Agent MIA (Microsoft Incentive Agent) is an AI agent created by Fusion5 using Microsoft Copilot Studio. It was designed to simplify Microsoft’s complex incentive programs by acting as a digital employee, answering repetitive queries, and guiding teams through eligibility and process steps.
Fusion5 helps clients explore the potential of AI in their businesses. But we believe credibility starts at home. If we are going to advise clients on AI adoption, we need to be “customer zero,” testing, learning, and living the change ourselves.
There is no shortage of AI hype. What is harder to find is evidence. That is why stories like Agent MIA matter. They show how AI can move from idea to impact, not in one step, but through the deliberate phases that turn potential into long-term value. They also show how digital employees can take their place alongside people, adding capacity and proving their value in an AI-enabled workforce.
Phase 1 – The challenge
Microsoft’s incentive programs are a powerful lever for partners like Fusion5. They shape how we engage with customers, unlock funding for pilots, and strengthen financial outcomes for both clients and Fusion5. But they also come wrapped in complexity. Each fiscal year brings hundreds of pages of updates and rules.
“The problem we were solving for was having an easier way to understand the commercial incentives that roll out every fiscal year,” says Eisa Quelette, Microsoft Alliance Manager.
One of the incentive books alone is 180 pages. For our client-facing teams, it is simply too complex to fully understand and extract value from.”
The result? Opportunities were missed, customers sometimes lost out on discounts or co-funding, and valuable time was consumed fielding constant questions. Much of the knowledge sat with a small group of experts and in practice almost every query about incentives ended up with our Microsoft Alliance Operations team. That created a bottleneck that was not scalable.
Phase 2 – Proof of concept
The opportunity was clear: build an AI agent to improve access, understanding, and use of Microsoft’s numerous incentives so customers, Fusion5, and our client-facing teams could all benefit.
Agent MIA (Microsoft Incentive Agent) was created and trained on Microsoft’s incentive guides and Fusion5’s designations, with the initial goal of letting users ask natural language questions and receive clear, accurate, and usable answers.
The POC proved the concept was sound. Instead of searching 180 pages, client facing teams could ask, “What funding applies to this ERP deal?” and get an instant reply. It showed the potential of agents to scale knowledge and unlock value.
But the POC also highlighted some gaps. Without built-in checks on eligibility and process, some of MIA’s answers were incomplete. In practice, that meant users would get an answer, but not always a clear next step or point of consideration. The agent needed to do more than read back information. It had to guide people forward with accuracy and confidence.
Phase 3 – Pilot and testing
Rather than rushing a rollout, Fusion5 chose to go deeper with testing and released MIA to a pilot group. This allowed the team to capture feedback, scenario test, and refine the agent before scaling.
Even in this early phase, the pilot has already pointed to where value can be realised:
- Unlocking funding – surfacing incentives that might otherwise be overlooked, with potential to unlock significant funding as the agent matures.
- Accelerating the pipeline – showing how deals could be unblocked and progressed faster.
- Boosting credibility – giving client-facing teams clearer insights to demonstrate Fusion5’s depth of knowledge in Microsoft’s ecosystem.
- Driving collaboration – highlighting opportunities for cross-pillar engagement across products and locations.
- Unlocking innovation – pointing Fusion5 towards Microsoft’s emerging technology investments and new areas of capability.
MIA is also changing how work gets done. Instead of defaulting every query to the Alliance Operations Team, people can now use MIA to get the information they need and understand how to apply it. As a digital team member, MIA is starting to handle many of the repetitive queries and starting to create capacity for the team to focus on higher-value activity.
Tanya Barrie, Microsoft Alliance Operations Team Lead, is already seeing the difference:
As MIA’s capability develops, we’re starting to feel the impact in the time saved from repeatable responses. That motivates us to keep developing her further, because we can clearly see the path to long-term value.”
