What we’ve learned delivering AI project with Microsoft Copilot

When you deliver AI projects using Microsoft Copilot Studio and Agent Builder, you learn quickly that these aren’t traditional technology implementations.

In most engagements, the actual implementation is maybe 20 percent of the work.

The other 80 percent shows up before and around the build. It’s time spent understanding the client’s environment, their constraints, and their readiness for AI. That work often determines whether an AI solution moves forward smoothly or gets stuck once it meets the realities of an enterprise setting.

Most organizations want AI. Many are already experimenting with Copilot AI or other GenAI tools. But once teams move past demos and proof of concept ideas, gaps start to come to light. Security, governance, identity, and IT foundations aren’t always set up to support AI safely at scale. Those gaps become visible very quickly when real enterprise use cases enter the picture.

Before walking through what this looks like in practice, it helps to clarify how different forms of AI show up inside Microsoft’s ecosystem. Here are some of the key lessons that have emerged from delivering AI projects with Microsoft Copilot.

A lot of teams are already familiar with assistive AI, including M365 Copilot, which helps users summarize content, draft material, or find information when prompted. These experiences fit comfortably within existing controls because the user remains the decision-maker.

Agentic AI introduces a different delivery dynamic. An agent can be given access to systems and data, follow defined logic, and take actions without constant user input. That capability opens up powerful opportunities, but it also brings identity, security, and governance questions to the surface much earlier in the project.

This is often the point where expectations shift. The conversation moves away from what the agent can do and toward whether the organization is ready to support it.

Before building anything, we spend time digging into the client’s environment. That usually includes:

• Their identity setup, including how users, roles, and permissions are managed across systems
• Security boundaries
• What governance exists today (sometimes very little)
• Who owns what across systems
• How comfortable the organization is giving an AI agent access to systems and data

In one enterprise engagement, the team had already completed planning, requirements, and design, and the solution performed as expected in development and testing environments. The challenge only became apparent when the agent moved closer to production. At that stage, Data Loss prevention (DPL) policies blocked several required connectors. From a development standpoint, everything worked as designed, but governance controls were doing exactly what they were meant to do.

The solution didn’t involve changing the agent. Instead, IT provisioned dedicated environments specifically for Copilot development. Environment-specific DLP policies allowed the required connectors without opening them tenant-wide. Once roles were aligned and access was properly scoped, the agent worked as expected.

The key takeaway wasn’t that something failed. It was that governance worked. The experience reinforced the importance of addressing environment strategy and application lifecycle management (ALM) early governance controls don’t become last-minute surprises.

That experience isn’t unusual. Many organizations are still early in their AI adoption journey. Internal teams, especially IT, security, and governance, aren’t always fully aligned yet. A significant portion of delivery work goes into helping organizations get there so AI can operate safely and reliably.

Copilot Studio and M365 Agent Builder are evolving quickly, and the differences between tools aren’t always obvious upfront. Copilot Studio is typically used to design and manage custom agents across enterprise systems, while Agent Builder focuses on creating more tailored, scenario-specific agents within Microsoft 365.

Some of the distinctions we see surprise clients:

• M365 Agent Builder provides out-of-the-box access to Outlook and organizational data
• Copilot Studio doesn’t offer that natively and requires more customization
• Certain scenarios benefit from Azure-based extensions that help agents handle more complex workflows, integrations, and scale
• Features and capabilities change month to month

In one corporate development use case, we started with a SharePoint-based agent. As requirements evolved, it became clear the approach wasn’t the right fit for the dynamic content the business needed. Copilot Studio introduced similar considerations, so the solution shifted to Agent Builder to meet the requirement.

In another example, Agent Builder produced responses that were effective initially but became less reliable as data volumes increased. We transitioned the agent into Copilot Studio, using more controlled tools to filter and manage larger datasets rather than relying on a single broad knowledge source.

Many AI projects perform well during early demos. Proofs of concept are valuable for validating ideas quickly and building momentum, but they can also create expectations that enterprise implementation will move at the same pace. As more complex requirements emerge, performance, reliability, governance, and user experience require stronger design and testing.

This is typically where performance, reliability, and user experience are tested — particularly in regulated workflows such as compliance or mutual fund advisor scenarios.  

