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

Montreal City Hall – Beaupré Michaud and Associates, Architects in collaboration with MU Architecture, Montreal. Photo credit: Raphaël Thibodeau The Ordre des architectes du Québechas revealed the winners of its 2025 Awards of Excellence in Architecture. A total of eleven projects were recognized at a gala hosted by Jean-René Dufort at Espace St-Denis in Montreal. The Grand Prix d’excellence en architecture was awarded to the restoration of Montreal City Hall , a major project led by Beaupré Michaud et Associés, architects, and MU Architecture. This complex project successfully preserves the building’s historical qualities while transforming it into an exemplary place in terms of energy and ecology.  Guided by plans from the 1920s, the architects revived this building by equipping it with contemporary, efficient, more open, and more accessible features for residents. In addition to the heritage restoration, the team also reconciled old and contemporary technologies, energy efficiency, universal accessibility, and the reappropriation of spaces that had become dilapidated. The People’s Choice Award was presented to the Coop Milieu de l’île, designed by Pivot: Coopérative d’architecture. Located in Outremont, this 91-unit intergenerational housing cooperative was born from the initiative of a group of committed citizens looking to address the housing crisis by creating affordable, off-market housing. In the context of the housing crisis, the jury emphasized that this project, which is also the recipient of an Award of Excellence, designed by and for its residents, acts as a “breath of fresh air in Outremont.” Coop Milieu de l’île. Pivot: Architecture Cooperative, Montreal. Photo credit: Annie Fafard “The projects we evaluated this year were truly remarkable in their richness and diversity. The jury found in them everything that makes Quebec architecture so strong and unique: rigor, attention to detail, and respect for the context and built heritage. We saw emblematic projects, but also discreet gestures, almost invisible in the landscape. Some projects rehabilitated forgotten places, transformed historic buildings, or even imagined new spaces for collective living. All, in their own way, highlighted the powerful impact of built quality on our living environments,” said Gabrielle Nadeau, chair of the OAQ Awards of Excellence Jury. The jury for the 2025 Awards of Excellence in Architecture was chaired by Gabrielle Nadeau, principal design architect, COBE in Copenhagen. It also included architects Marianne Charbonneau of Agence Spatiale, Maxime-Alexis Frappier of ACDF, and Guillaume Martel-Trudel of Provencher-Roy. Élène Levasseur, director of research and education at Architecture sans frontières Québec, acted as the public representative. Through the Awards of Excellence in Architecture, presented annually, the Order aims to raise awareness among Quebecers of the multiple dimensions of architectural quality, in addition to promoting the role of the architects in the design of inspiring, sustainable and thoughtful senior living environments. The full list of winners include the following. Habitat Sélénite by _naturehumaine Habitat Sélénite – _naturehumaine, Eastman. Photo: Raphaël Thibodeau École secondaire du Bosquet by ABCP | Menkès Shooner Dagenais LeTourneux | Bilodeau Baril Leeming Architectes École secondaire du Bosquet – ABCP | Menkès Shooner Dagenais LeTourneux | Bilodeau Baril Leeming Architectes, Drummondville. Photo: Stéphane Brügger Bibliothèque Gabrielle-Roy by Saucier + Perrotte Architectes et GLCRM Architectes Bibliothèque Gabrielle-Roy – Saucier + Perrotte Architectes et GLCRM Architectes, Québec. Photo: Olivier Blouin Maison A by Atelier Pierre Thibault Maison A – Atelier Pierre Thibault, Saint-Nicolas. Photo: Maxime Brouillet Nouvel Hôtel de Ville de La Pêche by BGLA Architecture et Design Urbain Nouvel Hôtel de Ville de La Pêche – BGLA Architecture et Design Urbain, La Pêche. Photo: Stéphane Brügger / Dominique Laroche École du Zénith by Pelletier de Fontenay + Leclerc École du Zénith – Pelletier de Fontenay + Leclerc, Shefford. Photo: James Brittain / David Boyer Le Paquebot by _naturehumaine Le Paquebot – _naturehumaine, Montréal. Photo: Ronan Mézière Coopérative funéraire la Seigneurie by ultralocal architectes Coopérative funéraire la Seigneurie – ultralocal architectes, Québec. Photo credit: Paul Dussault Site d’observation des bélugas Putep’t-awt by atelier5 + mainstudio Site d’observation des bélugas Putep’t-awt – atelier5 + mainstudio, Cacouna. Photo: Stéphane Groleau The post OAQ Awards of Excellence winners announced appeared first on Canadian Architect. #oaq #awards #excellence #winners #announced

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

