught that true craft equals razor‑sharp typography, perfect grids, and pixel precision. So the minute a task arrives, we pop open Figma and start polishing long before polish is needed.
    I’ve skipped the sketch phase more times than I care to admit. I told myself it would be faster, yet I always ended up spending hours producing a tidy mock‑up when a scribbled thumbnail would have sparked a five‑minute chat with my product manager. Rough sketches felt “unprofessional,” so I hid them.
    The cost? Lost time, wasted energy — and, by the third redo, teammates were quietly wondering if I even understood the brief.
    The real problem here is the habit: we open Figma and start perfecting the UI before we’ve even solved the problem.
    So why do we hide these rough sketches? It’s not just a bad habit or plain silly. There are solid psychological reasons behind it. We often just call it perfectionism, but it’s deeper than wanting things neat. Digging into the psychologyshows there are a couple of flavors driving this:

    Socially prescribed perfectionismIt’s that nagging feeling that everyone else expects perfect work from you, which makes showing anything rough feel like walking into the lion’s den.
    Self-oriented perfectionismWhere you’re the one setting impossibly high standards for yourself, leading to brutal self-criticism if anything looks slightly off.

    Either way, the result’s the same: showing unfinished work feels wrong, and you miss out on that vital early feedback.
    Back to the design side, remember that clients rarely see architects’ first pencil sketches, but these sketches still exist; they guide structural choices before the 3D render. Treat your thumbnails the same way — artifacts meant to collapse uncertainty, not portfolio pieces. Once stakeholders see the upside, roughness becomes a badge of speed, not sloppiness. So, the key is to consciously make that shift:
    Treat early sketches as disposable tools for thinking and actively share them to get feedback faster.

    Reason #2: You Fix The Symptom, Not The Cause
    Before tackling any task, we need to understand what business outcome we’re aiming for. Product managers might come to us asking to enlarge the payment button in the shopping cart because users aren’t noticing it. The suggested solution itself isn’t necessarily bad, but before redesigning the button, we should ask, “What data suggests they aren’t noticing it?” Don’t get me wrong, I’m not saying you shouldn’t trust your product manager. On the contrary, these questions help ensure you’re on the same page and working with the same data.
    From my experience, here are several reasons why users might not be clicking that coveted button:

    Users don’t understand that this step is for payment.
    They understand it’s about payment but expect order confirmation first.
    Due to incorrect translation, users don’t understand what the button means.
    Lack of trust signals.
    Unexpected additional coststhat appear at this stage.
    Technical issues.

    Now, imagine you simply did what the manager suggested. Would you have solved the problem? Hardly.
    Moreover, the responsibility for the unresolved issue would fall on you, as the interface solution lies within the design domain. The product manager actually did their job correctly by identifying a problem: suspiciously, few users are clicking the button.
    Psychologically, taking on this bigger role isn’t easy. It means overcoming the fear of making mistakes and the discomfort of exploring unclear problems rather than just doing tasks. This shift means seeing ourselves as partners who create value — even if it means fighting a hesitation to question product managers— and understanding that using our product logic expertise proactively is crucial for modern designers.
    There’s another critical reason why we, designers, need to be a bit like product managers: the rise of AI. I deliberately used a simple example about enlarging a button, but I’m confident that in the near future, AI will easily handle routine design tasks. This worries me, but at the same time, I’m already gladly stepping into the product manager’s territory: understanding product and business metrics, formulating hypotheses, conducting research, and so on. It might sound like I’m taking work away from PMs, but believe me, they undoubtedly have enough on their plates and are usually more than happy to delegate some responsibilities to designers.
    Reason #3: You’re Solving The Wrong Problem
    Before solving anything, ask whether the problem even deserves your attention.
    During a major home‑screen redesign, our goal was to drive more users into paid services. The initial hypothesis — making service buttons bigger and brighter might help returning users — seemed reasonable enough to test. However, even when A/B testsshowed minimal impact, we continued to tweak those buttons.
    Only later did it click: the home screen isn’t the place to sell; visitors open the app to start, not to buy. We removed that promo block, and nothing broke. Contextual entry points deeper into the journey performed brilliantly. Lesson learned:
    Without the right context, any visual tweak is lipstick on a pig.

    Why did we get stuck polishing buttons instead of stopping sooner? It’s easy to get tunnel vision. Psychologically, it’s likely the good old sunk cost fallacy kicking in: we’d already invested time in the buttons, so stopping felt like wasting that effort, even though the data wasn’t promising.
    It’s just easier to keep fiddling with something familiar than to admit we need a new plan. Perhaps the simple question I should have asked myself when results stalled was: “Are we optimizing the right thing or just polishing something that fundamentally doesn’t fit the user’s primary goal here?” That alone might have saved hours.
    Reason #4: You’re Drowning In Unactionable Feedback
    We all discuss our work with colleagues. But here’s a crucial point: what kind of question do you pose to kick off that discussion? If your go-to is “What do you think?” well, that question might lead you down a rabbit hole of personal opinions rather than actionable insights. While experienced colleagues will cut through the noise, others, unsure what to evaluate, might comment on anything and everything — fonts, button colors, even when you desperately need to discuss a user flow.
    What matters here are two things:

    The question you ask,
    The context you give.

    That means clearly stating the problem, what you’ve learned, and how your idea aims to fix it.
    For instance:
    “The problem is our payment conversion rate has dropped by X%. I’ve interviewed users and found they abandon payment because they don’t understand how the total amount is calculated. My solution is to show a detailed cost breakdown. Do you think this actually solves the problem for them?”

    Here, you’ve stated the problem, shared your insight, explained your solution, and asked a direct question. It’s even better if you prepare a list of specific sub-questions. For instance: “Are all items in the cost breakdown clear?” or “Does the placement of this breakdown feel intuitive within the payment flow?”
    Another good habit is to keep your rough sketches and previous iterations handy. Some of your colleagues’ suggestions might be things you’ve already tried. It’s great if you can discuss them immediately to either revisit those ideas or definitively set them aside.
    I’m not a psychologist, but experience tells me that, psychologically, the reluctance to be this specific often stems from a fear of our solution being rejected. We tend to internalize feedback: a seemingly innocent comment like, “Have you considered other ways to organize this section?” or “Perhaps explore a different structure for this part?” can instantly morph in our minds into “You completely messed up the structure. You’re a bad designer.” Imposter syndrome, in all its glory.
    So, to wrap up this point, here are two recommendations:

    Prepare for every design discussion.A couple of focused questions will yield far more valuable input than a vague “So, what do you think?”.
    Actively work on separating feedback on your design from your self-worth.If a mistake is pointed out, acknowledge it, learn from it, and you’ll be less likely to repeat it. This is often easier said than done. For me, it took years of working with a psychotherapist. If you struggle with this, I sincerely wish you strength in overcoming it.

    Reason #5 You’re Just Tired
    Sometimes, the issue isn’t strategic at all — it’s fatigue. Fussing over icon corners can feel like a cozy bunker when your brain is fried. There’s a name for this: decision fatigue. Basically, your brain’s battery for hard thinking is low, so it hides out in the easy, comfy zone of pixel-pushing.
    A striking example comes from a New York Times article titled “Do You Suffer From Decision Fatigue?.” It described how judges deciding on release requests were far more likely to grant release early in the daycompared to late in the daysimply because their decision-making energy was depleted. Luckily, designers rarely hold someone’s freedom in their hands, but the example dramatically shows how fatigue can impact our judgment and productivity.
    What helps here:

    Swap tasks.Trade tickets with another designer; novelty resets your focus.
    Talk to another designer.If NDA permits, ask peers outside the team for a sanity check.
    Step away.Even a ten‑minute walk can do more than a double‑shot espresso.

    By the way, I came up with these ideas while walking around my office. I was lucky to work near a river, and those short walks quickly turned into a helpful habit.