One early pilot user, Shannon Moir, Director of AI, shared his experience:
I’ve used MIA several times. I take work orders I’ve drafted for customers and ask MIA what funding options could be appropriate. That’s been exceptional. And when I’m meeting customers, I like to know what’s possible in terms of funding and specialisations. I’m still relatively new to the Microsoft ecosystem, so MIA is an accelerant, helping me understand certifications, funding opportunities, and where we can bring value”
Shannon Moir | Director of AI, Fusion5
Phase 4 – Continual development and next steps
With early evidence of value in hand, the next phase is refinement. The focus is on building MIA into more than an information recall tool. The agent needs to:
- Check eligibility and thresholds
- Identify owners and next steps
- Provide supporting assets, such as one-pagers ready to share with customers
- Build trust by being consistently accurate
“We’re still at the early phases,” says Eisa.
It’s not about one agent doing everything. We want to keep agents defined and then layer them. Over time, as confidence grows, we’ll integrate MIA into our Dynamics CRM and automate further.”
The aim is to move deliberately through each phase, building from a promising proof of concept to a reliable, scalable capability.
What we’ve learned
Every organisation experimenting with AI agents faces the same truth: it is not set and forget. The real dividends come in the middle phases, when you refine, retrain, and embed governance.
Some of our lessons so far:
- Phase it, don’t rush. Going wide too soon risks credibility. Controlled rollout builds trust.
- Trust is everything. If people don’t believe the answer, they won’t use the agent again.
- Treat agents like digital employees. They need training, feedback, and governance to handle repeatable work and deliver lasting value.
- Evidence beats excitement. The “wow” moment of first use is important, but lasting value comes from discipline and iteration.
Why it matters
For Fusion5, Agent MIA is more than a tool. It is evidence of how we approach AI adoption: starting with a real problem, testing with purpose, and building value step by step. It is also showing us what it means to blend human and digital capability inside a team, where digital employees take on repeatable work and people focus on the judgement, nuance, and higher-value activity.
For clients, that evidence matters. It shows that agents can deliver measurable value when built with discipline, and that Fusion5 has the experience to help others do the same.
The road ahead
The next steps for MIA are clear: wider rollout across Fusion5, deeper integration into CRM, and ongoing refinement. Each phase builds confidence and capability.
By telling the story openly, Fusion5 is showing what it takes to move from idea to impact. AI agents are not magic. They are built, trained, refined, and governed into existence.
Agent MIA is the latest proof that Fusion5 doesn’t just talk about AI transformation. We live it, and we share what we learn along the way.
Fusion5 is helping organisations across Australia and New Zealand turn the promise of AI into practical business value. This case study shows how we deployed Agent MIA with Microsoft Copilot Studio, an example of agentic AI in action. By treating AI agents as digital employees, Fusion5 demonstrates how AI adoption in business can unlock capacity, accelerate processes, and deliver scalable impact. If you are exploring Microsoft Copilot Studio, AI agent deployment, or the role of AI agents in transforming work, talk to Fusion5 about where to start.
Q&A
What is Agent MIA and how was it built?
How does this case study show the value of agentic AI?
This AI agents case study highlights how agentic AI can move from proof of concept to real business value. Agent MIA demonstrates how digital employees can reduce manual workload, free people for higher-value tasks, and create scalable, repeatable impact across the business.
Why use Microsoft Copilot Studio for AI agent deployment?
Microsoft Copilot Studio provides a secure and flexible platform for designing and deploying AI agents inside business workflows. For Fusion5, it enabled the rapid development of Agent MIA, giving client-facing teams easier access to Microsoft incentive information and improving efficiency.
What has Fusion5 learned about AI adoption in business from Agent MIA?
One of the biggest lessons is that AI adoption in business is not “set and forget.” To deliver value, AI agents need refinement, governance, and feedback, just like a new team member. This disciplined approach builds trust and ensures long-term productivity gains.
How can other organisations start their own AI agent deployment?
Organisations should begin by identifying a specific business challenge where repeatable queries or processes consume valuable time. From there, an AI agent can be designed in Copilot Studio to act as a digital employee, handle the repetitive work, and create capacity for people to focus on more complex tasks.
