From the field: How agentic AI is reshaping adoption at Microsoft

Two years ago, AI adoption at Microsoft felt kind of like pushing a boulder up a hill. We had the technology, the licenses, and the leadership mandate, but when it came to cultural embrace of this new era, we didn’t yet have the momentum to get that rock to the top.

In those early days, training sessions, nudges, champions, and outreach campaigns kept us learning and growing through experimentation—but it was difficult to build momentum.

Today, the situation is fundamentally different. With the growing acceptance of agentic AI, adoption seems less like pushing a boulder and more like tending a garden where a million flowers bloom. It’s something we cultivate rather than force, planting seeds that continue to grow as our people discover new possibilities for themselves and their teams.

How did that shift happen? A great example comes from the experience of our field teams in Microsoft Europe South.

As they discovered, the challenge of enterprise AI has definitely changed. Adoption today is no longer the primary obstacle. As agents become easier to build and use, the harder questions are shifting towards governance, trust, and helping teams navigate a safe path from experimentation to production.

Early challenges with AI adoption: The prompt gap 

The generative AI era has taught us discipline. Rolling out Microsoft 365 Copilot at scale across a company of more than 200,000 employees was never really about the model. It was about the unglamorous foundations underneath it: clean data, clear governance, and relentless change management.

Where those foundations were solid, Copilot delivered. Where they were thin, no amount of enthusiasm could compensate.

There was a quieter challenge in those early days that we rarely named: The prompt gap. Copilot worked brilliantly if you knew what to ask it, but most people did not know exactly what—or how—to ask. Successful adoption depended on teaching tens of thousands of our Microsoft colleagues to clearly phrase their intent, and every gain faded the moment their attention moved elsewhere.

It was akin to the frustrating experience of pushing a boulder up a hill only to watch it roll back down, over and over. The value of AI was real, but progress was slow.

How the physics changed

Agentic AI changed the physics. Now, an agent doesn’t need to be prompted at every step. It carries context, can take multi-step actions, and returns a result instead of a suggestion. The interaction shifted from prompting to delegating, from “help me draft this” to “handle this task for me.”

That change rewired the incentives for AI adoption. People no longer had to translate every task into the perfect prompt. Instead, they could delegate meaningful work to agentic AI and stay in the loop as it progressed. The value became easier to see, because it showed up in completed work, not just generated content.

When we introduced Copilot agents such as Cowork and Scout into our everyday work, we stopped having to repeatedly sell the idea. People no longer needed a campaign to understand the value of a digital colleague that could do the work; they just needed one good proof point from someone they trusted.

What surprised us most was how the spread of Cowork and Scout happened: Not top-down through a rollout plan, but sideways, desk to desk and team to team. Someone would use these agents to handle a recurring task, show how it worked in a meeting, and within a week three more colleagues had their own ideas for agentic workflows. The change management challenge had switched from driving adoption to creating the conditions for it—providing fertile soil, as it were.

Organic adoption: Cultivating the garden

The organic spread of agentic AI was not an accident; it was a deliberate change in how we worked with our employees. In Microsoft Europe South we tested this change most fully with local teams, such as Customer and Partner Solutions Spain.

In a pilot program for this group, we didn’t launch with an AI training initiative. Instead, we started with a pattern we had observed from hackathons and previous community sessions.

Previously, participants were enthusiastic about agentic AI, but often lacked meaningful end-to-end business problems to solve. Many of the strongest ideas overlapped with solutions already being built elsewhere, while other scenarios could be addressed through existing Copilot capabilities. Often, the biggest barrier was the perceived complexity of making sure an agent met security, privacy, accessibility, and responsible AI requirements.

So, we changed our adoption efforts, focusing more on helping employees examine their own processes and identify workflows they could automate, and then assisting them in navigating through the compliance issues.  We were asking people to challenge everything they did at work—even the routines they had never thought to question—and identify the most repetitive, manual, decision-heavy tasks that quietly consumed their week.

Across a series of ideation workshops, teams generated a wealth of agentic AI opportunities. Yet the true success of the program was not measured by the number of ideas produced, but by the rigor applied to each one. Every idea was scrutinized not only for its potential value and feasibility, but also based on a more fundamental question: Does this problem truly require an agent, or is there a simpler path to solving it?

As a result, our team introduced a basic but powerful principle: Build, Reuse, or Prompt.