    And one more trick that helps me snap out of detail mode early: if I catch myself making around 20 little tweaks — changing font weight, color, border radius — I just stop. Over time, it turned into a habit. I have a similar one with Instagram: by the third reel, my brain quietly asks, “Wait, weren’t we working?” Funny how that kind of nudge saves a ton of time.
    Four Steps I Use to Avoid Drowning In Detail
    Knowing these potential traps, here’s the practical process I use to stay on track:
    1. Define the Core Problem & Business Goal
    Before anything, dig deep: what’s the actual problem we’re solving, not just the requested task or a surface-level symptom? Ask ‘why’ repeatedly. What user pain or business need are we addressing? Then, state the clear business goal: “What metric am I moving, and do we have data to prove this is the right lever?” If retention is the goal, decide whether push reminders, gamification, or personalised content is the best route. The wrong lever, or tackling a symptom instead of the cause, dooms everything downstream.
    2. Choose the MechanicOnce the core problem and goal are clear, lock the solution principle or ‘mechanic’ first. Going with a game layer? Decide if it’s leaderboards, streaks, or badges. Write it down. Then move on. No UI yet. This keeps the focus high-level before diving into pixels.
    3. Wireframe the Flow & Get Focused Feedback
    Now open Figma. Map screens, layout, and transitions. Boxes and arrows are enough. Keep the fidelity low so the discussion stays on the flow, not colour. Crucially, when you share these early wires, ask specific questions and provide clear contextto get actionable feedback, not just vague opinions.
    4. Polish the VisualsI only let myself tweak grids, type scales, and shadows after the flow is validated. If progress stalls, or before a major polish effort, I surface the work in a design critique — again using targeted questions and clear context — instead of hiding in version 47. This ensures detailing serves the now-validated solution.
    Even for something as small as a single button, running these four checkpoints takes about ten minutes and saves hours of decorative dithering.
    Wrapping Up
    Next time you feel the pull to vanish into mock‑ups before the problem is nailed down, pause and ask what you might be avoiding. Yes, that can expose an uncomfortable truth. But pausing to ask what you might be avoiding — maybe the fuzzy core problem, or just asking for tough feedback — gives you the power to face the real issue head-on. It keeps the project focused on solving the right problem, not just perfecting a flawed solution.
    Attention to detail is a superpower when used at the right moment. Obsessing over pixels too soon, though, is a bad habit and a warning light telling us the process needs a rethink.
    #why #designers #get #stuck #details
    Why Designers Get Stuck In The Details And How To Stop
    You’ve drawn fifty versions of the same screen — and you still hate every one of them. Begrudgingly, you pick three, show them to your product manager, and hear: “Looks cool, but the idea doesn’t work.” Sound familiar? In this article, I’ll unpack why designers fall into detail work at the wrong moment, examining both process pitfalls and the underlying psychological reasons, as understanding these traps is the first step to overcoming them. I’ll also share tactics I use to climb out of that trap. Reason #1 You’re Afraid To Show Rough Work We designers worship detail. We’re taught that true craft equals razor‑sharp typography, perfect grids, and pixel precision. So the minute a task arrives, we pop open Figma and start polishing long before polish is needed. I’ve skipped the sketch phase more times than I care to admit. I told myself it would be faster, yet I always ended up spending hours producing a tidy mock‑up when a scribbled thumbnail would have sparked a five‑minute chat with my product manager. Rough sketches felt “unprofessional,” so I hid them. The cost? Lost time, wasted energy — and, by the third redo, teammates were quietly wondering if I even understood the brief. The real problem here is the habit: we open Figma and start perfecting the UI before we’ve even solved the problem. So why do we hide these rough sketches? It’s not just a bad habit or plain silly. There are solid psychological reasons behind it. We often just call it perfectionism, but it’s deeper than wanting things neat. Digging into the psychologyshows there are a couple of flavors driving this: Socially prescribed perfectionismIt’s that nagging feeling that everyone else expects perfect work from you, which makes showing anything rough feel like walking into the lion’s den. Self-oriented perfectionismWhere you’re the one setting impossibly high standards for yourself, leading to brutal self-criticism if anything looks slightly off. Either way, the result’s the same: showing unfinished work feels wrong, and you miss out on that vital early feedback. Back to the design side, remember that clients rarely see architects’ first pencil sketches, but these sketches still exist; they guide structural choices before the 3D render. Treat your thumbnails the same way — artifacts meant to collapse uncertainty, not portfolio pieces. Once stakeholders see the upside, roughness becomes a badge of speed, not sloppiness. So, the key is to consciously make that shift: Treat early sketches as disposable tools for thinking and actively share them to get feedback faster. Reason #2: You Fix The Symptom, Not The Cause Before tackling any task, we need to understand what business outcome we’re aiming for. Product managers might come to us asking to enlarge the payment button in the shopping cart because users aren’t noticing it. The suggested solution itself isn’t necessarily bad, but before redesigning the button, we should ask, “What data suggests they aren’t noticing it?” Don’t get me wrong, I’m not saying you shouldn’t trust your product manager. On the contrary, these questions help ensure you’re on the same page and working with the same data. From my experience, here are several reasons why users might not be clicking that coveted button: Users don’t understand that this step is for payment. They understand it’s about payment but expect order confirmation first. Due to incorrect translation, users don’t understand what the button means. Lack of trust signals. Unexpected additional coststhat appear at this stage. Technical issues. Now, imagine you simply did what the manager suggested. Would you have solved the problem? Hardly. Moreover, the responsibility for the unresolved issue would fall on you, as the interface solution lies within the design domain. The product manager actually did their job correctly by identifying a problem: suspiciously, few users are clicking the button. Psychologically, taking on this bigger role isn’t easy. It means overcoming the fear of making mistakes and the discomfort of exploring unclear problems rather than just doing tasks. This shift means seeing ourselves as partners who create value — even if it means fighting a hesitation to question product managers— and understanding that using our product logic expertise proactively is crucial for modern designers. There’s another critical reason why we, designers, need to be a bit like product managers: the rise of AI. I deliberately used a simple example about enlarging a button, but I’m confident that in the near future, AI will easily handle routine design tasks. This worries me, but at the same time, I’m already gladly stepping into the product manager’s territory: understanding product and business metrics, formulating hypotheses, conducting research, and so on. It might sound like I’m taking work away from PMs, but believe me, they undoubtedly have enough on their plates and are usually more than happy to delegate some responsibilities to designers. Reason #3: You’re Solving The Wrong Problem Before solving anything, ask whether the problem even deserves your attention. During a major home‑screen redesign, our goal was to drive more users into paid services. The initial hypothesis — making service buttons bigger and brighter might help returning users — seemed reasonable enough to test. However, even when A/B testsshowed minimal impact, we continued to tweak those buttons. Only later did it click: the home screen isn’t the place to sell; visitors open the app to start, not to buy. We removed that promo block, and nothing broke. Contextual entry points deeper into the journey performed brilliantly. Lesson learned: Without the right context, any visual tweak is lipstick on a pig. Why did we get stuck polishing buttons instead of stopping sooner? It’s easy to get tunnel vision. Psychologically, it’s likely the good old sunk cost fallacy kicking in: we’d already invested time in the buttons, so stopping felt like wasting that effort, even though the data wasn’t promising. It’s just easier to keep fiddling with something familiar than to admit we need a new plan. Perhaps the simple question I should have asked myself when results stalled was: “Are we optimizing the right thing or just polishing something that fundamentally doesn’t fit the user’s primary goal here?” That alone might have saved hours. Reason #4: You’re Drowning In Unactionable Feedback We all discuss our work with colleagues. But here’s a crucial point: what kind of question do you pose to kick off that discussion? If your go-to is “What do you think?” well, that question might lead you down a rabbit hole of personal opinions rather than actionable insights. While experienced colleagues will cut through the noise, others, unsure what to evaluate, might comment on anything and everything — fonts, button colors, even when you desperately need to discuss a user flow. What matters here