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Maximilian Kenfenheuer

Manager @ BearingPoint

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#Microsoft365Copilot #MicrosoftAI #Copilot #AITransformation #FutureOfWork

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

Thursday, September 10, 2026 · Maximilian Kenfenheuer

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?

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

Wednesday, September 9, 2026 · Maximilian Kenfenheuer

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)

Tuesday, September 8, 2026 · Maximilian Kenfenheuer

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 Copilot Notebooks May Become One of Microsoft’s Most Important Enterprise AI Moves

Sunday, July 26, 2026 · Maximilian Kenfenheuer

#Microsoft365Copilot #CopilotNotebooks #AI #EnterpriseAI #MicrosoftAI

Most enterprise AI still has a context problem. The useful information is scattered across decks, meeting notes, spreadsheets, chats, whiteboards, and half-finished drafts. So even when the model is strong, the work often starts with rebuilding the project context from scratch. That is why Microsoft’s push around Copilot Notebooks is more strategically important than it may first appear. Notebooks create a bounded workspace where Copilot reasons over selected project materials rather than the entire enterprise by default. Microsoft says users can bring together files, Pages, links, and other references, keep them current as the project evolves, and get responses grounded only in that curated set. It is also expanding access: Copilot Notebooks is now available to Copilot Chat licensed users, not just the full Microsoft 365 Copilot audience. The interesting part is not just better summarization. It is the operating model behind it: scoped context, persistent project memory, and tighter grounding around the actual artifacts of work. Add newer capabilities like audio overviews and Capture for in-person conversations and whiteboard sessions, and Microsoft starts turning messy project context into something AI can actually work with. In the article, I unpack why this matters for enterprise AI adoption, governance, and execution—and why the next competitive layer may be not just models or agents, but the systems that package context into usable workspaces. Do you think enterprise AI will create more value from better reasoning, or from better context architecture?