🎮✨ What an exciting time to be a gamer! Did you know that Xbox games are actually helping to save the PS5 from its midlife crisis? 🌟 It's incredible how competition drives innovation and creativity! Just look back at 2017 when Horizon Zero Dawn transformed the PS4 landscape, paving the way for phenomenal titles like Spider-Man and God of War! 🕷️⚔️ This is a reminder that even in the midst of challenges, we can find opportunities for growth and improvement. Let's celebrate the amazing gaming community that pushes each other to reach new heights! 🌈 Keep playing, keep dreaming, and remember: every challenge is just a stepping stone to greatness! 🚀 #XboxGames #PS5 #Gaming

Boris FX just dropped SynthEyes 2025.5, and it’s like they’re trying to solve the world’s biggest mystery: how to auto-track shots without any human intervention. Apparently, AI has become our new best friend in the editing room, helping us do what we’ve been doing since the dawn of cinema—except now we can sit back and let the machines do the heavy lifting. Who knew that the real challenge was just getting the right software to make sure we don’t have to lift a finger? Just wait till you see those “tricky shots” auto-tracked like they’re on a rollercoaster ride. #BorisFX #SynthEyes #3DTracking #AIMotionEstimation #Filmm

The so-called “Creative Bloq’s AI in Focus series” is nothing more than a thinly veiled marketing ploy! Instead of genuinely helping us navigate the complexities of integrating AI into our work, they’re just drumming up clicks with flashy headlines and empty promises. This “ultimate guide” is just a way to profit off the confusion surrounding AI, rather than truly empowering users. It’s infuriating how companies exploit our need for guidance while offering nothing of substance in return. We deserve better than this superficial nonsense that only serves the interests of corporate greed! #AI #TechCritique #CreativeBloq #DigitalCulture #Innovation

Well, well, well! It seems Cory Barlog and his merry band at Santa Monica Studios have decided to keep the suspense alive by not launching a new license. Instead, they’re serving us another helping of the Kratos saga. Because, clearly, nothing screams innovation like rehashing the same old tale, right? I mean, who needs fresh ideas when you've got a grumpy god with a penchant for violence? So, gear up for yet another adventure where Kratos likely makes bad parenting choices while slaying mythical creatures. Let’s just hope this time he learns to read the room – or at least, the Norse mythology handbook. #GodOfWar #CoryBarlog #SantaMonicaStudios #GamingNews

🌟✨ Exciting news, everyone! 🎉 This Prime Day, let's celebrate our all-American spirit with an incredible deal! 🇺🇸 The Artograph EZ Tracer Projector is available at a fantastic price, helping you avoid those pesky tariffs while saving more! 💰💖 This is your chance to take your creativity to the next level and create stunning art like never before! 🌈 Don't miss out on this amazing opportunity to invest in yourself and your passion! Let's make this Prime Day a win for all of us! 🙌💪 #PrimeDayDeals #MadeInAmerica #ArtographEZTracer #CreativityUnleashed #SaveMore

It's absolutely infuriating to see how companies like Acer continue to shove their so-called "cutting-edge technology" down our throats while the actual issues in the tech world remain unaddressed. Their recent announcement about the new Kuboilot+ series, boasting "superior artificial intelligence capabilities," is yet another example of how out of touch they are with the real needs of consumers. Let’s break it down. What exactly are people looking for in a laptop today? Is it just flashy features and buzzwords like "AI"? Or is it more about reliability, usability, and actual performance? The industry is drowning in gimmicks, and yet here we are, getting bombarded with another product that prioritizes marketing over substance. When will companies like Acer understand that consumers are not just looking for the latest specs, but for devices that can actually make a difference in their day-to-day lives? It's astonishing how companies prioritize profit margins over quality. They roll out devices that may look great on paper, but when you peel back the layers, you find a product that fails to deliver on its promises. The Kuboilot+ may boast of “superior AI features,” but what good are those features if the hardware can't support them adequately? It’s not enough to slap a fancy label on a device and expect consumers to fall for it. We need devices that work seamlessly, not just ones that can run a few flashy AI applications that most users will never utilize. Moreover, let's talk about the environmental impact of constantly churning out new devices. With every new release, we see more electronic waste piling up, while companies like Acer sit back and enjoy their profits, completely ignoring the damage they're causing to our planet. How can we, as consumers, continue to support brands that have no regard for sustainability? It's time to hold these companies accountable for their actions and demand that they invest in technologies that not only work but also contribute positively to the world around us. And let's not forget about customer support. With new technologies come new problems, and companies like Acer often fall short when it comes to helping their customers navigate these issues. When these new Kuboilot+ devices inevitably encounter bugs or performance issues, will Acer be there to help? Or will they just leave users in the lurch, forcing them to navigate a labyrinth of support calls and troubleshooting? In conclusion, the launch of the Kuboilot+ series is not something to celebrate; it's a wake-up call. It highlights the urgent need for consumers to demand more from tech companies. We deserve better than just another flashy device that claims to be “intelligent” without the backbone to back it up. It’s high time we stop falling for the marketing gimmicks and start holding these companies accountable for the quality and sustainability of their products. #Acer #KuboilotPlus #ArtificialIntelligence #TechCritique #ConsumerRights