are two things: The question you ask, The context you give. That means clearly stating the problem, what you’ve learned, and how your idea aims to fix it. For instance: “The problem is our payment conversion rate has dropped by X%. I’ve interviewed users and found they abandon payment because they don’t understand how the total amount is calculated. My solution is to show a detailed cost breakdown. Do you think this actually solves the problem for them?” Here, you’ve stated the problem, shared your insight, explained your solution, and asked a direct question. It’s even better if you prepare a list of specific sub-questions. For instance: “Are all items in the cost breakdown clear?” or “Does the placement of this breakdown feel intuitive within the payment flow?” Another good habit is to keep your rough sketches and previous iterations handy. Some of your colleagues’ suggestions might be things you’ve already tried. It’s great if you can discuss them immediately to either revisit those ideas or definitively set them aside. I’m not a psychologist, but experience tells me that, psychologically, the reluctance to be this specific often stems from a fear of our solution being rejected. We tend to internalize feedback: a seemingly innocent comment like, “Have you considered other ways to organize this section?” or “Perhaps explore a different structure for this part?” can instantly morph in our minds into “You completely messed up the structure. You’re a bad designer.” Imposter syndrome, in all its glory. So, to wrap up this point, here are two recommendations: Prepare for every design discussion.A couple of focused questions will yield far more valuable input than a vague “So, what do you think?”. Actively work on separating feedback on your design from your self-worth.If a mistake is pointed out, acknowledge it, learn from it, and you’ll be less likely to repeat it. This is often easier said than done. For me, it took years of working with a psychotherapist. If you struggle with this, I sincerely wish you strength in overcoming it. Reason #5 You’re Just Tired Sometimes, the issue isn’t strategic at all — it’s fatigue. Fussing over icon corners can feel like a cozy bunker when your brain is fried. There’s a name for this: decision fatigue. Basically, your brain’s battery for hard thinking is low, so it hides out in the easy, comfy zone of pixel-pushing. A striking example comes from a New York Times article titled “Do You Suffer From Decision Fatigue?.” It described how judges deciding on release requests were far more likely to grant release early in the daycompared to late in the daysimply because their decision-making energy was depleted. Luckily, designers rarely hold someone’s freedom in their hands, but the example dramatically shows how fatigue can impact our judgment and productivity. What helps here: Swap tasks.Trade tickets with another designer; novelty resets your focus. Talk to another designer.If NDA permits, ask peers outside the team for a sanity check. Step away.Even a ten‑minute walk can do more than a double‑shot espresso. By the way, I came up with these ideas while walking around my office. I was lucky to work near a river, and those short walks quickly turned into a helpful habit. And one more trick that helps me snap out of detail mode early: if I catch myself making around 20 little tweaks — changing font weight, color, border radius — I just stop. Over time, it turned into a habit. I have a similar one with Instagram: by the third reel, my brain quietly asks, “Wait, weren’t we working?” Funny how that kind of nudge saves a ton of time. Four Steps I Use to Avoid Drowning In Detail Knowing these potential traps, here’s the practical process I use to stay on track: 1. Define the Core Problem & Business Goal Before anything, dig deep: what’s the actual problem we’re solving, not just the requested task or a surface-level symptom? Ask ‘why’ repeatedly. What user pain or business need are we addressing? Then, state the clear business goal: “What metric am I moving, and do we have data to prove this is the right lever?” If retention is the goal, decide whether push reminders, gamification, or personalised content is the best route. The wrong lever, or tackling a symptom instead of the cause, dooms everything downstream. 2. Choose the MechanicOnce the core problem and goal are clear, lock the solution principle or ‘mechanic’ first. Going with a game layer? Decide if it’s leaderboards, streaks, or badges. Write it down. Then move on. No UI yet. This keeps the focus high-level before diving into pixels. 3. Wireframe the Flow & Get Focused Feedback Now open Figma. Map screens, layout, and transitions. Boxes and arrows are enough. Keep the fidelity low so the discussion stays on the flow, not colour. Crucially, when you share these early wires, ask specific questions and provide clear contextto get actionable feedback, not just vague opinions. 4. Polish the VisualsI only let myself tweak grids, type scales, and shadows after the flow is validated. If progress stalls, or before a major polish effort, I surface the work in a design critique — again using targeted questions and clear context — instead of hiding in version 47. This ensures detailing serves the now-validated solution. Even for something as small as a single button, running these four checkpoints takes about ten minutes and saves hours of decorative dithering. Wrapping Up Next time you feel the pull to vanish into mock‑ups before the problem is nailed down, pause and ask what you might be avoiding. Yes, that can expose an uncomfortable truth. But pausing to ask what you might be avoiding — maybe the fuzzy core problem, or just asking for tough feedback — gives you the power to face the real issue head-on. It keeps the project focused on solving the right problem, not just perfecting a flawed solution. Attention to detail is a superpower when used at the right moment. Obsessing over pixels too soon, though, is a bad habit and a warning light telling us the process needs a rethink. #why #designers #get #stuck #details
    SMASHINGMAGAZINE.COM
    Why Designers Get Stuck In The Details And How To Stop
    You’ve drawn fifty versions of the same screen — and you still hate every one of them. Begrudgingly, you pick three, show them to your product manager, and hear: “Looks cool, but the idea doesn’t work.” Sound familiar? In this article, I’ll unpack why designers fall into detail work at the wrong moment, examining both process pitfalls and the underlying psychological reasons, as understanding these traps is the first step to overcoming them. I’ll also share tactics I use to climb out of that trap. Reason #1 You’re Afraid To Show Rough Work We designers worship detail. We’re taught that true craft equals razor‑sharp typography, perfect grids, and pixel precision. So the minute a task arrives, we pop open Figma and start polishing long before polish is needed. I’ve skipped the sketch phase more times than I care to admit. I told myself it would be faster, yet I always ended up spending hours producing a tidy mock‑up when a scribbled thumbnail would have sparked a five‑minute chat with my product manager. Rough sketches felt “unprofessional,” so I hid them. The cost? Lost time, wasted energy — and, by the third redo, teammates were quietly wondering if I even understood the brief. The real problem here is the habit: we open Figma and start perfecting the UI before we’ve even solved the problem. So why do we hide these rough sketches? It’s not just a bad habit or plain silly. There are solid psychological reasons behind it. We often just call it perfectionism, but it’s deeper than wanting things neat. Digging into the psychology (like the research by Hewitt and Flett) shows there are a couple of flavors driving this: Socially prescribed perfectionismIt’s that nagging feeling that everyone else expects perfect work from you, which makes showing anything rough feel like walking into the lion’s den. Self-oriented perfectionismWhere you’re the one setting impossibly high standards for yourself, leading to brutal self-criticism if anything looks slightly off. Either way, the result’s the same: showing unfinished work feels wrong, and you miss out on that vital early feedback. Back to the design side, remember that clients rarely see architects’ first pencil sketches, but these sketches still exist; they guide structural choices before the 3D render. Treat your thumbnails the same way — artifacts meant to collapse uncertainty, not portfolio pieces. Once stakeholders see the upside, roughness becomes a badge of speed, not sloppiness. So, the key is to consciously make that shift: Treat early sketches as disposable tools for thinking and actively share them to get feedback faster. Reason #2: You Fix The Symptom, Not The Cause Before tackling any task, we need to understand what business outcome we’re aiming for. Product managers might come to us asking to enlarge the payment button in the shopping cart because users aren’t noticing it. The suggested solution itself isn’t necessarily bad, but before redesigning the button, we should ask, “What data suggests they aren’t noticing it?” Don’t get me wrong, I’m not saying you shouldn’t trust your product manager. On the contrary, these questions help ensure you’re on the same page and working with the same data. From my experience, here are several reasons why users might not be clicking that coveted button: Users don’t understand that this step is for payment. They understand it’s about payment but expect order confirmation first. Due to incorrect translation, users don’t understand what the button means. Lack of trust signals (no security icons, unclear seller information). Unexpected additional costs (hidden fees, shipping) that appear at this stage. Technical issues (inactive button, page