Rather than assuming every problem required a new agent, opportunities were explicitly routed to the most appropriate path. Many ideas were already covered by existing solutions. Others could be solved through one or more of the following:

• Better prompts
• Copilot capabilities
• Existing agents like Cowork, Scout, or even GitHub Copilot

Only a subset of challenges justified building a new agent. This reduced duplication, clarified ownership, and ensured that effort was invested where genuinely new value could be created.

The assessment process itself became part of the adoption journey. Ideas were evaluated against existing agent usage, technical feasibility, role readiness, value measurement, and data and security considerations. We also applied an important principle borrowed from continuous improvement: Sometimes a process should be improved before it’s automated.

The shift from just “knowing how to use AI” to “thinking differently about work” was the real outcome.

By the end, a number of colleagues had moved from passive consumers of AI to active builders. The strongest agent ideas rarely came from technical teams. Instead, they came from the people closest to the work. Once someone questioned a process, identified friction, and built a solution to reduce or remove it, they rarely stopped at one idea.

Our pilot program also reinforced another lesson: AI adoption is not only about learning technology. It’s about seeing how work can change. The next measures of success are whether those agents reduce friction, shorten cycle times, improve follow-through, and free people to focus on higher-value activities.

Trust, orchestration, and tending to what grows

The tough questions we face today regarding AI adoption are about trust, orchestration, and control. Will our people feel comfortable with an agent acting on their behalf? How do dozens of independently built agents coordinate across an organization, instead of colliding? And how do we govern software that actually does the work?

To help answer these questions, agent governance became our center of gravity. We learned to build these considerations into our process, beginning with our first interactions with field teams, and not just bolt it on at the end.

“The technology opens extraordinary possibilities, but the greatest impact comes when it is combined with the right methodology, governance, collaboration, and continuous learning. Transforming ourselves in this way is helping us become a more effective organization while also making us better equipped to guide our customers through their own AI transformation.”

Doris Gomes, customer success lead, Microsoft Spain

At Microsoft, every agent has to tread the same garden path before it can be published through approved channels:

• Service Tree registration
• Security Development Lifecycle and Secure Future Initiative validation
• Privacy assessments
• Accessibility checks
• Responsible AI review

Framed early, these stopped being obstacles and became part of what it means to build well.

We also learned that agentic AI adoption accelerates when publishing feels achievable. Making the governance process visible early helped teams understand the path to production and reduced the uncertainty that often discourages experimentation.

Governing what grows also demands visibility, and this is where Agent 365 has become our control plane. It lets us monitor every agent across all environments in the Microsoft tenant, making sure the ones appearing at the edge are discoverable, secured, and accountable rather than turning into invisible sprawl.

The principle is simple: Let ideas grow freely, but never lose sight of what your garden contains.

“The technology opens extraordinary possibilities, but the greatest impact comes when it is combined with the right methodology, governance, collaboration, and continuous learning,” says Doris Gomes, a customer success lead for Microsoft Spain. “Transforming ourselves in this way is helping us become a more effective organization while also making us better equipped to guide our customers through their own AI transformation.”

Growing the agentic future

The organizations that succeed in the next phase of AI adoption will not be the organizations that push the hardest. Success will be achieved by those that cultivate with care: helping employees rethink their work, creating the conditions for experimentation, and ensuring that what grows can scale safely.

“Agentic AI is helping our teams move from working harder to working smarter. We are simplifying processes, improving operational quality, better leveraging information and insights, and creating more scalable ways to serve our customers.”

Paco Salcedo, general manager, Microsoft Spain

This is the difference between pushing a boulder and planting a seed. A boulder needs continuous force. A seed needs the right conditions—good soil, a little light, a healthy environment. The plant then grows on its own and reseeds.

We are still early on the path to the Frontier Firm future, and there are many ways for us to keep growing and improving. Trust has to be earned, governance has to keep pace with autonomy, and not every seed takes. But with the right nurturing, a brighter agentic future can flower.

“Agentic AI is helping our teams move from working harder to working smarter,” says Paco Salcedo, a general manager for Microsoft Spain. “We are simplifying processes, improving operational quality, better leveraging information and insights, and creating more scalable ways to serve our customers.”

Salcedo points to innovations such as MSX-IQ, an AI assistant built by our team for Microsoft sellers that provides a “chat with your pipeline” experience and is now used across multiple countries. Its adoption demonstrates how giving people the right tools, governance, and support can accelerate meaningful business transformation.