Ivy Grant, SVP of Strategy & Operations, Twilio June 13, 20255 Min Readpeshkova via alamy stockAsk technologists and enterprise leaders what they hope AI will deliver, and most will land on some iteration of the "T" word: transformation. No surprise, AI and its “cooler than you” cousin, generative AI, have been hyped nonstop for the past 24 months. But therein lies the problem. Many organizations are rushing to implement AI without a grasp on the return on investment, leading to high spend and low impact. Without anchoring AI to clear friction points and acceleration opportunities, companies invite fatigue, anxiety and competitive risk. Two-thirds of C-suite execs say GenAI has created tension and division within their organizations; nearly half say it’s “tearing their company apart.” Mostreport adoption challenges; more than a third call it a massive disappointment. While AI's potential is irrefutable, companies need to reject the narrative of AI as a standalone strategy or transformational savior. Its true power is as a catalyst to amplify what already works and surface what could. Here are three principles to make that happen. 1. Start with friction, not function Many enterprises struggle with where to start when integrating AI. My advice: Start where the pain is greatest. Identify the processes that create the most friction and work backward from there. AI is a tool, not a solution. By mapping real pain points to AI use cases, you can hone investments to the ripest fruit rather than simply where it hangs at the lowest. Related:For example, one of our top sources of customer pain was troubleshooting undeliverable messages, which forced users to sift through error code documentation. To solve this, an AI assistant was introduced to detect anomalies, explain causes in natural language, and guide customers toward resolution. We achieved a 97% real-time resolution rate through a blend of conversational AI and live support. Most companies have long-standing friction points that support teams routinely explain. Or that you’ve developed organizational calluses over; problems considered “just the cost of doing business.” GenAI allows leaders to revisit these areas and reimagine what’s possible. 2. The need forspeed We hear stories of leaders pushing an “all or nothing” version of AI transformation: Use AI to cut functional headcount or die. Rather than leading with a “stick” through wholesale transformation mandates or threats to budgets, we must recognize AI implementation as a fundamental culture change. Just as you wouldn't expect to transform your company culture overnight by edict, it's unreasonable to expect something different from your AI transformation. Related:Some leaders have a tendency to move faster than the innovation ability or comfort level of their people. Most functional leads aren’t obstinate in their slow adoption of AI tools, their long-held beliefs to run a process or to assess risks. We hired these leaders for their decades of experience in “what good looks like” and deep expertise in incremental improvements; then we expect them to suddenly define a futuristic vision that challenges their own beliefs. As executive leaders, we must give grace, space and plenty of “carrots” -- incentives, training, and support resources -- to help them reimagine complex workflows with AI. And, we must recognize that AI has the ability to make progress in ways that may not immediately create cost efficiencies, such as for operational improvements that require data cleansing, deep analytics, forecasting, dynamic pricing, and signal sensing. These aren’t the sexy parts of AI, but they’re the types of issues that require superhuman intelligence and complex problem-solving that AI was made for. 3. A flywheel of acceleration The other transformation that AI should support is creating faster and broader “test and learn” cycles. AI implementation is not a linear process with start here and end there. Organizations that want to leverage AI as a competitive advantage should establish use cases where AI can break down company silos and act as a catalyst to identify the next opportunity. That identifies the next as a flywheel of acceleration. This flywheel builds on accumulated learnings, making small successes into larger wins while avoiding costly AI disasters from rushed implementation. Related:For example, at Twilio we are building a customer intelligence platform that analyzes thousands of conversations to identify patterns and drive insights. If we see multiple customers mention a competitor's pricing, it could signal a take-out campaign. What once took weeks to recognize and escalate can now be done in near real-time and used for highly coordinated activations across marketing, product, sales, and other teams. With every AI acceleration win, we uncover more places to improve hand-offs, activation speed, and business decision-making. That flywheel of innovation is how true AI transformation begins to drive impactful business outcomes. Ideas to Fuel Your AI Strategy Organizations can accelerate their AI implementations through these simple shifts in approach: Revisit your long-standing friction points, both customer-facing and internal, across your organization -- particularly explore the ones you thought were “the cost of doing business” Don’t just look for where AI can reduce manual processes, but find the highly complex problems and start experimenting Support your functional experts with AI-driven training, resources, tools, and incentives to help them challenge their long-held beliefs about what works for the future Treat AI implementation as a cultural change that requires time, experimentation, learning, and carrots Recognize that transformation starts with a flywheel of acceleration, where each new experiment can lead to the next big discovery The most impactful AI implementations don’t rush transformation; they strategically accelerate core capabilities and unlock new ones to drive measurable change. About the AuthorIvy GrantSVP of Strategy & Operations, Twilio Ivy Grant is Senior Vice President of Strategy & Operations at Twilio where she leads strategic planning, enterprise analytics, M&A Integration and is responsible for driving transformational initiatives that enable Twilio to continuously improve its operations. Prior to Twilio, Ivy’s career has balanced senior roles in strategy consulting at McKinsey & Company, Edelman and PwC with customer-centric operational roles at Walmart, Polo Ralph Lauren and tech startup Eversight Labs. She loves solo international travel, hugging exotic animals and boxing. Ivy has an MBA from NYU’s Stern School of Business and a BS in Applied Economics from Cornell University. See more from Ivy GrantReportsMore ReportsNever Miss a Beat: Get a snapshot of the issues affecting the IT industry straight to your inbox.SIGN-UPYou May Also Like #why #companies #need #reimagine #their