freezing). Now, imagine you simply did what the manager suggested. Would you have solved the problem? Hardly. Moreover, the responsibility for the unresolved issue would fall on you, as the interface solution lies within the design domain. The product manager actually did their job correctly by identifying a problem: suspiciously, few users are clicking the button. Psychologically, taking on this bigger role isn’t easy. It means overcoming the fear of making mistakes and the discomfort of exploring unclear problems rather than just doing tasks. This shift means seeing ourselves as partners who create value — even if it means fighting a hesitation to question product managers (which might come from a fear of speaking up or a desire to avoid challenging authority) — and understanding that using our product logic expertise proactively is crucial for modern designers. There’s another critical reason why we, designers, need to be a bit like product managers: the rise of AI. I deliberately used a simple example about enlarging a button, but I’m confident that in the near future, AI will easily handle routine design tasks. This worries me, but at the same time, I’m already gladly stepping into the product manager’s territory: understanding product and business metrics, formulating hypotheses, conducting research, and so on. It might sound like I’m taking work away from PMs, but believe me, they undoubtedly have enough on their plates and are usually more than happy to delegate some responsibilities to designers. Reason #3: You’re Solving The Wrong Problem Before solving anything, ask whether the problem even deserves your attention. During a major home‑screen redesign, our goal was to drive more users into paid services. The initial hypothesis — making service buttons bigger and brighter might help returning users — seemed reasonable enough to test. However, even when A/B tests (a method of comparing two versions of a design to determine which performs better) showed minimal impact, we continued to tweak those buttons. Only later did it click: the home screen isn’t the place to sell; visitors open the app to start, not to buy. We removed that promo block, and nothing broke. Contextual entry points deeper into the journey performed brilliantly. Lesson learned: Without the right context, any visual tweak is lipstick on a pig. Why did we get stuck polishing buttons instead of stopping sooner? It’s easy to get tunnel vision. Psychologically, it’s likely the good old sunk cost fallacy kicking in: we’d already invested time in the buttons, so stopping felt like wasting that effort, even though the data wasn’t promising. It’s just easier to keep fiddling with something familiar than to admit we need a new plan. Perhaps the simple question I should have asked myself when results stalled was: “Are we optimizing the right thing or just polishing something that fundamentally doesn’t fit the user’s primary goal here?” That alone might have saved hours. Reason #4: You’re Drowning In Unactionable Feedback We all discuss our work with colleagues. But here’s a crucial point: what kind of question do you pose to kick off that discussion? If your go-to is “What do you think?” well, that question might lead you down a rabbit hole of personal opinions rather than actionable insights. While experienced colleagues will cut through the noise, others, unsure what to evaluate, might comment on anything and everything — fonts, button colors, even when you desperately need to discuss a user flow. What matters here are two things: The question you ask, The context you give. That means clearly stating the problem, what you’ve learned, and how your idea aims to fix it. For instance: “The problem is our payment conversion rate has dropped by X%. I’ve interviewed users and found they abandon payment because they don’t understand how the total amount is calculated. My solution is to show a detailed cost breakdown. Do you think this actually solves the problem for them?” Here, you’ve stated the problem (conversion drop), shared your insight (user confusion), explained your solution (cost breakdown), and asked a direct question. It’s even better if you prepare a list of specific sub-questions. For instance: “Are all items in the cost breakdown clear?” or “Does the placement of this breakdown feel intuitive within the payment flow?” Another good habit is to keep your rough sketches and previous iterations handy. Some of your colleagues’ suggestions might be things you’ve already tried. It’s great if you can discuss them immediately to either revisit those ideas or definitively set them aside. I’m not a psychologist, but experience tells me that, psychologically, the reluctance to be this specific often stems from a fear of our solution being rejected. We tend to internalize feedback: a seemingly innocent comment like, “Have you considered other ways to organize this section?” or “Perhaps explore a different structure for this part?” can instantly morph in our minds into “You completely messed up the structure. You’re a bad designer.” Imposter syndrome, in all its glory. So, to wrap up this point, here are two recommendations: Prepare for every design discussion.A couple of focused questions will yield far more valuable input than a vague “So, what do you think?”. Actively work on separating feedback on your design from your self-worth.If a mistake is pointed out, acknowledge it, learn from it, and you’ll be less likely to repeat it. This is often easier said than done. For me, it took years of working with a psychotherapist. If you struggle with this, I sincerely wish you strength in overcoming it. Reason #5 You’re Just Tired Sometimes, the issue isn’t strategic at all — it’s fatigue. Fussing over icon corners can feel like a cozy bunker when your brain is fried. There’s a name for this: decision fatigue. Basically, your brain’s battery for hard thinking is low, so it hides out in the easy, comfy zone of pixel-pushing. A striking example comes from a New York Times article titled “Do You Suffer From Decision Fatigue?.” It described how judges deciding on release requests were far more likely to grant release early in the day (about 70% of cases) compared to late in the day (less than 10%) simply because their decision-making energy was depleted. Luckily, designers rarely hold someone’s freedom in their hands, but the example dramatically shows how fatigue can impact our judgment and productivity. What helps here: Swap tasks.Trade tickets with another designer; novelty resets your focus. Talk to another designer.If NDA permits, ask peers outside the team for a sanity check. Step away.Even a ten‑minute walk can do more than a double‑shot espresso. By the way, I came up with these ideas while walking around my office. I was lucky to work near a river, and those short walks quickly turned into a helpful habit. And one more trick that helps me snap out of detail mode early: if I catch myself making around 20 little tweaks — changing font weight, color, border radius — I just stop. Over time, it turned into a habit. I have a similar one with Instagram: by the third reel, my brain quietly asks, “Wait, weren’t we working?” Funny how that kind of nudge saves a ton of time. Four Steps I Use to Avoid Drowning In Detail Knowing these potential traps, here’s the practical process I use to stay on track: 1. Define the Core Problem & Business Goal Before anything, dig deep: what’s the actual problem we’re solving, not just the requested task or a surface-level symptom? Ask ‘why’ repeatedly. What user pain or business need are we addressing? Then, state the clear business goal: “What metric am I moving, and do we have data to prove this is the right lever?” If retention is the goal, decide whether push reminders, gamification, or personalised content is the best route. The wrong lever, or tackling a symptom instead of the cause, dooms everything downstream. 2. Choose the Mechanic (Solution Principle) Once the core problem and goal are clear, lock the solution principle or ‘mechanic’ first. Going with a game layer? Decide if it’s leaderboards, streaks, or badges. Write it down. Then move on. No UI yet. This keeps the focus high-level before diving into pixels. 3. Wireframe the Flow & Get Focused Feedback Now open Figma. Map screens, layout, and transitions. Boxes and arrows are enough. Keep the fidelity low so the discussion stays on the flow, not colour. Crucially, when you share these early wires, ask specific questions and provide clear context (as discussed in ‘Reason #4’) to get actionable feedback, not just vague opinions. 4. Polish the Visuals (Mindfully) I only let myself tweak grids, type scales, and shadows after the flow is validated. If progress stalls, or before a major polish effort, I surface the work in a design critique — again using targeted questions and clear context — instead of hiding in version 47. This ensures detailing serves the now-validated solution. Even for something as small as a single button, running these four checkpoints takes about ten minutes and saves hours of decorative dithering. Wrapping Up Next time you feel the pull to vanish into mock‑ups before the problem is nailed down, pause and ask what you might be avoiding. Yes, that can expose an uncomfortable truth. But pausing to ask what you might be avoiding — maybe the fuzzy core problem, or just asking for tough feedback — gives you the power to face the real issue head-on. It keeps the project focused on solving the right problem, not just perfecting a flawed solution. Attention to detail is a superpower when used at the right moment. Obsessing over pixels too soon, though, is a bad habit and a warning light telling us the process needs a rethink.
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  • Alec Haase Q&A: Customer Engagement Book Interview