🌟 Hello, wonderful friends! 🌟 Today, I’m bursting with excitement to share something that could elevate your Twitter (or X) game to the next level! 🚀 Do you want to manage your profile like a pro without spending a dime? Well, you’re in the right place! 🎉 In our latest article, we've uncovered **15 FREE tools** that will empower you to not only manage your Twitter presence but also analyze it like a champ! 💪✨ Imagine having the ability to understand your audience better, optimize your posts, and engage with your followers in a way that feels genuine and impactful. Isn’t that amazing? 😍 Whether you’re a budding entrepreneur, a social media enthusiast, or just someone who loves to connect with others, these tools are tailored for you! From basic functionalities to advanced features, we’ve got you covered. 💼💖 1. **Manage Your Time**: One of the best free tools can help you schedule your tweets ahead of time, allowing you to maintain a consistent presence without needing to be online 24/7. ⏰✨ 2. **Analyze Your Impact**: Want to know what resonates with your audience? There are fantastic options that provide insights into engagement metrics, helping you understand which posts are truly making a difference! 📈💥 3. **Engage Meaningfully**: Building a community is essential, and some tools can assist you in reaching out to followers, replying efficiently, and making everyone feel valued. After all, connection is key! 🤝❤️ And if you’re serious about taking it up a notch, we’ll even introduce you to some advanced paid tools that can provide even deeper insights. 👍💡 The best part? You won’t have to break the bank! 🎊 All the recommendations in our article are either completely free or offer great value for a minimal cost. So, what are you waiting for? Dive into the world of Twitter tools and watch your engagement soar! 🌈✨ Remember, every great journey begins with a single step. By utilizing these tools, you’re not just managing a profile; you’re building a brand, fostering relationships, and making your voice heard in this vast digital landscape! 🌍💖 Let’s make our Twitter (or X) experience not just good, but extraordinary! Together, we can create a thriving community that inspires and uplifts! Are you ready to take that leap? 🌠💪 #TwitterTools #SocialMediaSuccess #EngagementBoost #FreeTools #Inspiration