Few roles evolve as quickly as that of the modern CIO. A great way to prepare for a future that is largely unknown is to build your adaptability skills through diverse work experiences, says Bhavik Rao, CIO for the Americas at Vertiv. Learn from your wins and your losses and carry on. Stay free of comfort zones and run towards the chaos. Leaders are born of challenges and not from comfort.Bhavik shares what he’s facing now, how he’s navigating it, and the hard-won lessons that helped shape his approach to IT leadership.Here’s what he had to say:What has your career path looked like so far? I actually started my career as a techno-functional consultant working with the public sector. That early experience gave me a solid grounding in both the technical and process side of enterprise systems. From there, I moved into consulting, which really opened up my world. I had the opportunity to work across multiple industries, leading everything from mobile app development and eCommerce deployments to omnichannel initiatives, data platforms, ERP rollouts, and ultimately large-scale digital transformation and IT strategy programs. It was fast paced, challenging, and incredibly rewarding.  That diversity shaped the way I think today. I learned how to adapt quickly, connect dots across domains, and communicate with everyone from developers to CXOs. Eventually, that path led me to Vertiv, where I now serve as the CIO for the Americas, in addition to leading a couple of global towers, such as data/AI and engineering systems, for example. I’ve been fortunate to lead initiatives that drive operational efficiency, scale GenAI adoption, and turn technology into a true business enabler.   Related:What are the highlights along your career path? There have been several defining moments, both wins and challenges, that have shaped how I lead today. One of the most pivotal chapters has been my time at Vertiv. I joined when the company was still owned by private equity. It was an intense, roll-up-your-sleeves kind of environment. Then, in 2020, we went public -- a huge milestone. But just as we were ramping up our digital transformation, COVID hit, and with it came massive supply chain disruptions. In the middle of all that chaos, I was asked to take over a large-scale transformation program that was struggling. bhBhavik RaoIt wasn’t easy. There were legacy challenges, resistance to change, and real execution pressure. But we rallied, restructured the program, and launched it. That experience taught me a lot about leading under pressure, aligning teams around outcomes, and staying focused even when everything feels like it’s shifting. Related:Another major learning moment was earlier in my career when I lost a large national account I’d spent over seven years building. That was a tough one, but it taught me resilience. I learned not to attach my identity to any one outcome and to keep moving forward with purpose. Then, there are the moments of creation, like launching VeGA, our internal GenAI platform at Vertiv. Seeing it go from idea to impact, with thousands of users and 100+ applications, has been incredibly energizing. It reminded me how powerful it is when innovation meets execution. I’ve also learned the power of being a “player-coach.” I don’t believe in leading from a distance. I get involved, understand the challenges on the ground, and then help teams move forward together.  What’s your vision for the future of sovereign AI? For me, sovereign AI isn’t just a regulatory checkbox; it’s about strategic autonomy. At our company, we are trying to be very intentional about how we scale AI responsibly across our global footprint. So, when I think about sovereign AI, I define it as the ability to control how, where, and why AI is built and deployed with full alignment to your business needs, risk posture, and data boundaries. Related:I’ve seen firsthand how AI becomes a competitive advantage only when you have governance, infrastructure flexibility, and contextual intelligence built in. Our work with VeGA, for example, has shown that employees adopt AI much faster when it’s embedded into secure, business-aligned workflows and not just bolted on from the outside. For CIOs, the shift to sovereign AI means: Designing AI infrastructure that can flex whether it’s hosted internally, cloud-based, or hybrid Building internal AI fluency so your teams aren't fully reliant on black-box solutions Creating a framework for trust and explainability, especially as AI touches regulated and legal processes It’s not about doing everything in-house, but it is about knowing what’s mission-critical to control. In my view, sovereign AI is less about isolation and more about intentional ownership. What do you do for fun or to relax? Golf is my go-to. It keeps me grounded and humble! It’s one of those games that’s as much about mindset as it is about mechanics. I try to work out regularly when I am not traveling for work.  I also enjoy traveling with my family and listening to podcasts.   What advice would you give to young people considering a leadership path in IT? Be curious, stay hands-on, don’t rush the title, and focus on impact. Learn the business, not just the tech. Some of the best technologists I’ve worked with are the ones who understand how a supply chain works or how a sale actually closes. Also, don’t be afraid to take on messy, undefined problems. Run toward the chaos. That’s where leadership is born. And finally, surround yourself with people smarter than you. Build teams that challenge you. That’s where real growth happens.  #cio #chaos #mastery #lessons #vertiv039s