    Reading Time: 6 minutes
    What is marketing without data? Assumptions. Guesses. Fluff.
    For Chapter 6 of our book, “The Customer Engagement Book: Adapt or Die,” we spoke with Alec Haase, Product GTM Lead, Commerce and AI at Hightouch, to explore how engagement data can truly inform critical business decisions. 
    Alec discusses the different types of customer behaviors that matter most, how to separate meaningful information from the rest, and the role of systems that learn over time to create tailored customer experiences.
    This interview provides insights into using data for real-time actions and shaping the future of marketing. Prepare to learn about AI decision-making and how a focus on data is changing how we engage with customers.

     
    Alec Haase Q&A Interview
    1. What types of customer engagement data are most valuable for making strategic business decisions?
    It’s a culmination of everything.
    Behavioral signals — the actual conversions and micro-conversions that users take within your product or website.
    Obviously, that’s things like purchases. But there are also other behavioral signals marketers should be using and thinking about. Things like micro-conversions — maybe that’s shopping for a product, clicking to learn more about a product, or visiting a certain page on your website.
    Behind that, you also need to have all your user data to tie that to.

    So I know someone took said action; I can follow up with them in email or out on paid social. I need the user identifiers to do that.

    2. How do you distinguish between data that is actionable versus data that is just noise?
    Data that’s actionable includes the conversions and micro-conversions — very clear instances of “someone did this.” I can react to or measure those.
    What’s becoming a bit of a challenge for marketers is understanding that there’s other data that is valuable for machine learning or reinforcement learning models, things like tags on the types of products customers are interacting with.
    Maybe there’s category information about that product, or color information. That would otherwise look like noise to the average marketer. But behind the scenes, it can be used for reinforcement learning.

    There is definitely the “clear-cut” actionable data, but marketers shouldn’t be quick to classify things as noise because the rise in machine learning and reinforcement learning will make that data more valuable.

    3. How can customer engagement data be used to identify and prioritize new business opportunities?
    At Hightouch, we don’t necessarily think about retroactive analysis. We have a system where we have customer engagement data firing in that we then have real-time scores reacting to.
    An interesting example is when you have machine learning and reinforcement learning models running. In the pet retailer example I gave you, the system is able to figure out what to prioritize.
    The concept of reinforcement learning is not a marketer making rules to say, “I know this type of thing works well on this type of audience.”

    It’s the machine itself using the data to determine what attribute responds well to which offer, recommendation, or marketing campaign.

    4. How can marketers ensure their use of customer engagement data aligns with the broader business objectives?
    It starts with the objectives. It’s starting with the desired outcome and working your way back. That whole flip of the paradigm is starting with outcomes and letting the system optimize. What are you trying to drive, and then back into the types of experiences that can make that happen?
    There’s personalization.
    When we talk about data-driven experiences and personalization, Spotify Wrapped is the North Star. For Spotify Wrapped, you want to drive customer stickiness and create a brand. To make that happen, you want to send a personalized email. What components do you want in that email?

    Maybe it’s top five songs, top five artists, and then you can back into the actual event data you need to make that happen.

    5. What role does engagement data play in influencing cross-functional decisions such as those in product development, sales, or customer service?
    For product development, it’s product analytics — knowing what features users are using, or seeing in heat maps where users are clicking.
    Sales is similar. We’re using behavioral signals like what types of content they’re reading on the site to help inform what they would be interested in — the types of products or the types of use cases.

    For customer service, you can look at errors they’ve run into in the past or specific purchases they’ve made, so that when you’re helping them the next time they engage with you, you know exactly what their past behaviors were and what products they could be calling about.

    6. What are some challenges marketers face when trying to translate customer engagement data into actionable insights?
    Access to data is one challenge. You might not know what data you have because marketers historically may not have been used to the systems where data is stored.
    Historically, that’s been pretty siloed away from them. Rich behavioral data and other data across the business was stored somewhere else.
    Now, as more companies embrace the data warehouse at the center of their business, it gives everyone a true single place where data can be stored.

    Marketers are working more with data teams, understanding more about the data they have, and using that data to power downstream use cases, personalization, reinforcement learning, or general business insights.

    7. How do you address skepticism or resistance from stakeholders when presenting data-driven recommendations?
    As a marketer, I think proof is key. The best thing is if you’ve actually run a test. “I think we should do this. I ran a small test, and it’s showing that this is actually proving out.” Being able to clearly explain and justify your reasoning with data is super important.

    8. What technology or tools have you found most effective for gathering and analyzing customer engagement data?
    Any type of behavioral event collection, specifically ones that write to the cloud data warehouse, is the critical component. Your data team is operating off the data warehouse.
    Having an event collection product that stores data in that central spot is really important if you want to use the other data when making recommendations.
    You want to get everything into the data warehouse where it can be used both for insights and for putting into action.

    For Spotify Wrapped, you want to collect behavioral event signals like songs listened to or concerts attended, writing to the warehouse so that you can get insights back — how many songs were played this year, projections for next month — but then you can also use those behavioral events in downstream platforms to fire off personalized emails with product recommendations or Spotify Wrapped-style experiences.

    9. How do you see the role of customer engagement data evolving in shaping business strategies over the next five years?

    What we’re excited about is the concept of AI Decisioning — having AI agents actually using customer data to train their own models and decision-making to create personalized experiences.
    We’re sitting on top of all this behavioral data, engagement data, and user attributes, and our system is learning from all of that to make the best decisions across downstream systems.
    Whether that’s as simple as driving a loyalty program and figuring out what emails to send or what on-site experiences to show, or exposing insights that might lead you to completely change your business strategy, we see engagement data as the fuel to the engine of reinforcement learning, machine learning, AI agents, this whole next wave of Martech that’s just now coming.
    But it all starts with having the data to train those systems.

