When an SMB first dips its toes into the world of Microsoft Copilot or other AI tools, there’s often a mix of curiosity, optimism, and — let’s be honest — a fair bit of uncertainty.

Before getting underway, partners and customers will have (hopefully) defined the need, objectives, expectations and outcomes sought from the pilot project. Going in with a shared vision of what must be proven from a pilot sets the north star for everything that follows this phase.

The early “wow” moments during a pilot are exciting, but they can also be uneven. A handful of users might see instant gains, while the rest of the business wonders what all the fuss is about. That’s why partners and clients must be aligned on the measures of success for the project.

There are many, many entry points for Copilot in a business, so I admittedly wander across a lot of examples in the video for this episode! The key points are woven up neatly for you to read through, and these highlight where we at IBRS see prime opportunities for partners to win trust when customers are piloting their first AI projects.

Ultimately landing the right AI pilot project for your customer will come down to their needs and your knowledge of their business.

So, let’s get into it!

I presented this matrix at a Crayon Connect partner summit event last year, and it remains pretty well in line with where we see early stage projects for Copilot getting the go-ahead with customers.  I’ve added some additional context around some of these further below.

What’s happening for the business during the pilot phase?

The focus during pilots is on testing and proving AI concepts in a controlled, small-scale environment. The emphasis is on proving that AI can work as intended — delivering a project to agreed scope, on time, on budget, and performing correctly in its defined use case.

At this stage, the business case is still hypothetical — the success criteria are about delivery confidence and technical validity rather than business transformation.

For customers, success in this stage means “it works here, it works now, and it didn’t break anything.”

• AI use is experimental — often confined to a single team, process, or defined problem area.
• The goal is to validate value: does Copilot (or other AI) improve productivity, decision-making, or customer outcomes in a measurable way?
• Partners focus on readiness assessments — data quality, governance gaps, and infrastructure suitability.
• Early training is hands-on and role-specific, building confidence and demonstrating quick wins.
• Feedback from pilot participants is used to refine use cases and identify potential blockers to wider rollout.
• AI performance monitoring is basic, often limited to observing outputs and user feedback rather than formal KPIs.

This stage of the journey is about exploring AI’s potential, managing risk, and generating proof points to build internal support for broader adoption.

Readiness First: Building the AI Launchpad.

Before Copilot can deliver game-changing insights or automation, SMBs need to have their house in order — especially when it comes to data. AI thrives on clean, well-structured, accessible information. 

This is the perfect moment for partners to: 

• Run AI Readiness assessments – Audit data sources, formats, and governance to spot gaps early. 
• Advise on cloud architecture – Help customers right-size their Azure environment so they’re not overpaying for capacity they’ll never use, or worse, under-provisioning. 
• Bridge skills gaps – Offer or broker in expertise for data cleansing, integration, and optimisation. 
• Introduce Microsoft Fabric or other unified data solutions – For some, Fabric is the perfect backbone for Copilot; for others, a lighter data architecture is more suitable. 

Getting this stage right ensures Copilot isn’t just “on” — it’s ready to deliver. 

Choosing the Right AI for the Job. 

Copilot isn’t the only AI in town. Azure offers a variety of AI models, each with strengths for different scenarios. SMBs often need guidance to pick the right tools for their goals. 

Ways you can help: 

• Compare Microsoft Copilot’s capabilities with other Azure AI services to match the right solution to the right outcome. 
• Run small-scale proofs-of-concept to validate model performance. 
• Explore Generative AI Ops and Machine Learning Ops for customers ready to go deeper into refinement and lifecycle management. 

Good model selection upfront avoids costly rework later. 

Business Applications: From Quick Wins to Strategic Impact. 

For SMBs running Dynamics 365, Business Central, or the Power Platform, Copilot can deliver productivity boosts almost immediately — but the real opportunity is in integration and specialisation. 

Partner plays here include: 

• Educate on native AI capabilities – Show customers what’s already available in their business apps. 
• Develop vertical-specific AI Agents – Create reusable solutions for niche industries (e.g., AI-assisted tenant onboarding for real estate CRMs). 
• Audit Business App data – Ensure CRM, ERP, and SharePoint data is structured for AI use. 
• Train users in context – Beyond “how to use Copilot”, focus on prompts, automation flows, and embedding AI into Power Platform workflows. 

This approach ensures AI adoption is sticky, not just a passing trend. 

Governance, Security, and Responsible AI. 

Without guardrails, AI adoption can get messy fast. A pilot is the perfect moment to establish governance and security frameworks. 

You could: 

• Advise on using Microsoft Purview for setting up information governance policies and setting up Privileged Access Management for AI tools, especially those touching sensitive data. 
• Conduct AI Impact Audits – not just on the risks of using AI incorrectly, but also the risks of falling behind competitors who embrace it. 
• Bundle AI readiness into broader Zero Trust or security assessment services. 

Customers that trust their AI environment will be far more willing to expand its use. 

Scaling from Pilot to Organisation-Wide Adoption. 

The leap from a small test group to a company-wide rollout is where many AI projects stall. During the pilot phase, partners can lay the groundwork for this expansion by: 

• Designing engagement strategies to showcase pilot wins and encourage adoption across teams. 
• Automating high-value prompts and embedding them directly into everyday tools. 
• Being ready to offer or facilitate managed AI services that monitor, refine, and expand Copilot usage over time. 
• Seeding customer conversation around integrating AI into continuity, security, and resilience planning so it becomes part of the customers operational DNA. 

When the expansion phase is well-handled, you’re not just delivering a project — you’re securing an ongoing service relationship. 

The Bottom Line: Win the Pilot, Win the Partner Role

For SMBs, a Copilot pilot is rarely just about technology. It’s a test of whether AI can be trusted, useful, and relevant to their business goals. 

For you, it’s a chance to: 

• Show strategic value, not just technical capability. 
• Align technology choices to measurable business outcomes. 
• Create a repeatable service model that grows alongside the customer’s AI maturity. 

Get the pilot right, and you’re not only helping customers take their first steps with AI — you’re positioning yourself as the partner they’ll call for every step after. 

 In the next episode, I’ll move on to considering the primary plays for customers entering the adoption phase with Copilot.  Click on through for more on that topic!