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Maximilian Kenfenheuer

Manager @ BearingPoint

Maximilian Kenfenheuer

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Why Microsoft’s New Focus on Measuring Completed Work Matters for Enterprise AI

Most AI dashboards still tell you what happened in the tool, not what happened in the work. That is why Microsoft’s latest shift around Copilot Cowork measurement stands out to me. The conversation is moving beyond prompts, clicks, and activity counts toward something more useful: whether AI is actually helping people complete meaningful work and return time to the business. For organizations investing in Microsoft AI solutions, that matters. If value is measured only by interaction volume, it is easy to confuse usage with impact. But when Microsoft starts framing measurement around assisted hours, completed work, and business process outcomes, it signals a more mature model for enterprise AI adoption. In the article, I explore: • why this change in measurement is strategically important for Microsoft AI solutions • what it says about the shift from AI engagement metrics to work outcome metrics • why baseline process measurement and role-based use cases matter more than generic adoption reporting • and how organizations can think more clearly about ROI as Copilot and agents become part of operational work For me, this is one of the more important signs that Microsoft AI solutions are being positioned not just as tools people use, but as capabilities businesses need to evaluate against real work transformation. How do you think organizations should measure AI success: by usage, by time returned, or by completed business outcomes?

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Why Power BI Grounding Could Be a Bigger Microsoft AI Shift Than It First Appears

Power BI is becoming a more important part of the Microsoft AI story. One of the more interesting recent updates is that Microsoft 365 Copilot can now reason over Power BI reports and semantic models in Chat and Cowork. For me, that matters because it moves Microsoft AI solutions closer to a question many leaders actually care about: can AI work with governed business metrics, not just documents and conversations? That is a meaningful step. When AI can answer in natural language against enterprise data models people already trust, the conversation shifts from generic productivity to decision support grounded in the business’s own definitions, permissions, and reporting structure. In the article, I explore: • why Power BI grounding changes the strategic value of Microsoft AI solutions • how semantic models help create more reliable AI answers than disconnected data access • why governed metrics may become a key layer in enterprise AI adoption • and what organizations should consider as Copilot moves closer to analytics and operational decision-making For me, this is another sign that Microsoft is building AI not only around content creation, but around enterprise understanding. How important do you think governed analytics context will become in making Microsoft AI solutions truly useful at scale?

Why Work IQ Could Become a Foundational Layer in Microsoft AI Solutions (1)

June 16 is a notable date in Microsoft AI solutions, not because of another chat feature, but because Work IQ APIs are becoming generally available beyond Microsoft 365 Copilot licensing. For me, that signals something bigger: Microsoft is starting to define enterprise AI around a shared intelligence layer for agents, apps, and workflows, not only around one assistant experience. That matters because once AI can reason over chat, files, meetings, people, and actions through a governed, permission-aware layer, the conversation changes. It becomes less about isolated copilots and more about how organizations build reliable AI systems on top of their actual work graph. In the article, I explore: • why Work IQ represents a deeper architectural move in Microsoft AI solutions • how APIs, A2A support, and a compact tool model point toward more scalable agent design • why governance and cost controls are becoming part of the platform story, not an afterthought • and what organizations should consider as Microsoft expands AI from product feature to enterprise intelligence layer I think this is one of the clearest signs yet that Microsoft AI solutions are evolving into infrastructure for how work gets understood and executed. How important do you think a shared intelligence layer like Work IQ will become in enterprise AI strategy?

Posts tagged #MicrosoftAI

Why Microsoft 365 Copilot’s New Design Matters More Than It First Appears

#Microsoft365Copilot #MicrosoftAI #AIAdoption #FutureOfWork #EnterpriseAI

Interface decisions do more strategic work than they get credit for. Microsoft’s redesign of Microsoft 365 Copilot points to something important: enterprise AI adoption is not only about model quality or new agent features. It is also about whether the experience reduces friction enough to become part of everyday work. A cleaner, faster Copilot that sits more naturally inside Microsoft 365 may sound like a product refinement. I think it is better understood as an adoption lever. In the article, I explore why UX and workflow design matter so much for Microsoft AI solutions: • why lower interaction friction can have outsized impact on usage • how in-flow AI experiences change the threshold for everyday adoption • why design consistency supports trust, governance, and scale • and why the next enterprise advantage may come from making AI easier to return to, not just more powerful If AI is going to become part of normal work, the experience has to feel native to work itself. What do you think matters more for enterprise AI adoption now: stronger capabilities, or better integration into the daily flow of work?

