From Copilot Rollout to AI Workplace: Adoption, Agents & Real Business Value with Christoffer Besler Hansen [MVP]

Microsoft 365 Copilot has moved beyond the question of “What can generative AI do?” The harder challenge is now turning AI into something thousands of employees actually use, trust, and derive measurable business value from. In this episode of M365 FM, Mirko Peters talks with Microsoft MVP Christoffer Besler Hansen, Head of AI Workplace at Atea Group, about what it takes to move from a Copilot rollout to a genuine AI-powered workplace. Drawing on experience supporting AI adoption across more than 8,000 employees, Christoffer shares lessons on Microsoft 365 Copilot adoption, training, agents, Copilot Studio, Microsoft Foundry, governance, security, extensibility, FinOps, and measuring business value.

WHAT IS AN AI WORKPLACE?
An AI workplace is much broader than simply giving employees Microsoft 365 Copilot licenses. At Atea, the AI Workplace team is responsible for how more than 8,000 users incorporate AI into their daily work. Microsoft Copilot is a major component, but the strategy also includes Copilot Studio, agentic AI, Foundry, experimentation with other technologies, and—critically—continuous user adoption. The objective is not to deploy one AI product. It is to change how people work with information, applications, and business processes.

TECHNOLOGY MOVES FAST. PEOPLE NEED TIME.
New AI models and capabilities can appear every week. Human working habits don't change at the same speed. Christoffer explains why organizations need to spend substantial effort helping employees feel comfortable changing established workflows. At Atea, this includes training throughout the year, sometimes every other week, with sessions designed for different audiences such as managers, consultants, salespeople, beginners, and advanced users. AI adoption therefore isn't a launch event. It is an ongoing organizational capability.

BUYING 5,000 COPILOT LICENSES ISN'T A STRATEGY
What should happen after an organization purchases thousands of Microsoft 365 Copilot licenses? According to Christoffer, the organization first needs to determine why it purchased them. What is the objective? What should employees accomplish differently? How will the organization support adoption? How will success be measured? Simply assigning licenses and expecting employees to teach themselves isn't enough. Employees already have jobs to perform and cannot realistically follow every weekly change across rapidly evolving AI products.

WHY EARLY COPILOT ADOPTION OFTEN DROPS
AI naturally generates curiosity. When users initially received Copilot without structured adoption support, Christoffer observed strong engagement for approximately the first four weeks. Employees experimented with the technology. But when they struggled to turn those experiments into new working habits, usage declined. After structured training was introduced, users were more likely to continue using Copilot over time—and began asking for additional training as the products evolved. Initial excitement gets people through the door. Continuous education helps keep them there.

HOW DO YOU MEASURE COPILOT ROI?
One of the hardest enterprise AI questions is determining whether Copilot is actually creating value. Usage alone isn't enough. An employee opening Copilot 50 times doesn't necessarily mean the organization has become more productive. Christoffer argues that organizations need to identify what matters to their particular business and then measure whether AI improves those outcomes. That could include completing work faster, handling more customer cases, improving quality, or increasing business capacity.

MEASURE OUTPUT, NOT JUST AI USAGE
One example discussed in the episode involves an employee who previously handled two cases simultaneously but could use AI to work across six while still receiving better customer feedback. That represents something more meaningful than a Copilot usage statistic. The employee is producing more output while maintaining or improving quality. The right KPI therefore depends on what the organization actually produces. AI metrics should ultimately connect with business metrics.

COPILOT AS A THINKING PARTNER
Meetings were one of the earliest areas where Microsoft 365 Copilot delivered obvious value. Transcription, summaries, and meeting intelligence can reduce administrative effort. But Christoffer highlights another important pattern: using AI as a thinking partner. Instead of asking AI to do all the thinking, start with your own ideas. Speak or dictate those thoughts. Let Copilot structure them. Review the result. Give feedback. Iterate until you have something useful. This approach can save time while simultaneously improving the quality of emails, presentations, documents, and other knowledge work.

