Governing the rise of Copilot and Agentic AI - Part one
Part one of a series where we zoom in on governing the Microsoft Copilot & Copilot Studio platforms
It often starts with excitement. A new AI feature drops, someone in the business discovers it, and within days there are pilots running across different teams. The pace feels exhilarating: everyone wants to experiment, everyone wants to claim “we are doing AI.” Yet when the initial energy fades, the same question comes back to haunt IT and leadership: who is in control here?
In our recent conversations, this pattern surfaced again and again. Organizations roll out Microsoft Copilot or Copilot Studio with the best intentions, but without a clear governance model. Business units build agents, data moves between systems, and suddenly leadership realizes they have no oversight of what is running, who owns it, or how it is maintained. The story isn’t about technology failing. It’s about the absence of governance.
Governance: the missing piece
Most companies we work with are still missing a basic, organization-wide AI strategy. At best, they run proof-of-concepts and small team pilots. At worst, they have dozens of parallel efforts without visibility or control.
When Microsoft released its “CIO playbook” three weeks ago, it included many familiar slides: exponential growth curves, billions of agents by 2028, and bold promises. Underneath that marketing layer, however, one message stood out: AI governance is inseparable from AI adoption.
That point matches what we see in practice. Whether it’s Copilot in Microsoft 365, custom GPT-like solutions, or agents built in Copilot Studio: without governance, adoption collapses into chaos. Teams lack guardrails, duplication spreads, and leaders cannot answer the simplest questions:
Which agents exist today?
Who built and owns them?
What data do they access?
How are they maintained and retired?
Lessons from Citizen Development
We’ve seen this movie before. When the Power Platform took off, organizations rushed to embrace “citizen development.” The promise was that business users could build apps and flows without IT involvement. In practice, only a small group of semi-technical employees ever built solutions. And without governance, many of those apps turned into orphaned tools nobody could maintain.
AI agents are following the same trajectory. It is easy to create something. It is much harder to manage it responsibly. Few companies have a mature citizen development framework, let alone the lifecycle management needed for AI agents.
Why governance is so hard
Part of the difficulty is cultural. AI is still wrapped in hype and fear. Some leaders worry about security breaches. Others are swept away by visions of thousands of autonomous agents transforming their business overnight. Both reactions create extremes: blanket bans or unregulated free-for-alls.
The real work is in the middle. Governance requires boring but vital practices:
Data loss prevention rules that actually work.
Lifecycle models for agents (what is personal, team-level, or enterprise-wide?).
Clear accountability: who is responsible if an AI agent makes a wrong decision?
This is not glamorous, but it is what separates sustainable adoption from a graveyard of failed pilots.
A practical way forward
From our experience, organizations that make progress with AI governance share three habits:
1. Start small, but design for scale.
Don’t begin with critical processes. Pick a manageable use case, validate value, and document the lifecycle. Treat it as a template for the next ten use cases.
2. Anchor governance in existing structures.
Most organizations already have processes for security, compliance, and application lifecycle. Extend those to AI, instead of inventing parallel structures. For example, use existing CI/CD pipelines to manage agent updates, or apply the same access review processes you already use for sensitive apps.
3. Involve leadership early.
AI cannot be governed from the bottom up. Executive sponsorship is needed to make trade-offs visible: when is “fun experimentation” allowed, and when must something move under enterprise controls? Without leadership alignment, governance becomes a paper exercise.
The risk of over-control
There is also a trap on the other side: over-governance. We’ve seen clients implement such strict restrictions that experimentation becomes impossible. Every new idea requires months of approval, which kills the energy AI needs to thrive.
The better path is proportional control. Not every agent needs enterprise-grade lifecycle management. An individual agent helping a sales rep with email summaries is not the same as an enterprise-wide claims processing agent. Governance should match risk, not strangle innovation.
The accountability question
One discussion we keep returning to is accountability. Imagine an AI agent handling service requests. A customer reports a broken device, the agent misclassifies it, and the issue is never resolved. Who is responsible? The developer who built the agent? The IT team who approved it? The business owner who sponsored it?
This question will define the next phase of AI governance. Just as in earlier eras IT learned to assign owners to applications, organizations will need clear models for AI accountability. Until then, human oversight is non-negotiable.
Looking ahead
Governance is rarely the most exciting topic. But if AI is to move from hype to habit, it is the one that matters most. The organizations that succeed will not be those that build the flashiest demos. They will be the ones that quietly, consistently put the right guardrails in place.
Our takeaway from the past months is simple: governance cannot be an afterthought. If your company is experimenting with Copilot, Copilot Studio, or any form of agentic AI, now is the time to define boundaries. Decide what can be tried freely, what requires oversight, and how success is measured.
The tools will evolve rapidly. The hype will rise and fall. What remains is the need for trust, accountability, and control. Governance is not the enemy of AI adoption. It is the only way to make it stick.
Key takeaway: Don’t wait until AI experiments sprawl out of control. Build governance into your AI journey from day one: start small, scale responsibly, and make sure someone is accountable for every agent you unleash.
Start by reading Microsoft’s CIO Playbook yourself via: https://marketingassets.microsoft.com/gdc/gdcZgoIOq/original
⏭️ Next up: our article about when governance and automation meet: scripting sanity into service plan sprawl. You will see this one coming online shortly!