    I think that behavioral data is the fuel of modern Martech, and that only holds more true as Martech platforms adopt these decisioning and AI capabilities, because they’re only as good as the data that’s training the models.

     

     
    This interview Q&A was hosted with Alec Haase, Product GTM Lead, Commerce and AI at Hightouch, for Chapter 6 of The Customer Engagement Book: Adapt or Die.
    Download the PDF or request a physical copy of the book here.
    The post Alec Haase Q&A: Customer Engagement Book Interview appeared first on MoEngage.
    #alec #haase #qampampa #customer #engagement
    Alec Haase Q&A: Customer Engagement Book Interview
    Reading Time: 6 minutes What is marketing without data? Assumptions. Guesses. Fluff. For Chapter 6 of our book, “The Customer Engagement Book: Adapt or Die,” we spoke with Alec Haase, Product GTM Lead, Commerce and AI at Hightouch, to explore how engagement data can truly inform critical business decisions.  Alec discusses the different types of customer behaviors that matter most, how to separate meaningful information from the rest, and the role of systems that learn over time to create tailored customer experiences. This interview provides insights into using data for real-time actions and shaping the future of marketing. Prepare to learn about AI decision-making and how a focus on data is changing how we engage with customers.   Alec Haase Q&A Interview 1. What types of customer engagement data are most valuable for making strategic business decisions? It’s a culmination of everything. Behavioral signals — the actual conversions and micro-conversions that users take within your product or website. Obviously, that’s things like purchases. But there are also other behavioral signals marketers should be using and thinking about. Things like micro-conversions — maybe that’s shopping for a product, clicking to learn more about a product, or visiting a certain page on your website. Behind that, you also need to have all your user data to tie that to. So I know someone took said action; I can follow up with them in email or out on paid social. I need the user identifiers to do that. 2. How do you distinguish between data that is actionable versus data that is just noise? Data that’s actionable includes the conversions and micro-conversions — very clear instances of “someone did this.” I can react to or measure those. What’s becoming a bit of a challenge for marketers is understanding that there’s other data that is valuable for machine learning or reinforcement learning models, things like tags on the types of products customers are interacting with. Maybe there’s category information about that product, or color information. That would otherwise look like noise to the average marketer. But behind the scenes, it can be used for reinforcement learning. There is definitely the “clear-cut” actionable data, but marketers shouldn’t be quick to classify things as noise because the rise in machine learning and reinforcement learning will make that data more valuable. 3. How can customer engagement data be used to identify and prioritize new business opportunities? At Hightouch, we don’t necessarily think about retroactive analysis. We have a system where we have customer engagement data firing in that we then have real-time scores reacting to. An interesting example is when you have machine learning and reinforcement learning models running. In the pet retailer example I gave you, the system is able to figure out what to prioritize. The concept of reinforcement learning is not a marketer making rules to say, “I know this type of thing works well on this type of audience.” It’s the machine itself using the data to determine what attribute responds well to which offer, recommendation, or marketing campaign. 4. How can marketers ensure their use of customer engagement data aligns with the broader business objectives? It starts with the objectives. It’s starting with the desired outcome and working your way back. That whole flip of the paradigm is starting with outcomes and letting the system optimize. What are you trying to drive, and then back into the types of experiences that can make that happen? There’s personalization. When we talk about data-driven experiences and personalization, Spotify Wrapped is the North Star. For Spotify Wrapped, you want to drive customer stickiness and create a brand. To make that happen, you want to send a personalized email. What components do you want in that email? Maybe it’s top five songs, top five artists, and then you can back into the actual event data you need to make that happen. 5. What role does engagement data play in influencing cross-functional decisions such as those in product development, sales, or customer service? For product development, it’s product analytics — knowing what features users are using, or seeing in heat maps where users are clicking. Sales is similar. We’re using behavioral signals like what types of content they’re reading on the site to help inform what they would be interested in — the types of products or the types of use cases. For customer service, you can look at errors they’ve run into in the past or specific purchases they’ve made, so that when you’re helping them the next time they engage with you, you know exactly what their past behaviors were and what products they could be calling about. 6. What are some challenges marketers face when trying to translate customer engagement data into actionable insights? Access to data is one challenge. You might not know what data you have because marketers historically may not have been used to the systems where data is stored. Historically, that’s been pretty siloed away from them. Rich behavioral data and other data across the business was stored somewhere else. Now, as more companies embrace the data warehouse at the center of their business, it gives everyone a true single place where data can be stored. Marketers are working more with data teams, understanding more about the data they have, and using that data to power downstream use cases, personalization, reinforcement learning, or general business insights. 7. How do you address skepticism or resistance from stakeholders when presenting data-driven recommendations? As a marketer, I think proof is key. The best thing is if you’ve actually run a test. “I think we should do this. I ran a small test, and it’s showing that this is actually proving out.” Being able to clearly explain and justify your reasoning with data is super important. 8. What technology or tools have you found most effective for gathering and analyzing customer engagement data? Any type of behavioral event collection, specifically ones that write to the cloud data warehouse, is the critical component. Your data team is operating off the data warehouse. Having an event collection product that stores data in that central spot is really important if you want to use the other data when making recommendations. You want to get everything into the data warehouse where it can be used both for insights and for putting into action. For Spotify Wrapped, you want to collect behavioral event signals like songs listened to or concerts attended, writing to the warehouse so that you can get insights back — how many songs were played this year, projections for next month — but then you can also use those behavioral events in downstream platforms to fire off personalized emails with product recommendations or Spotify Wrapped-style experiences. 9. How do you see the role of customer engagement data evolving in shaping business strategies over the next five years? What we’re excited about is the concept of AI Decisioning — having AI agents actually using customer data to train their own models and decision-making to create personalized experiences. We’re sitting on top of all this behavioral data, engagement data, and user attributes, and our system is learning from all of that to make the best decisions across downstream systems. Whether that’s as simple as driving a loyalty program and figuring out what emails to send or what on-site experiences to show, or exposing insights that might lead you to completely change your business strategy, we see engagement data as the fuel to the engine of reinforcement learning, machine learning, AI agents, this whole next wave of Martech that’s just now coming. But it all starts with having the data to train those systems. I think that behavioral data is the fuel of modern Martech, and that only holds more true as Martech platforms adopt these decisioning and AI capabilities, because they’re only as good as the data that’s training the models.     This interview Q&A was hosted with Alec Haase, Product GTM Lead, Commerce and AI at Hightouch, for Chapter 6 of The Customer Engagement Book: Adapt or Die. Download the PDF or request a physical copy of the book here. The post Alec Haase Q&A: Customer Engagement Book Interview appeared first on MoEngage. #alec #haase #qampampa #customer #engagement
    WWW.MOENGAGE.COM
    Alec Haase Q&A: Customer Engagement Book Interview
    Reading Time: 6 minutes What is marketing without data? Assumptions. Guesses. Fluff. For Chapter 6 of our book, “The Customer Engagement Book: Adapt or Die,” we spoke with Alec Haase, Product GTM Lead, Commerce and AI at Hightouch, to explore how engagement data can truly inform critical business decisions.  Alec discusses the different types of customer behaviors that matter most, how to separate meaningful information from the rest, and the role of systems that learn over time to create tailored customer experiences. This interview provides insights into using data for real-time actions and shaping the future of marketing. Prepare to learn about AI decision-making and how a focus on data is changing how we engage with customers.   Alec Haase Q&A Interview 1. What types of customer engagement data are most valuable for making strategic business decisions? It’s a culmination of everything. Behavioral signals — the actual conversions and micro-conversions that users take within your product or website. Obviously, that’s things like purchases. But there are also other behavioral signals marketers should be using and thinking about. Things like micro-conversions — maybe that’s shopping for a product, clicking to learn more about a product, or visiting a certain page on your website. Behind that, you also need to have all your user data to tie that to. So I know someone took said action; I can follow up with them in email or out on paid social. I need the user identifiers to do that. 2. How do you distinguish between data that is actionable versus data that is just noise? Data that’s actionable includes the conversions and micro-conversions — very clear instances of “someone did this.” I can react to or measure those. What’s becoming a bit of a challenge for marketers is understanding that there’s other data that is valuable for machine learning or reinforcement learning models, things like tags on the types of products customers are interacting with. Maybe there’s category information about that product, or color information. That would otherwise look like noise to the average marketer. But behind the scenes, it can be used for reinforcement learning. There is definitely the “clear-cut” actionable data, but marketers shouldn’t be quick to classify things as noise because the rise in machine learning and reinforcement learning will make that data more valuable. 3. How can customer engagement data be used to identify and prioritize new business opportunities? At Hightouch, we don’t necessarily think about retroactive analysis. We have a system where we have customer engagement data firing in that we then have real-time scores reacting to. An interesting example is when you have machine learning and reinforcement learning models running. In the pet retailer example I gave you, the system is able to figure out what to prioritize. The concept of reinforcement learning is not a marketer making rules to say, “I know this type of thing works well on this type of audience.” It’s the machine itself using the data to determine what attribute responds well to which offer, recommendation, or marketing campaign. 4. How can marketers ensure their use of customer engagement data aligns with the broader business objectives? It starts with the objectives. It’s starting with the desired outcome and working your way back. That whole flip of the paradigm is starting with outcomes and letting the system optimize. What are you trying to drive, and then back into the types of experiences that can make that happen? There’s personalization. When we talk about data-driven experiences and personalization, Spotify Wrapped is the North Star. For Spotify Wrapped, you want to drive customer stickiness and create a brand. To make that happen, you want to send a personalized email. What components do you want in that email? Maybe it’s top five songs, top five artists, and then you can back into the actual event data you need to make that happen. 5. What role does engagement data play in influencing cross-functional decisions such as those in product development, sales, or customer service? For product development, it’s product analytics — knowing what features users are using, or seeing in heat maps where users are clicking. Sales is similar. We’re using behavioral signals like what types of content they’re reading on the site to help inform what they would be interested in — the types of products or the types of use cases. For customer service, you can look at errors they’ve run into in the past or specific purchases they’ve made, so that when you’re helping them the next time they engage with you, you know exactly what their past behaviors were and what products they could be calling about. 6. What are some challenges marketers face when trying to translate customer engagement data into actionable insights? Access to data is one challenge. You might not know what data you have because marketers historically may not have been used to the systems where data is stored. Historically, that’s been pretty siloed away from them. Rich behavioral data and other data across the business was stored somewhere else. Now, as more companies embrace the data warehouse at the center of their business, it gives everyone a true single place where data can be stored. Marketers are working more with data teams, understanding more about the data they have, and using that data to power downstream use cases, personalization, reinforcement learning, or general business insights. 7. How do you address skepticism or resistance from stakeholders when presenting data-driven recommendations? As a marketer, I think proof is key. The best thing is if you’ve actually run a test. “I think we should do this. I ran a small test, and it’s showing that this is actually proving out.” Being able to clearly explain and justify your reasoning with data is super important. 8. What technology or tools have you found most effective for gathering and analyzing customer engagement data? Any type of behavioral event collection, specifically ones that write to the cloud data warehouse, is the critical component. Your data team is operating off the data warehouse. Having an event collection product that stores data in that central spot is really important if you want to use the other data when making recommendations. You want to get everything into the data warehouse where it can be used both for insights and for putting into action. For Spotify Wrapped, you want to collect behavioral event signals like songs listened to or concerts attended, writing to the warehouse so that you can get insights back — how many songs were played this year, projections for next month — but then you can also use those behavioral events in downstream platforms to fire off personalized emails with product recommendations or Spotify Wrapped-style experiences. 9. How do you see the role of customer engagement data evolving in shaping business strategies over the next five years? What we’re excited about is the concept of AI Decisioning — having AI agents actually using customer data to train their own models and decision-making to create personalized experiences. We’re sitting on top of all this behavioral data, engagement data, and user attributes, and our system is learning from all of that to make the best decisions across downstream systems. Whether that’s as simple as driving a loyalty program and figuring out what emails to send or what on-site experiences to show, or exposing insights that might lead you to completely change your business strategy, we see engagement data as the fuel to the engine of reinforcement learning, machine learning, AI agents, this whole next wave of Martech that’s just now coming. But it all starts with having the data to train those systems. I think that behavioral data is the fuel of modern Martech, and that only holds more true as Martech platforms adopt these decisioning and AI capabilities, because they’re only as good as the data that’s training the models.     This interview Q&A was hosted with Alec Haase, Product GTM Lead, Commerce and AI at Hightouch, for Chapter 6 of The Customer Engagement Book: Adapt or Die. Download the PDF or request a physical copy of the book here. The post Alec Haase Q&A: Customer Engagement Book Interview appeared first on MoEngage.
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  • MedTech AI, hardware, and clinical application programmes

