When it comes to running a business, getting paid on time is critical.
Our Global Collection team in the Microsoft Treasury division makes sure payments are seamlessly executed in our fast-moving global enterprise environment. However, our case managers were often losing valuable time figuring out things like who the right contact was for a given customer, which issues were likely to be challenged by a customer, and where an exception should be routed next. This information was spread across systems or buried in handoffs.
To solve these challenges, our team built a human-led, AI agent-assisted support system to reduce preparation time and streamline their processes.
“Building the AI assistance wasn’t the hard part,” says Kathy Brustad, a director in the Global Treasury and Financial Services division at Microsoft. “The hard part was reimagining the collection experience with AI front and center, and bringing the underlying infrastructure up to speed to get it there.”
In this post, we explain how we did it so you can learn from our experience.
“We have over 1,000 collectors around the world who perform collections for Microsoft. They had multiple systems they had to go to in order to find out things like the totality of the customer’s invoice and what conversations a different team had with the customer. The information was fragmented.”
Kathy Brustad, director, Global Treasury and Financial Services
Stitching together information across systems
Our AI agent is focused on helping our case managers prioritize high-value work by:
- Predicting late payments and possible customer disputes
- Summarizing customer case interactions for use by case managers
- Routing customer emails to the right collections manager faster and with greater precision Automatically matching payments to invoices
- Automatically responding to customer inquiries
“We have over 1,000 collectors around the world who perform collections for Microsoft,” Brustad says. “They had multiple systems they had to go to in order to find out things like the totality of the customer’s invoice and what conversations a different team had with the customer. All of this information was fragmented. We didn’t have a single view of how much a customer owed us.”
We started by consolidating these dispersed tools and systems into an SAP and Microsoft Dynamics 365 environment, creating a single source of truth for all relevant customer, invoice, and payment data.
On that foundation, we layered on Microsoft’s IQ intelligence platform to infuse semantic understanding and business context. That standardized our workflows by simplifying templates and worklists to reduce complexity and put consistent global practices into place. Routine communications became fully automated.
We then applied AI to improve payment matching accuracy from 40% to 90%, generate customer response drafts, and intelligently route cases to reduce time-consuming back‑and‑forth.
Copilot assistance was embedded directly into the daily workflow of our case managers to reduce administrative load by providing inline knowledge suggestions, summarizing calls, and automatically drafting replies. With these standardized automated workflows, we could apply 98% of payments within 48 hours.
“In a nutshell, this is the collection story: We have various agents and models deployed to assist our human agents with all the activities they have to do, saving hundreds of thousands of hours that we spent on manually tracking things before.”
Kathy Brustad, director, Global Treasury and Financial Services
Moving faster on ‘act ready’ work
Deploying the agent was only the starting point. The harder work was helping our collection team change established ways of working. Brustad described the shift as learning to “run it in a different way,” moving from manual, fragmented preparation toward workflows where prioritization, context gathering, and routing were increasingly supported within the system.
To make that shift possible, the team introduced a change management work stream program and role-based training focused on real, day-to-day scenarios alongside the rollout. By anchoring the work in clear business pain points and showing tangible improvements, our team saw how the new approach made their work easier. Each morning, the agent prioritized each case manager’s workload according to urgency and past client behavior so case managers could immediately focus on the accounts that were the most pressing.
We reduced repetitive communications using automatically drafted responses and automated statements.
“In a nutshell, this is the collection story: We have various agents and models deployed to assist our human agents with all the activities they have to do, saving hundreds of thousands of hours that we spent on manually tracking things before,” Brustad says.
After deploying this system to our case managers, we saw measurable improvements in both productivity and speed, including:
- Hundreds of thousands of hours unlocked annually in order to do more human-led high-value work rather than routine administrative tasks
- 40% reduction in call preparation time
- 2X growth in automatic cash applications
- 2.5X acceleration of customer inquiry resolution time
Operationally, the team also saw up to 60% reduction in inquiry handling time through inline suggestions, summarized calls, and automatically drafted replies. To ensure these improvements were real and repeatable, we emphasized observability in our evaluation approach. Our team tracked dollars collected through collections and hours worked to create productivity metrics.
Data, trust, and good governance
When introducing AI systems or agents into finance workflows, leaders often ask two questions:
- Can we trust the outputs?
- Can we govern the process?
“The biggest takeaway is to know your own process very, very well. You need to understand where all the bottlenecks and pain points are. Start from there to design the new agent-enabled process instead of saying, ‘I’m going to just inject the agent into my existing process.’”
Kathy Brustad, director, Global Treasury and Financial Services
For us, trust came from getting the basics right in the form of right-sizing our enterprise data, standardizing our workflows, and establishing clear ownership for each part of the work. When we tested early and included frontline users throughout the process, outcomes improved.
“The biggest takeaway is to know your own process very, very well,” Brustad says. “You need to understand where all the bottlenecks and pain points are. Start from there to design the new agent-enabled process instead of saying, ‘I’m going to just inject the agent into my existing process.’”
Embed custom agent assistance directly into the moments where time disappears, such as prioritization, preparation, routing, and drafting so adoption feels natural and can be measured. You can prove impact with a small set of metrics like cycle time, throughput, dollars collected, and hours saved, and iterate from there.
Key takeaways
Modernizing collections is about fixing the fundamentals first, before you add AI into the mix. As you begin to streamline your own finance workflows, keep these lessons in mind:
- Fix fragmented workflows before adding intelligence: AI delivers the most value when it’s layered on top of standardized processes and a unified data foundation rather than disconnected systems and ad hoc handoffs.
- Embed assistance where time is actually lost: Copilot-style support works best when it shows up directly in prioritization, preparation, routing, and drafting to reduce friction without changing how people work.
- Focus AI on high‑ROI decisions, not just automation: Predicting late payments, flagging likely invoice disputes, and surfacing context can help teams spend time where it matters.
- Design around the practitioner’s day: When work arrives prioritized and prepped, case managers spend less time chasing context and more time resolving exceptions.
- Measure what matters to prove impact: Cycle time, dollars collected, throughput, and hours saved provide a clear, repeatable way to track productivity gains and cashflow velocity.
- Pair generative AI with strong governance: Trust comes from clear ownership, standardized workflows, quality data, and ongoing human oversight.
Editor’s notes:
- SAP is an enterprise finance system that many organizations use to manage invoices, payments, and financial records in a single, centralized platform.
- All metrics cited are based on Microsoft internal data gathered during the writing of this article. They’re best read as directional signals from that period, and they may change as systems, processes, and behaviors evolve. Microsoft makes no warranties, express, implied, or statutory.
Try it out
Related links
- Discover the five lessons we learned on how to make AI stick for sellers during our Copilot rollout.
- See how we revamped and modernized our Microsoft Treasury infrastructure using Azure.
- Explore how we’re supercharging our support experience with Microsoft Dynamics 365 and AI.
- Learn how we’re transforming the marketing function at Microsoft with AI.
- Read about our role as Customer Zero in an AI-powered world.
- Check out how we’re measuring the impact of Microsoft 365 Copilot and AI internally.