Using AI for ideation, UX scenarios, and the value of world-building in UX designContinue reading on UX Collective » #scenarios #genai

When it comes to Apple, all eyes are on AI. Generative AIis the most disruptive technology we’ve seen in years; it is weaving itself into all parts of life – so why should IT management be left unscathed? It won’t be, and the latest AI-powered IT management features within the Jamf platform will soon be the kind of tools IT expects. Jamf is a leading Apple-in-the-enterprise device management . The company has been working away on AI features to support its solutions for some time, and has at last introduced some of these at its Jamf Nation Live event. The tools are designed to boost efficiency and support better decision-making when it comes to handling your fleets. Of course, you’d expect anyone fielding genAI solutions to say something like that, so what do these tools do? Introducing Jamf AI Assistant Available as a beta, AI Assistant is designed to support tech support! That means it will help IT admins find what they need and help them understand how and why devices they do find are configured. Jamf splits these two paths into two categories: Search skill and Explain skill. Search skill lets admins perform natural language inventory queries across their managed fleets, enabling them to quickly find devices within their flotilla that meet the search parameters. The goal is to make it quicker and easier to audit managed devices for compliance, and to troubleshoot when things go wrong. Explain skill caters to another facet of an IT admin’s daily challenges. As Jamf explains, it means the genAI can translate complex configurations and policies into clear, easy-to-understand language. This helps admins make informed decisions, streamline troubleshooting and manage policies more confidently, says Jamf. While these new Jamf tools don’t automate much of the workload facing IT, it’s not hard to see how once the AI can understand what’s happening on a Mac and identify those devices that meet a set of parameters, the only missing piece is to automate some of the workflow in between. This, of course, is the direction of travel and will likely ripple across IT and every platform. Who knows, it might even make the cost of supporting Windows fleets almost as affordable as that of managing fleets of Apple devices.Beyond AI Jamf also made a handful of announcements outside of AI, including the general availability of Blueprints, a set of tools the company announced at JNUC last year. Blueprints builds on Apple’s Declarative Device Management framework and is designed to simplify and accelerate device configuration by consolidating policies, profiles and restrictions into a single, unified workflow. This makes a lot of sense on a road map to further AI deployment, as well as for anyone attempting to manage and deploy large Apple fleets. I imagine admins preparing for mammoth college- or school-wide deployments will have some optimism that Blueprints could help save time. Don’t neglect that education tech is expected to deploy thousands of devices in a few weeks, so these tools should be significant to them. Jamf continues working on Blueprints, and has introduced a beta release of Configuration Profiles within Blueprints. This tech consists of a new dynamic framework designed to help teams manage devices at scale, thanks to the new dynamic framework for MDM key delivery. Ticket to ride Jamf has offered a Self Service+ portal since earlier this year. Aimed at end-users, the system lets users request, download and update apps, as well as monitor their device security. Those features have been expanded with identity management tools, so users can view their accounts change passwords, and request things like temporary admin access. The beauty of Self Service+ is that it enables users to do these things autonomously while keeping their devices fully auditable and compliant. The idea is that it’s a lot better to focus the expensive tech support teams on the big problems, rather than seeing them bogged down in small, transient, challenges.  The company also introduced Compliance Benchmarks. Based on Apple’s macOS Security Compliance Project, this system helps IT automate the process of securing their Apple devices. Jamf has also added malware detection to its App Installers module, which means every application made available through that system is scanned to maintain security confidence. That’s really important to companies attempting to provision apps to employees, particularly if they want to avoid accidental installs of hacked malware posing as the original app. You can follow me on social media! Join me on BlueSky,  LinkedIn, and Mastodon. #jamf #puts #inside #apple #device