In one solution, capabilities that worked during early demonstrations didn’t hold up under real usage. Certain features weren’t available in custom agent, and performance became a concern. Extending the solution with Azure patterns helped stabilize it and make it production-ready, without over-engineering the design.

Azure services such as Logic Apps can help orchestrate workflows by controlling step-by-step execution, managing retries, and handling errors so performance remains stable as data volumes and users demand grows. In more advanced scenarios, introducing a governed tool layer can limit agents to approved actions such as search, validation, or summarization instead of relying on a single large knowledge source. This improves accuracy, traceability, and reliability at enterprise scale

These experiences help create how we set expectations early on:

• What the product can do today
• What it can’t do
• What requires an Azure pro-code extension approach

That clarity reduces rework, frustration, and loss of trust later in the process.

If you want an AI agent to behave consistently, you can’t rely on chance.

Over time, we’ve learned to:

• Teach teams to structure prompts clearly
• Define expected output formats
• Limit scope so the agent stays focused
• Build guardrails that make teams comfortable using it

Prompting alone doesn’t solve everything. A common misconception we hear is that if users can access SharePoint content, the AI agent should automatically retrieve and use it effectively.

In a finance-focused use case, users had the right permissions, but the agent struggled to answer questions accurately. The issue wasn’t access. It was how information was being interpreted and retrieved at scale. We shifted to more structured retrieval methods instead of relying on raw documents, which improved response quality and reliability.

Consistent answers come from a combination of good prompts and well-structured knowledge. Access by itself rarely guarantees strong results.

While teams work through security, governance, and product decisions, delivering something small and useful early makes a difference.

It gives the business something real to react to. Users gain confidence. Stakeholders see value. It also creates space to handle complex IT and governance work in parallel.

In one enterprise project, the original vision was broad and ambitious, involving Excel outputs, automations, and downstream processes. Rather than waiting for every integration and approval, we intentionally started with a focused agent. Its role was simply to answer questions accurately from a large dataset the business already relied on.

That first version didn’t automate everything, but it achieved something more important early on. It built trust in AI. Users saw consistent answers. Stakeholders saw value without needing to change how they worked day to day. While that confidence grew, we worked with IT to unblock triggers and connectors and to begin the right conversations around licensing and what an autonomous, agentic AI approach could look like in later phases.

That early momentum made the larger vision easier to move forward.

Even when starting small, the same level of design discipline still applies. Agents should be developed and governed with the care given to any other software solution. The difference is that agent development introduces a user experience lens that conventional systems don’t always require. Natural language interactions guide how users interpret value. Even when an agent completes its work behind the scenes, the way results are returned, including clarity, tone, and structure, influence trust and adoption.

Across engagements, the approach stays consistent. Here’s how to we build of consistency, while keeping accountable:

• Be honest about what the technology can and can’t support
• Spend time understanding the client’s environment
• Work with IT and security teams, not around them
• Use Azure services when extensions are needed
• Deliver quick wins while planning for scale
• Design agents for consistency through prompting and structure

Once those pieces are aligned, implementation becomes the most straightforward part of the journey.

Promising use cases can slow once governance enters the conversation. Copilot AI might be gaining traction in parts of the organization while IT works through identity and security implications. Strong proof of concept results can start to shift once usage increases and performance or accuracy come under closer scrutiny.

That doesn’t mean the strategy is wrong. It usually means implementation and environment maturity aren’t moving together.

Getting governance and foundational decisions right early is what makes enterprise-scale agentic AI possible later.  

Microsoft Copilot and agentic AI capabilities are evolving quickly, and organizations are building the foundations required to support them responsibly. Real advancement happens when those efforts move in step.

With a clear environment strategy, realistic expectations, and phased delivery that builds trust over time, AI initiatives gain stability and momentum.

If you’re looking to accelerate or streamline your AI journey, reach out to our experts to explore how these lessons can support your next phase.

Sitting between leadership and delivery, Divya is a Solution Architect at MNP, leading AI and agentic solutions across the Microsoft ecosystem including Copilot, Power Platform, and Azure AI. She helps organizations use technology in a way that’s practical, scalable, and actually useful for the business.

Rem is a Digital Advisor on MNP’s Digital Services team in Vancouver. With more than two decades of business process automation experience, Rem helps connect people and processes to ensure effective communication, getting the right information to the right person at the right time on any device.