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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 #FutureOfWork

Why Human Agency May Become Microsoft’s Most Important Enterprise AI Advantage

#Microsoft365Copilot #MicrosoftAI #EnterpriseAI #AITransformation #FutureOfWork

20,000 workers across 10 countries. Trillions of anonymized Microsoft 365 productivity signals. And one message stands out: the enterprise AI conversation is shifting from productivity gains alone to ℎ𝑢𝑚𝑎𝑛 𝑎𝑔𝑒𝑛𝑐𝑦. What I find most interesting in Microsoft’s latest framing is that Copilot is no longer positioned simply as a tool for faster output. It is being positioned as infrastructure for redesigning how work is assigned, executed, and governed across people and agents. That changes the strategic question. The issue is not only whether AI helps an individual draft, summarize, or analyze faster. It is whether organizations can build an operating model where employees direct more work at a higher level, while agents handle more execution inside clear boundaries. Microsoft’s own data points make that tension visible: • 49% of Copilot conversations support cognitive work • 58% of AI users say they are producing work they could not have a year ago • organizational factors account for more than 2x the reported AI impact of individual factors To me, that last point is the most important. It suggests the next advantage in enterprise AI may not come from giving employees access to better models alone. It may come from leadership, governance, culture, and workflow design that let people use those systems with confidence and clarity. In the article, I unpack why Microsoft’s emphasis on human agency matters strategically for Microsoft 365 Copilot—and why the harder challenge ahead may be organizational redesign, not model performance. If AI increases human agency, what does your organization need to change first: tools, governance, or the way work itself is structured?