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

Reading Time: 9 minutes In marketing, data isn’t a buzzword. It’s the lifeblood of all successful campaigns. But are you truly harnessing its power, or are you drowning in a sea of information? To answer this question, we sat down with Ankur Kothari, a seasoned Martech expert, to dive deep into this crucial topic. This interview, originally conducted for Chapter 6 of “The Customer Engagement Book: Adapt or Die” explores how businesses can translate raw data into actionable insights that drive real results. Ankur shares his wealth of knowledge on identifying valuable customer engagement data, distinguishing between signal and noise, and ultimately, shaping real-time strategies that keep companies ahead of the curve.   Ankur Kothari Q&A Interview 1. What types of customer engagement data are most valuable for making strategic business decisions? Primarily, there are four different buckets of customer engagement data. I would begin with behavioral data, encompassing website interaction, purchase history, and other app usage patterns. Second would be demographic information: age, location, income, and other relevant personal characteristics. Third would be sentiment analysis, where we derive information from social media interaction, customer feedback, or other customer reviews. Fourth would be the customer journey data. We track touchpoints across various channels of the customers to understand the customer journey path and conversion. Combining these four primary sources helps us understand the engagement data. 2. How do you distinguish between data that is actionable versus data that is just noise? First is keeping relevant to your business objectives, making actionable data that directly relates to your specific goals or KPIs, and then taking help from statistical significance. Actionable data shows clear patterns or trends that are statistically valid, whereas other data consists of random fluctuations or outliers, which may not be what you are interested in. You also want to make sure that there is consistency across sources. Actionable insights are typically corroborated by multiple data points or channels, while other data or noise can be more isolated and contradictory. Actionable data suggests clear opportunities for improvement or decision making, whereas noise does not lead to meaningful actions or changes in strategy. By applying these criteria, I can effectively filter out the noise and focus on data that delivers or drives valuable business decisions. 3. How can customer engagement data be used to identify and prioritize new business opportunities? First, it helps us to uncover unmet needs. By analyzing the customer feedback, touch points, support interactions, or usage patterns, we can identify the gaps in our current offerings or areas where customers are experiencing pain points. Second would be identifying emerging needs. Monitoring changes in customer behavior or preferences over time can reveal new market trends or shifts in demand, allowing my company to adapt their products or services accordingly. Third would be segmentation analysis. Detailed customer data analysis enables us to identify unserved or underserved segments or niche markets that may represent untapped opportunities for growth or expansion into newer areas and new geographies. Last is to build competitive differentiation. Engagement data can highlight where our companies outperform competitors, helping us to prioritize opportunities that leverage existing strengths and unique selling propositions. 4. Can you share an example of where data insights directly influenced a critical decision? I will share an example from my previous organization at one of the financial services where we were very data-driven, which made a major impact on our critical decision regarding our credit card offerings. We analyzed the customer engagement data, and we discovered that a large segment of our millennial customers were underutilizing our traditional credit cards but showed high engagement with mobile payment platforms. That insight led us to develop and launch our first digital credit card product with enhanced mobile features and rewards tailored to the millennial spending habits. Since we had access to a lot of transactional data as well, we were able to build a financial product which met that specific segment’s needs. That data-driven decision resulted in a 40% increase in our new credit card applications from this demographic within the first quarter of the launch. Subsequently, our market share improved in that specific segment, which was very crucial. 5. Are there any other examples of ways that you see customer engagement data being able to shape marketing strategy in real time? When it comes to using the engagement data in real-time, we do quite a few things. In the recent past two, three years, we are using that for dynamic content personalization, adjusting the website content, email messaging, or ad creative based on real-time user behavior and preferences. We automate campaign optimization using specific AI-driven tools to continuously analyze performance metrics and automatically reallocate the budget to top-performing channels or ad segments. Then we also build responsive social media engagement platforms like monitoring social media sentiments and trending topics to quickly adapt the messaging and create timely and relevant content. With one-on-one personalization, we do a lot of A/B testing as part of the overall rapid testing and market elements like subject lines, CTAs, and building various successful variants of the campaigns. 6. How are you doing the 1:1 personalization? We have advanced CDP systems, and we are tracking each customer’s behavior in real-time. So the moment they move to different channels, we know what the context is, what the relevance is, and the recent interaction points, so we can cater the right offer. So for example, if you looked at a certain offer on the website and you came from Google, and then the next day you walk into an in-person interaction, our agent will already know that you were looking at that offer. That gives our customer or potential customer more one-to-one personalization instead of just segment-based or bulk interaction kind of experience. We have a huge team of data scientists, data analysts, and AI model creators who help us to analyze big volumes of data and bring the right insights to our marketing and sales team so that they can provide the right experience to our customers. 7. What role does customer engagement data play in influencing cross-functional decisions, such as with product development, sales, and customer service? Primarily with product development — we have different products, not just the financial products or products whichever organizations sell, but also various products like mobile apps or websites they use for transactions. So that kind of product development gets improved. The engagement data helps our sales and marketing teams create more targeted campaigns, optimize channel selection, and refine messaging to resonate with specific customer segments. Customer service also gets helped by anticipating common issues, personalizing support interactions over the phone or email or chat, and proactively addressing potential problems, leading to improved customer satisfaction and retention. So in general, cross-functional application of engagement improves the customer-centric approach throughout the organization. 