Why Model Choice in Microsoft 365 Copilot Could Become a Strategic Enterprise Advantage

#Microsoft365Copilot #MicrosoftAI #AI #CopilotStudio #EnterpriseAI

Model choice inside Microsoft 365 Copilot may become a bigger enterprise differentiator than many people expect. What caught my attention is not just that Microsoft is expanding available models in Copilot environments. It is the operating implication: organizations are moving toward an AI layer where different models can be matched to different kinds of work, with admin controls, visibility, and clear data-handling boundaries. That matters because enterprise AI is no longer one simple question of “do we have a model?” It is increasingly a question of 𝑤ℎ𝑖𝑐ℎ 𝑚𝑜𝑑𝑒𝑙 𝑠ℎ𝑜𝑢𝑙𝑑 ℎ𝑎𝑛𝑑𝑙𝑒 𝑤ℎ𝑖𝑐ℎ 𝑡𝑎𝑠𝑘, 𝑢𝑛𝑑𝑒𝑟 𝑤ℎ𝑖𝑐ℎ 𝑔𝑜𝑣𝑒𝑟𝑛𝑎𝑛𝑐𝑒 𝑐𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛𝑠, 𝑎𝑛𝑑 𝑤𝑖𝑡ℎ 𝑤ℎ𝑎𝑡 𝑡𝑟𝑎𝑑𝑒-𝑜𝑓𝑓 𝑏𝑒𝑡𝑤𝑒𝑒𝑛 𝑠𝑝𝑒𝑒𝑑, 𝑑𝑒𝑝𝑡ℎ, 𝑎𝑛𝑑 𝑟𝑒𝑡𝑒𝑛𝑡𝑖𝑜𝑛 𝑝𝑜𝑠𝑡𝑢𝑟𝑒? In the article, I explore why this shift matters for Microsoft AI solutions: • why model choice is becoming an architectural decision, not just a product feature • how Copilot environments are starting to separate fast everyday work from deeper reasoning work • why admin controls and data-retention signals matter just as much as model quality • and how this could shape the next phase of trusted enterprise AI adoption The next advantage may not come from one model winning outright. It may come from giving organizations a governed way to use the right model for the right job. Do you think enterprise AI will be shaped more by having the best single model, or by orchestrating multiple models well?

Why Domain Exclusion Matters More Than It First Appears in Microsoft 365 Copilot

#Microsoft365Copilot #MicrosoftAI #AIGovernance #EnterpriseAI #Copilot #ResponsibleAI

Up to 1,000 domains can now be excluded from Microsoft 365 Copilot web grounding. That may sound like a small admin setting. I think it is a meaningful signal about where enterprise AI is going. As Copilot becomes more embedded in day-to-day work, the strategic issue is not only how much context AI can access. It is how precisely organizations can shape 𝑤ℎ𝑖𝑐ℎ 𝑒𝑥𝑡𝑒𝑟𝑛𝑎𝑙 𝑐𝑜𝑛𝑡𝑒𝑥𝑡 is allowed to influence responses. What stands out here is the governance implication. Domain exclusion gives admins a way to reduce unwanted or low-trust web sources in grounded answers, which matters for reliability, compliance, and confidence at scale. It also reinforces a broader point: enterprise AI adoption depends not just on capability, but on controllability. In the article, I explore why this kind of policy control matters for Microsoft AI solutions—and why the next differentiator may be the ability to tune AI systems with more precision, not simply make them more powerful. Will enterprise trust in AI be driven more by broader access to information, or by tighter control over what the system is allowed to use?

Why Work IQ Could Become One of Microsoft’s Most Strategic AI Advantages

#Microsoft365Copilot #MicrosoftAI #Copilot #AI #EnterpriseAI #CopilotStudio

June 2 may turn out to be one of the more important dates in Microsoft’s enterprise AI roadmap. That is when Microsoft introduced Work IQ APIs—opening up the intelligence layer behind Microsoft 365 Copilot so agents and applications can reason over work context, not just retrieve isolated data. What stands out to me is the architectural implication. If Work IQ becomes the shared context layer across Microsoft 365, then the competitive advantage is no longer only the assistant interface. It is the ability to give agents secure, permission-aware understanding of how work actually happens across files, meetings, chats, mail, sites, and business systems. In the article, I explore why that matters strategically for Microsoft AI solutions: • Work IQ as infrastructure, not just a feature • why semantic context may matter more than another model upgrade • how governance and user-scoped access shape enterprise trust • why this could accelerate a new generation of Microsoft-based agents The next phase of enterprise AI may be defined less by who has a chatbot—and more by who has the best intelligence layer behind it. Do you think the bigger differentiator will be better models, or better organizational context?

Why Microsoft’s App-Native AI in Word, Excel, and PowerPoint Matters

#Microsoft365 #Copilot #MicrosoftAI #AI #EnterpriseAI

Word, Excel, and PowerPoint are no longer just places where AI helps you draft faster. They are becoming places where AI can 𝑤𝑜𝑟𝑘 𝑤𝑖𝑡ℎ 𝑦𝑜𝑢 𝑖𝑛𝑠𝑖𝑑𝑒 𝑡ℎ𝑒 𝑑𝑜𝑐𝑢𝑚𝑒𝑛𝑡 𝑖𝑡𝑠𝑒𝑙𝑓. That shift matters more than it may first appear. What stands out in Microsoft’s rollout of agentic capabilities across its core productivity apps is the move from generic assistance toward application-native execution: restructuring content in Word, analyzing and reasoning over data in Excel, and building presentations in PowerPoint with deeper in-app context. To me, this is one of the clearest signals yet of where Microsoft’s AI strategy is heading. The value is not only in having Copilot available across Microsoft 365. It is in embedding specialized AI behavior directly into the environments where work is already being done. In the article, I explore why that matters strategically—and why the next enterprise advantage may come from AI that understands the grammar of each application, not just the user’s prompt. Which will create more value in practice: one general assistant everywhere, or app-specific AI that can operate natively inside the tools people already trust?