RESEARCH AGENT CHANGES KNOWLEDGE WORK
Christoffer highlights Microsoft's Research Agent as one of the particularly valuable additions to Copilot. For large projects involving significant amounts of information, an agent capable of working through numerous sources can dramatically reduce research effort. It can also help users find information they may previously have struggled to discover manually. Importantly, users can review the underlying sources and verify whether the resulting information is correct.

YOUR AI IS ONLY AS GOOD AS YOUR INFORMATION
Enterprise AI quickly exposes existing information-management problems. Organizations need to think about how information is structured across SharePoint, OneDrive, CRM systems, and other repositories. Microsoft Purview can play an important role in identifying and protecting confidential information. Retention policies also matter because outdated documents can lead AI toward outdated answers. Simply giving an agent access to more information doesn't automatically make it better. Sometimes the correct approach is to clean the data before connecting the agent.

WHEN SHOULD YOU BUILD AN AGENT? ㅤ Christoffer recommends encouraging employees to start thinking about potential agent use cases early. Initially, organizations may create many simple agents that primarily retrieve information. Some will provide little long-term value. But experimentation changes how employees think about automation. The next maturity step is building agents that don't merely answer questions but perform actions—sending messages, updating CRM systems, triggering processes, or interacting with other applications. Eventually, organizations can move toward more autonomous agents working alongside employees.

AGENT BUILDER VS COPILOT STUDIO
Not every employee needs to begin with Copilot Studio. Christoffer sees many non-technical employees using Agent Builder directly inside Copilot to create simpler agents. More technical users move toward Copilot Studio when they require additional capabilities. This can create a useful progression: Idea → Simple Agent → Validation → Copilot Studio → Advanced Enterprise Agent An employee can prove the concept without becoming a professional developer, then involve technical specialists when the solution needs to become more sophisticated.

FROM ANSWERS TO ACTIONS
An HR agent answering “How many vacation days do I have?” is useful. An agent that can actually book next Friday as vacation represents a fundamentally different capability. This transition from information retrieval toward actions changes the architecture and security requirements surrounding AI. Christoffer expects users to interact less directly with traditional application interfaces as agents increasingly perform tasks on their behalf. Instead of navigating several administrative screens, users may simply describe the desired outcome to an agent.

AGENT SECURITY BECOMES CRITICAL
Once agents can take actions, organizations need strong guardrails. What can the agent do automatically? What requires explicit approval? What can it delete? Which systems can it access? What permissions does it receive? Christoffer emphasizes least privilege and approval controls, particularly when agents interact with administrative environments. Giving an autonomous agent Global Administrator privileges and allowing it to operate without restrictions would create obvious risks. The more capable agents become, the more important their permission architecture becomes.

ENTERPRISE AGENTS NEED AN INTAKE PROCESS
A personal agent used by one employee is different from an agent deployed to thousands of users. Enterprise-wide agents need quality control. Organizations should review instructions, permissions, connectors, integrations, and data access before allowing large numbers of employees to use them. Christoffer suggests establishing an intake process where employee ideas can be evaluated. Some agents may be returned to their creators for improvement. Strategically important ideas can instead be developed by a dedicated internal agent team.

COPILOT EXTENSIBILITY AND AGENT 365
The conversation also explores Copilot extensibility and the Agent 365 SDK. Christoffer describes experimenting with personal agents that have their own identities in Microsoft Entra. Such an agent could appear within an organizational structure, have its own email and Teams presence, and receive carefully controlled permissions. His example involves building an agent that can act as a personal assistant and potentially answer appropriate questions when he is away from work. The important architectural shift is that the agent begins looking less like a chatbot and more like another identity participating in the organization.

Become a supporter of this podcast: https://www.spreaker.com/podcast/m365-fm-modern-work-security-and-productivity-with-microsoft-365--6704921/support.