8. What do you think some of the main challenges marketers face when trying to translate customer engagement data into actionable business insights? I think the huge amount of data we are dealing with. As we are getting more digitally savvy and most of the customers are moving to digital channels, we are getting a lot of data, and that sheer volume of data can be overwhelming, making it very difficult to identify truly meaningful patterns and insights. Because of the huge data overload, we create data silos in this process, so information often exists in separate systems across different departments. We are not able to build a holistic view of customer engagement. Because of data silos and overload of data, data quality issues appear. There is inconsistency, and inaccurate data can lead to incorrect insights or poor decision-making. Quality issues could also be due to the wrong format of the data, or the data is stale and no longer relevant. As we are growing and adding more people to help us understand customer engagement, I’ve also noticed that technical folks, especially data scientists and data analysts, lack skills to properly interpret the data or apply data insights effectively. So there’s a lack of understanding of marketing and sales as domains. It’s a huge effort and can take a lot of investment. Not being able to calculate the ROI of your overall investment is a big challenge that many organizations are facing. 9. Why do you think the analysts don’t have the business acumen to properly do more than analyze the data? If people do not have the right idea of why we are collecting this data, we collect a lot of noise, and that brings in huge volumes of data. If you cannot stop that from step one—not bringing noise into the data system—that cannot be done by just technical folks or people who do not have business knowledge. Business people do not know everything about what data is being collected from which source and what data they need. It’s a gap between business domain knowledge, specifically marketing and sales needs, and technical folks who don’t have a lot of exposure to that side. Similarly, marketing business people do not have much exposure to the technical side — what’s possible to do with data, how much effort it takes, what’s relevant versus not relevant, and how to prioritize which data sources will be most important. 10. Do you have any suggestions for how this can be overcome, or have you seen it in action where it has been solved before? First, cross-functional training: training different roles to help them understand why we’re doing this and what the business goals are, giving technical people exposure to what marketing and sales teams do. And giving business folks exposure to the technology side through training on different tools, strategies, and the roadmap of data integrations. The second is helping teams work more collaboratively. So it’s not like the technology team works in a silo and comes back when their work is done, and then marketing and sales teams act upon it. Now we’re making it more like one team. You work together so that you can complement each other, and we have a better strategy from day one. 11. How do you address skepticism or resistance from stakeholders when presenting data-driven recommendations? We present clear business cases where we demonstrate how data-driven recommendations can directly align with business objectives and potential ROI. We build compelling visualizations, easy-to-understand charts and graphs that clearly illustrate the insights and the implications for business goals. We also do a lot of POCs and pilot projects with small-scale implementations to showcase tangible results and build confidence in the data-driven approach throughout the organization. 12. What technologies or tools have you found most effective for gathering and analyzing customer engagement data? I’ve found that Customer Data Platforms help us unify customer data from various sources, providing a comprehensive view of customer interactions across touch points. Having advanced analytics platforms — tools with AI and machine learning capabilities that can process large volumes of data and uncover complex patterns and insights — is a great value to us. We always use, or many organizations use, marketing automation systems to improve marketing team productivity, helping us track and analyze customer interactions across multiple channels. Another thing is social media listening tools, wherever your brand is mentioned or you want to measure customer sentiment over social media, or track the engagement of your campaigns across social media platforms. Last is web analytical tools, which provide detailed insights into your website visitors’ behaviors and engagement metrics, for browser apps, small browser apps, various devices, and mobile apps. 13. How do you ensure data quality and consistency across multiple channels to make these informed decisions? We established clear guidelines for data collection, storage, and usage across all channels to maintain consistency. Then we use data integration platforms — tools that consolidate data from various sources into a single unified view, reducing discrepancies and inconsistencies. While we collect data from different sources, we clean the data so it becomes cleaner with every stage of processing. We also conduct regular data audits — performing periodic checks to identify and rectify data quality issues, ensuring accuracy and reliability of information. We also deploy standardized data formats. On top of that, we have various automated data cleansing tools, specific software to detect and correct data errors, redundancies, duplicates, and inconsistencies in data sets automatically. 14. How do you see the role of customer engagement data evolving in shaping business strategies over the next five years? The first thing that’s been the biggest trend from the past two years is AI-driven decision making, which I think will become more prevalent, with advanced algorithms processing vast amounts of engagement data in real-time to inform strategic choices. Somewhat related to this is predictive analytics, which will play an even larger role, enabling businesses to anticipate customer needs and market trends with more accuracy and better predictive capabilities. We also touched upon hyper-personalization. We are all trying to strive toward more hyper-personalization at scale, which is more one-on-one personalization, as we are increasingly capturing more engagement data and have bigger systems and infrastructure to support processing those large volumes of data so we can achieve those hyper-personalization use cases. As the world is collecting more data, privacy concerns and regulations come into play. I believe in the next few years there will be more innovation toward how businesses can collect data ethically and what the usage practices are, leading to more transparent and consent-based engagement data strategies. And lastly, I think about the integration of engagement data, which is always a big challenge. I believe as we’re solving those integration challenges, we are adding more and more complex data sources to the picture. So I think there will need to be more innovation or sophistication brought into data integration strategies, which will help us take a truly customer-centric approach to strategy formulation.   This interview Q&A was hosted with Ankur Kothari, a previous Martech Executive, 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 Ankur Kothari Q&A: Customer Engagement Book Interview appeared first on MoEngage. #ankur #kothari #qampampa #customer #engagement

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