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Maximilian Kenfenheuer

Manager · Microsoft 365 & AI Transformation @ BearingPoint

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#Microsoft365Copilot #MicrosoftAI #Copilot #AITransformation #FutureOfWork

Measuring Completed Work: Baselines, Assisted Hours and Business Outcomes

Thursday, September 10, 2026 · Maximilian Kenfenheuer

A team can generate thousands of prompts without improving a single core process. Another uses AI rarely but saves hours in contract review. Microsoft increasingly measures Copilot value by completed work and assisted hours instead of activity. Its internal guidance recommends baselines for painful processes before the rollout. In this article, I describe a three-layer measurement model of adoption signals, time and effort indicators and business outcomes, and explain why role-based deployment makes value measurable.

Power BI Grounding: Copilot Reasons Over Semantic Models

Wednesday, September 9, 2026 · Maximilian Kenfenheuer

Since the August 2026 update, Microsoft 365 Copilot reasons over Power BI reports and semantic models in Chat and Cowork. Copilot thus works with governed business definitions: how revenue is defined, how margin is calculated, which KPIs leadership uses and who may see which numbers. At the same time, a poorly defined KPI now spreads faster. In this article, I explain why semantic models become the foundation for trustworthy AI answers and how organizations can prepare their analytics layer.

From Draft to Deliverable: Agentic Copilot in the Core Apps

Saturday, September 5, 2026 · Maximilian Kenfenheuer

On April 22, Microsoft made agentic capabilities in Word, Excel and PowerPoint generally available and brought business apps into Copilot via agents. AI moves to where proposals are written, financial models built and board decks assembled. At the same time, human judgment gains weight: a polished deck is not automatically a sound strategy, and a well-structured spreadsheet is not automatically a correct model. In this article, I explain what changes when AI participates in the work and which five readiness questions organizations should answer.

Posts tagged #CopilotStudio

Data, Memory, Inference: The Three Layers of Work IQ

Thursday, August 6, 2026 · Maximilian Kenfenheuer

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

An excellent model with thin enterprise context still produces generic output. The advantage lies with the platform that provides context. Work IQ builds this context in three layers: data from Microsoft 365 and business systems, memory about how teams work, and inference for reasoning and action. Microsoft offers it via REST, agent-to-agent and a remote MCP server, on a consumption basis and independent of Copilot licensing. In this article, I explain why this context infrastructure may become more durable than any assistant interface.

Chat, Context, Tools, Workspaces: The Four Domains of the Work IQ APIs

Sunday, July 26, 2026 · Maximilian Kenfenheuer

#Microsoft365Copilot #MicrosoftAI #AI #EnterpriseAI #CopilotStudio

The new Work IQ APIs look like a developer release. In practice, they show how Microsoft wants to scale enterprise agents: with a runtime that supplies context, tools and persistent state inside the Microsoft 365 tenant. The API surface covers four domains: Chat, Context, Tools and Workspaces. Billing runs on Copilot Credits with spending limits for admins. In this article, I walk through the four domains and explain what they mean for developers, partners and IT leaders.

Work IQ: An Intelligence Layer That Understands How Work Gets Done

Sunday, July 26, 2026 · Maximilian Kenfenheuer

#Microsoft365Copilot #MicrosoftAI #CopilotStudio #EnterpriseAI #AITransformation

With the June updates, Microsoft introduced Work IQ as a named intelligence layer for Microsoft 365 and exposes it through APIs. Billing runs via Copilot Credits, without a separate license. Work IQ aims to show agents who is involved in a task, which files and meetings matter and which decisions are still open. Custom agents, partner solutions and Copilot Studio can use the same layer. In this article, I explain why this work-aware layer may become harder to replace than any single model or assistant.