    Modern healthcare innovations span AI, devices, software, images, and regulatory frameworks, all requiring stringent coordination. Generative AI arguably has the strongest transformative potential in healthcare technology programmes, with it already being applied across various domains, such as R&D, commercial operations, and supply chain management.Traditional models for medical appointments, like face-to-face appointments, and paper-based processes may not be sufficient to meet the fast-paced, data-driven medical landscape of today. Therefore, healthcare professionals and patients are seeking more convenient and efficient ways to access and share information, meeting the complex standards of modern medical science. According to McKinsey, Medtech companies are at the forefront of healthcare innovation, estimating they could capture between billion and billion annually in productivity gains. Through GenAI adoption, an additional billion plus in revenue is estimated from products and service innovations. A McKinsey 2024 survey revealed around two thirds of Medtech executives have already implemented Gen AI, with approximately 20% scaling their solutions up and reporting substantial benefits to productivity.  While advanced technology implementation is growing across the medical industry, challenges persist. Organisations face hurdles like data integration issues, decentralised strategies, and skill gaps. Together, these highlight a need for a more streamlined approach to Gen AI deployment. Of all the Medtech domains, R&D is leading the way in Gen AI adoption. Being the most comfortable with new technologies, R&D departments use Gen AI tools to streamline work processes, such as summarising research papers or scientific articles, highlighting a grassroots adoption trend. Individual researchers are using AI to enhance productivity, even when no formal company-wide strategies are in place.While AI tools automate and accelerate R&D tasks, human review is still required to ensure final submissions are correct and satisfactory. Gen AI is proving to reduce time spent on administrative tasks for teams and improve research accuracy and depth, with some companies experiencing 20% to 30% gains in research productivity. KPIs for success in healthcare product programmesMeasuring business performance is essential in the healthcare sector. The number one goal is, of course, to deliver high-quality care, yet simultaneously maintain efficient operations. By measuring and analysing KPIs, healthcare providers are in a better position to improve patient outcomes through their data-based considerations. KPIs can also improve resource allocation, and encourage continuous improvement in all areas of care. In terms of healthcare product programmes, these structured initiatives prioritise the development, delivery, and continual optimisation of medical products. But to be a success, they require cross-functional coordination of clinical, technical, regulatory, and business teams. Time to market is critical, ensuring a product moves from the concept stage to launch as quickly as possible.Of particular note is the emphasis needing to be placed on labelling and documentation. McKinsey notes that AI-assisted labelling has resulted in a 20%-30% improvement in operational efficiency. Resource utilisation rates are also important, showing how efficiently time, budget, and/or headcount are used during the developmental stage of products. In the healthcare sector, KPIs ought to focus on several factors, including operational efficiency, patient outcomes, financial health of the business, and patient satisfaction. To achieve a comprehensive view of performance, these can be categorised into financial, operational, clinical quality, and patient experience.Bridging user experience with technical precision – design awardsInnovation is no longer solely judged by technical performance with user experiencebeing equally important. Some of the latest innovations in healthcare are recognised at the UX Design Awards, products that exemplify the best in user experience as well as technical precision. Top products prioritise the needs and experiences of both patients and healthcare professionals, also ensuring each product meets the rigorous clinical and regulatory standards of the sector. One example is the CIARTIC Move by Siemens Healthineers, a self-driving 3D C-arm imaging system that lets surgeons operate, controlling the device wirelessly in a sterile field. Computer hardware company ASUS has also received accolades for its HealthConnect App and VivoWatch Series, showcasing the fusion of AIoT-driven smart healthcare solutions with user-friendly interfaces – sometimes in what are essentially consumer devices. This demonstrates how technical innovation is being made accessible and becoming increasingly intuitive as patients gain technical fluency.  Navigating regulatory and product development pathways simultaneously The establishing of clinical and regulatory paths is important, as this enables healthcare teams to feed a twin stream of findings back into development. Gen AI adoption has become a transformative approach, automating the production and refining of complex documents, mixed data sets, and structured and unstructured data. By integrating regulatory considerations early and adopting technologies like Gen AI as part of agile practices, healthcare product programmes help teams navigate a regulatory landscape that can often shift. Baking a regulatory mindset into a team early helps ensure compliance and continued innovation. Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.Explore other upcoming enterprise technology events and webinars powered by TechForge here.
    #medtech #hardware #clinical #application #programmes
    MedTech AI, hardware, and clinical application programmes
    Modern healthcare innovations span AI, devices, software, images, and regulatory frameworks, all requiring stringent coordination. Generative AI arguably has the strongest transformative potential in healthcare technology programmes, with it already being applied across various domains, such as R&D, commercial operations, and supply chain management.Traditional models for medical appointments, like face-to-face appointments, and paper-based processes may not be sufficient to meet the fast-paced, data-driven medical landscape of today. Therefore, healthcare professionals and patients are seeking more convenient and efficient ways to access and share information, meeting the complex standards of modern medical science. According to McKinsey, Medtech companies are at the forefront of healthcare innovation, estimating they could capture between billion and billion annually in productivity gains. Through GenAI adoption, an additional billion plus in revenue is estimated from products and service innovations. A McKinsey 2024 survey revealed around two thirds of Medtech executives have already implemented Gen AI, with approximately 20% scaling their solutions up and reporting substantial benefits to productivity.  While advanced technology implementation is growing across the medical industry, challenges persist. Organisations face hurdles like data integration issues, decentralised strategies, and skill gaps. Together, these highlight a need for a more streamlined approach to Gen AI deployment. Of all the Medtech domains, R&D is leading the way in Gen AI adoption. Being the most comfortable with new technologies, R&D departments use Gen AI tools to streamline work processes, such as summarising research papers or scientific articles, highlighting a grassroots adoption trend. Individual researchers are using AI to enhance productivity, even when no formal company-wide strategies are in place.While AI tools automate and accelerate R&D tasks, human review is still required to ensure final submissions are correct and satisfactory. Gen AI is proving to reduce time spent on administrative tasks for teams and improve research accuracy and depth, with some companies experiencing 20% to 30% gains in research productivity. KPIs for success in healthcare product programmesMeasuring business performance is essential in the healthcare sector. The number one goal is, of course, to deliver high-quality care, yet simultaneously maintain efficient operations. By measuring and analysing KPIs, healthcare providers are in a better position to improve patient outcomes through their data-based considerations. KPIs can also improve resource allocation, and encourage continuous improvement in all areas of care. In terms of healthcare product programmes, these structured initiatives prioritise the development, delivery, and continual optimisation of medical products. But to be a success, they require cross-functional coordination of clinical, technical, regulatory, and business teams. Time to market is critical, ensuring a product moves from the concept stage to launch as quickly as possible.Of particular note is the emphasis needing to be placed on labelling and documentation. McKinsey notes that AI-assisted labelling has resulted in a 20%-30% improvement in operational efficiency. Resource utilisation rates are also important, showing how efficiently time, budget, and/or headcount are used during the developmental stage of products. In the healthcare sector, KPIs ought to focus on several factors, including operational efficiency, patient outcomes, financial health of the business, and patient satisfaction. To achieve a comprehensive view of performance, these can be categorised into financial, operational, clinical quality, and patient experience.Bridging user experience with technical precision – design awardsInnovation is no longer solely judged by technical performance with user experiencebeing equally important. Some of the latest innovations in healthcare are recognised at the UX Design Awards, products that exemplify the best in user experience as well as technical precision. Top products prioritise the needs and experiences of both patients and healthcare professionals, also ensuring each product meets the rigorous clinical and regulatory standards of the sector. One example is the CIARTIC Move by Siemens Healthineers, a self-driving 3D C-arm imaging system that lets surgeons operate, controlling the device wirelessly in a sterile field. Computer hardware company ASUS has also received accolades for its HealthConnect App and VivoWatch Series, showcasing the fusion of AIoT-driven smart healthcare solutions with user-friendly interfaces – sometimes in what are essentially consumer devices. This demonstrates how technical innovation is being made accessible and becoming increasingly intuitive as patients gain technical fluency.  Navigating regulatory and product development pathways simultaneously The establishing of clinical and regulatory paths is important, as this enables healthcare teams to feed a twin stream of findings back into development. Gen AI adoption has become a transformative approach, automating the production and refining of complex documents, mixed data sets, and structured and unstructured data. By integrating regulatory considerations early and adopting technologies like Gen AI as part of agile practices, healthcare product programmes help teams navigate a regulatory landscape that can often shift. Baking a regulatory mindset into a team early helps ensure compliance and continued innovation. Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.Explore other upcoming enterprise technology events and webinars powered by TechForge here. #medtech #hardware #clinical #application #programmes
    WWW.ARTIFICIALINTELLIGENCE-NEWS.COM
    MedTech AI, hardware, and clinical application programmes
    Modern healthcare innovations span AI, devices, software, images, and regulatory frameworks, all requiring stringent coordination. Generative AI arguably has the strongest transformative potential in healthcare technology programmes, with it already being applied across various domains, such as R&D, commercial operations, and supply chain management.Traditional models for medical appointments, like face-to-face appointments, and paper-based processes may not be sufficient to meet the fast-paced, data-driven medical landscape of today. Therefore, healthcare professionals and patients are seeking more convenient and efficient ways to access and share information, meeting the complex standards of modern medical science. According to McKinsey, Medtech companies are at the forefront of healthcare innovation, estimating they could capture between $14 billion and $55 billion annually in productivity gains. Through GenAI adoption, an additional $50 billion plus in revenue is estimated from products and service innovations. A McKinsey 2024 survey revealed around two thirds of Medtech executives have already implemented Gen AI, with approximately 20% scaling their solutions up and reporting substantial benefits to productivity.  While advanced technology implementation is growing across the medical industry, challenges persist. Organisations face hurdles like data integration issues, decentralised strategies, and skill gaps. Together, these highlight a need for a more streamlined approach to Gen AI deployment. Of all the Medtech domains, R&D is leading the way in Gen AI adoption. Being the most comfortable with new technologies, R&D departments use Gen AI tools to streamline work processes, such as summarising research papers or scientific articles, highlighting a grassroots adoption trend. Individual researchers are using AI to enhance productivity, even when no formal company-wide strategies are in place.While AI tools automate and accelerate R&D tasks, human review is still required to ensure final submissions are correct and satisfactory. Gen AI is proving to reduce time spent on administrative tasks for teams and improve research accuracy and depth, with some companies experiencing 20% to 30% gains in research productivity. KPIs for success in healthcare product programmesMeasuring business performance is essential in the healthcare sector. The number one goal is, of course, to deliver high-quality care, yet simultaneously maintain efficient operations. By measuring and analysing KPIs, healthcare providers are in a better position to improve patient outcomes through their data-based considerations. KPIs can also improve resource allocation, and encourage continuous improvement in all areas of care. In terms of healthcare product programmes, these structured initiatives prioritise the development, delivery, and continual optimisation of medical products. But to be a success, they require cross-functional coordination of clinical, technical, regulatory, and business teams. Time to market is critical, ensuring a product moves from the concept stage to launch as quickly as possible.Of particular note is the emphasis needing to be placed on labelling and documentation. McKinsey notes that AI-assisted labelling has resulted in a 20%-30% improvement in operational efficiency. Resource utilisation rates are also important, showing how efficiently time, budget, and/or headcount are used during the developmental stage of products. In the healthcare sector, KPIs ought to focus on several factors, including operational efficiency, patient outcomes, financial health of the business, and patient satisfaction. To achieve a comprehensive view of performance, these can be categorised into financial, operational, clinical quality, and patient experience.Bridging user experience with technical precision – design awardsInnovation is no longer solely judged by technical performance with user experience (UX) being equally important. Some of the latest innovations in healthcare are recognised at the UX Design Awards, products that exemplify the best in user experience as well as technical precision. Top products prioritise the needs and experiences of both patients and healthcare professionals, also ensuring each product meets the rigorous clinical and regulatory standards of the sector. One example is the CIARTIC Move by Siemens Healthineers, a self-driving 3D C-arm imaging system that lets surgeons operate, controlling the device wirelessly in a sterile field. Computer hardware company ASUS has also received accolades for its HealthConnect App and VivoWatch Series, showcasing the fusion of AIoT-driven smart healthcare solutions with user-friendly interfaces – sometimes in what are essentially consumer devices. This demonstrates how technical innovation is being made accessible and becoming increasingly intuitive as patients gain technical fluency.  Navigating regulatory and product development pathways simultaneously The establishing of clinical and regulatory paths is important, as this enables healthcare teams to feed a twin stream of findings back into development. Gen AI adoption has become a transformative approach, automating the production and refining of complex documents, mixed data sets, and structured and unstructured data. By integrating regulatory considerations early and adopting technologies like Gen AI as part of agile practices, healthcare product programmes help teams navigate a regulatory landscape that can often shift. Baking a regulatory mindset into a team early helps ensure compliance and continued innovation. (Image source: “IBM Achieves New Deep Learning Breakthrough” by IBM Research is licensed under CC BY-ND 2.0.)Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.Explore other upcoming enterprise technology events and webinars powered by TechForge here.
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  • design/leader: Sheppard Robson’s Michael Davies

    Michael Davies is head of Stix Design, the graphic design and branding arm of architecture firm Sheppard Robson. He’s worked on BBC Cardiff, UCL Marshgate and Freshfields law firm’s London HQ.
    Design
    What would your monograph be called?
    No, I don’t shop at High and Mighty. I am the first-born son of West African parents, and growing up, I stood out because I was very tall – I’m now 6’ 7” – and also one of the only black kids at school. This led to a strong desire to fit in.
    Maybe this has made me always feel really comfortable as part of the team, working shoulder-to-shoulder to create work that stands out. But, of course, this instinct to collaborate is balanced with my idiosyncrasies and expressing my own perspectives on work and life.
    And yes, I shop at the same places as everyone else.
    What recent design work made you a bit jealous?
    I really like the wayfinding scheme at Borough Yards by f.r.a. When I first went there, the designs really spoke to me as a body of work. The work hit every button – intuition, intrigue, interaction, story-telling. The lot.
    It’s how I would love to have answered the brief.
    f.r.a.’s wayfinding work at Borough Yards
    What’s an unusual place you get inspiration from?
    I have a few. Salts Mill at Saltaire – a former mill housing art galleries, shops, and cafe and diner – and The Pheasantry Cafe at Bushy Park, but my current favourite has to be the ground floor cafe at the V&A.
    The sheer scale of its beauty and ambience is always surprising. They’ll throw in a quartet every now and then, in case you might think it isn’t atmospheric enough. It’s great for people-watching, too – I bet it’s a good spot for writers.
    Name something that is brilliantly designed, but overlooked.
    It has to be a brand new pencil. The very sight of one conjures up so much potential before you’ve even made a mark.
    What object in your studio best sums up your taste?
    Perhaps not strictly an object, but I Iove the cupboard-sized meeting room in the far corner of the second floor of our Camden office. It has a huge, cantilevered window that looks out onto our green roof.
    In the summer it turns into a full-on meadow. It’s a great place to feel the breeze, feel connected to nature and think.
    The view from the second floor meeting room
    Leadership
    What feedback felt brutal at the time, but turned out to be useful?
    Earlier in my senior career I worked for someone who’d employed a number of us from a previous agency. The familiarity was a key factor in why I took the position.
    A good friend and design director there advised me to step up and assume full responsibility for all aspects of the projects I worked on – “Don’t wait for instruction from your design leader, try to come forward with solutions rather than asking what should you do.”
    His implication was, “Don’t be too comfortable” and try not to lean into my design leader too often. Be more proactive.
    This proved to be a difficult period of transition for me at the time, with lots of sleepless nights questioning my every decision. Eventually, I learned there’s a value to making mistakes as it afforded me the opportunity to grow. That outcome was career-changing.
    What’s an underappreciated skill that design leaders need?
    Make the process as enjoyable as you can. A little self-deprecation and good humour goes a long way. Don’t take yourself too seriously, and be honest with praise – say when something goes well or looks great, just as you would when it doesn’t.
    What keeps you up at night?
    I work with a smallish team in a large organisation, so occasionally, several jobs might come in from different directions, all at the same time. That can be quite stressful. There’s always that one job that’s taking too long to land, you take on others to fill the gap and then it suddenly drops – arrrgh!
    What trait is non-negotiable in new hires?
    Working alongside so many different skill-sets here at Sheppard Robson affords us the luxury of attacking problems from all sides.
    The key to doing this successfully is through open lines of communication. I need good communicators and great listeners. Their work will always speak for itself, but those two traits make all the difference.
    Complete this sentence, “I wish more clients…”
    …would allow us to just lead the way. I know this isn’t always easy for clients, seeing as creativity is a totally different language/science to some.
    However, there’s no need to fight the process. Take your time selecting the right agency, then trust us, and enjoy the journey.
    #designleader #sheppard #robsons #michael #davies
    design/leader: Sheppard Robson’s Michael Davies
    Michael Davies is head of Stix Design, the graphic design and branding arm of architecture firm Sheppard Robson. He’s worked on BBC Cardiff, UCL Marshgate and Freshfields law firm’s London HQ. Design What would your monograph be called? No, I don’t shop at High and Mighty. I am the first-born son of West African parents, and growing up, I stood out because I was very tall – I’m now 6’ 7” – and also one of the only black kids at school. This led to a strong desire to fit in. Maybe this has made me always feel really comfortable as part of the team, working shoulder-to-shoulder to create work that stands out. But, of course, this instinct to collaborate is balanced with my idiosyncrasies and expressing my own perspectives on work and life. And yes, I shop at the same places as everyone else. What recent design work made you a bit jealous? I really like the wayfinding scheme at Borough Yards by f.r.a. When I first went there, the designs really spoke to me as a body of work. The work hit every button – intuition, intrigue, interaction, story-telling. The lot. It’s how I would love to have answered the brief. f.r.a.’s wayfinding work at Borough Yards What’s an unusual place you get inspiration from? I have a few. Salts Mill at Saltaire – a former mill housing art galleries, shops, and cafe and diner – and The Pheasantry Cafe at Bushy Park, but my current favourite has to be the ground floor cafe at the V&A. The sheer scale of its beauty and ambience is always surprising. They’ll throw in a quartet every now and then, in case you might think it isn’t atmospheric enough. It’s great for people-watching, too – I bet it’s a good spot for writers. Name something that is brilliantly designed, but overlooked. It has to be a brand new pencil. The very sight of one conjures up so much potential before you’ve even made a mark. What object in your studio best sums up your taste? Perhaps not strictly an object, but I Iove the cupboard-sized meeting room in the far corner of the second floor of our Camden office. It has a huge, cantilevered window that looks out onto our green roof. In the summer it turns into a full-on meadow. It’s a great place to feel the breeze, feel connected to nature and think. The view from the second floor meeting room Leadership What feedback felt brutal at the time, but turned out to be useful? Earlier in my senior career I worked for someone who’d employed a number of us from a previous agency. The familiarity was a key factor in why I took the position. A good friend and design director there advised me to step up and assume full responsibility for all aspects of the projects I worked on – “Don’t wait for instruction from your design leader, try to come forward with solutions rather than asking what should you do.” His implication was, “Don’t be too comfortable” and try not to lean into my design leader too often. Be more proactive. This proved to be a difficult period of transition for me at the time, with lots of sleepless nights questioning my every decision. Eventually, I learned there’s a value to making mistakes as it afforded me the opportunity to grow. That outcome was career-changing. What’s an underappreciated skill that design leaders need? Make the process as enjoyable as you can. A little self-deprecation and good humour goes a long way. Don’t take yourself too seriously, and be honest with praise – say when something goes well or looks great, just as you would when it doesn’t. What keeps you up at night? I work with a smallish team in a large organisation, so occasionally, several jobs might come in from different directions, all at the same time. That can be quite stressful. There’s always that one job that’s taking too long to land, you take on others to fill the gap and then it suddenly drops – arrrgh! What trait is non-negotiable in new hires? Working alongside so many different skill-sets here at Sheppard Robson affords us the luxury of attacking problems from all sides. The key to doing this successfully is through open lines of communication. I need good communicators and great listeners. Their work will always speak for itself, but those two traits make all the difference. Complete this sentence, “I wish more clients…” …would allow us to just lead the way. I know this isn’t always easy for clients, seeing as creativity is a totally different language/science to some. However, there’s no need to fight the process. Take your time selecting the right agency, then trust us, and enjoy the journey. #designleader #sheppard #robsons #michael #davies
    WWW.DESIGNWEEK.CO.UK
    design/leader: Sheppard Robson’s Michael Davies
    Michael Davies is head of Stix Design, the graphic design and branding arm of architecture firm Sheppard Robson. He’s worked on BBC Cardiff, UCL Marshgate and Freshfields law firm’s London HQ. Design What would your monograph be called? No, I don’t shop at High and Mighty. I am the first-born son of West African parents, and growing up, I stood out because I was very tall – I’m now 6’ 7” – and also one of the only black kids at school. This led to a strong desire to fit in. Maybe this has made me always feel really comfortable as part of the team, working shoulder-to-shoulder to create work that stands out. But, of course, this instinct to collaborate is balanced with my idiosyncrasies and expressing my own perspectives on work and life. And yes, I shop at the same places as everyone else. What recent design work made you a bit jealous? I really like the wayfinding scheme at Borough Yards by f.r.a. When I first went there, the designs really spoke to me as a body of work. The work hit every button – intuition, intrigue, interaction, story-telling. The lot. It’s how I would love to have answered the brief. f.r.a.’s wayfinding work at Borough Yards What’s an unusual place you get inspiration from? I have a few. Salts Mill at Saltaire – a former mill housing art galleries, shops, and cafe and diner – and The Pheasantry Cafe at Bushy Park, but my current favourite has to be the ground floor cafe at the V&A. The sheer scale of its beauty and ambience is always surprising. They’ll throw in a quartet every now and then, in case you might think it isn’t atmospheric enough. It’s great for people-watching, too – I bet it’s a good spot for writers. Name something that is brilliantly designed, but overlooked. It has to be a brand new pencil. The very sight of one conjures up so much potential before you’ve even made a mark. What object in your studio best sums up your taste? Perhaps not strictly an object, but I Iove the cupboard-sized meeting room in the far corner of the second floor of our Camden office. It has a huge, cantilevered window that looks out onto our green roof. In the summer it turns into a full-on meadow. It’s a great place to feel the breeze, feel connected to nature and think. The view from the second floor meeting room Leadership What feedback felt brutal at the time, but turned out to be useful? Earlier in my senior career I worked for someone who’d employed a number of us from a previous agency. The familiarity was a key factor in why I took the position. A good friend and design director there advised me to step up and assume full responsibility for all aspects of the projects I worked on – “Don’t wait for instruction from your design leader, try to come forward with solutions rather than asking what should you do.” His implication was, “Don’t be too comfortable” and try not to lean into my design leader too often. Be more proactive. This proved to be a difficult period of transition for me at the time, with lots of sleepless nights questioning my every decision. Eventually, I learned there’s a value to making mistakes as it afforded me the opportunity to grow. That outcome was career-changing. What’s an underappreciated skill that design leaders need? Make the process as enjoyable as you can. A little self-deprecation and good humour goes a long way. Don’t take yourself too seriously, and be honest with praise – say when something goes well or looks great, just as you would when it doesn’t. What keeps you up at night? I work with a smallish team in a large organisation, so occasionally, several jobs might come in from different directions, all at the same time. That can be quite stressful. There’s always that one job that’s taking too long to land, you take on others to fill the gap and then it suddenly drops – arrrgh! What trait is non-negotiable in new hires? Working alongside so many different skill-sets here at Sheppard Robson affords us the luxury of attacking problems from all sides. The key to doing this successfully is through open lines of communication. I need good communicators and great listeners. Their work will always speak for itself, but those two traits make all the difference. Complete this sentence, “I wish more clients…” …would allow us to just lead the way. I know this isn’t always easy for clients, seeing as creativity is a totally different language/science to some. However, there’s no need to fight the process. Take your time selecting the right agency, then trust us, and enjoy the journey.
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