Artificial intelligence has become a major topic in the accounting profession, and for good reason. Firm owners are asking practical questions: What will AI actually do for us? How will it support the work we do each day? How do we choose tools that genuinely help instead of creating new oversight tasks? And most importantly, how do we continue supporting clients accurately, efficiently, and consistently as expectations keep rising?
AI can absolutely help with these challenges, but only when it is built intentionally. Accounting work depends on accuracy, structure, reliability, and trust. Any AI that enters this space has to respect those realities, not gloss over them.
That is why I wanted to learn more about BILL’s approach. Their platform sits at the center of accounts payable, accounts receivable, vendor payments, expense management, and document workflows for hundreds of thousands of businesses. They also have nearly two decades of structured activity behind those systems. If any company is positioned to take a data-driven, responsible approach to AI, it is BILL.
I sat down with Ariege Misherghi, SVP and GM of Accounts Payable, Accounts Receivable, and the Accountant Channel at BILL, to understand how they think about AI agents. We focused on two agents recently announced, the W9 Agent and the Reconciliation Agent, but the real insight came from understanding the philosophy behind the tools.
What follows is my perspective on that conversation and why I believe BILL’s approach matters for firms that want to adopt AI thoughtfully, not reactively.
AI should support teams, not overwhelm them
Many AI products in the market today focus on speed, auto-categorizing transactions, summarizing data, or reducing clicks. Speed has value, but speed alone is not a strategy. It does not fix process gaps. It does not ensure team alignment. And it does not guarantee accurate or timely information.
During my conversation with Ariege, it was clear that BILL is not trying to simply accelerate individual tasks. Their focus is on designing AI that fits into real accounting workflows. That means recognizing what leadership teams expect, what clients need, and where missing information impacts reporting.
AI should clarify the work, not complicate it. And for that to happen, the data underneath the system has to be solid.
Data quality is the foundation for accuracy
Everything we do in accounting depends on accurate information. When firms report incomplete or inaccurate data to a client’s leadership team, the consequences are immediate. Planning becomes harder. Decisions slow down. Trust can erode.
This is why the data behind AI systems matters. AI is only as good as the history it learns from, and most tools in the market are learning from messy, inconsistent inputs.
BILL’s advantage here is not that they “do AI.” It’s that they already sit on a long trail of real, structured activity. Nineteen years of processing work the same way, at scale, adds up. Their platform has seen over $1 trillion in transactions and more than a billion documents flow through standardized vendor onboarding, payments, tax validation, and approvals. That volume matters because it is repetitive, validated, and tied to outcomes, not just raw files sitting in a drive somewhere. In plain terms, the system has had a lot of chances to learn what “right” looks like in real accounting workflows.
Vendor onboarding follows a consistent process. Payment behavior is recorded in a standardized way. Tax information is validated through clear steps. Document workflows generate reliable audit trails. Over time, all of this creates a deep, clean historical dataset, something most AI tools simply do not have access to.
That foundation enables one of the most important concepts in BILL’s AI strategy: the Golden Data Set.
A Golden Data Set, explained clearly
A Golden Data Set is a collection of historically validated outcomes. These are not hypothetical examples or simulated scenarios. They come directly from years of real financial activity on the BILL platform: payments that cleared, vendor records that have been validated multiple times, invoices that were approved correctly, and patterns of business behavior that repeat consistently.
Here’s why this matters.
First, it gives the AI a reference point rooted in truth. When the agent processes a task, it compares its output against what “correct” has looked like across millions of transactions. Second, it strengthens risk management. When an agent produces something that falls outside established patterns, the system can flag it.
This mirrors the way accountants already work. We compare new information against our experience and historical context. BILL’s agents are being trained to use that same logic.
Safety, risk, and confidence deserve equal attention
Throughout my conversation with Ariege, one theme kept coming up: firms need automation, but they also need safety and confidence. AI should not introduce new risk into financial workflows.
Because BILL operates its own payment rails, it must comply with and meet regulatory expectations that shape how its agents behave. They use risk-based gating, meaning actions involving money movement, vendor identity, or tax data require higher confidence thresholds. If something looks unusual, the system can pause and escalate before moving forward.
Every action taken by an agent is logged. Every decision is traceable. And importantly, these agents are opt-in, so firms retain control over when and where they are used.
This structure makes the system more dependable in daily accounting work and gives firms more confidence as they adopt AI.
Why fraud protection matters in BILL’s approach
Fraud is one of the biggest risks our clients face, and catching issues earlier makes a meaningful difference.
BILL cannot disclose every detail of its fraud systems for security reasons, but it is building models to identify suspicious activity early, before money leaves a client’s account.
They shared the following statement about their fraud strategy:
“BILL’s AI-driven fraud solutions leverage extensive payments data, along with AI modeling and automation expertise, to detect and stop fraud in real time. The systems continuously monitor digital payment and account activity, adapting to emerging threat patterns. In FY25, BILL’s predictive AI solutions helped stop over 8 million fraudulent attempts.”
Clients and firms want clarity and confidence, especially when it comes to anything involving money. Systems that surface risks early help both advisors and clients stay protected.
AI in action: The W9 Agent
The W9 Agent is a practical example of how AI can reduce administrative burden without sacrificing accuracy.
Instead of chasing vendors for forms, manually validating tax information, and tracking the process across emails and spreadsheets, the agent handles the entire loop. It reaches out to vendors, collects the form, validates the TIN, confirms completeness, and stores everything with an audit trail.
BILL has reported that this removes more than 80 percent of the manual work firms typically handle during W9 season.
This is a clear example of AI solving a real pain point while maintaining the accuracy firms expect.
AI in action: The Reconciliation Agent
The Reconciliation Agent helps with two of accounting’s most repetitive tasks: coding transactions and matching receipts. These tasks consume significant staff time each month across all clients.
BILL has shared early results showing accuracy rates above 90 percent and a meaningful reduction in transactions requiring manual review. This does not replace professionals; it frees them to focus on exceptions, analysis, and higher-value conversations with clients.
Both the W9 and Reconciliation agents reflect the same philosophy: use strong data, reinforce safety, and support the capacity of accounting teams.
Why rollout discipline matters
One part of my conversation with Ariege that stood out was how BILL approaches feature rollout. AI features do not simply appear across every customer account. Each agent undergoes staged testing: internal testing, a small opt-in alpha group, a broader beta group, and eventually general availability, only after performance benchmarks are met.
This is important because accounting workflows depend on stability. A poorly timed update or unexpected behavior can disrupt a close cycle or create avoidable client issues. BILL’s method gives firms the clarity and control they need as they adopt new tools.
Final thoughts
The pressure firms feel to support clients more accurately, efficiently, and quickly is real and growing. AI can help meet those expectations if it is built thoughtfully, with strong data, clear safeguards, and a real understanding of how accounting teams work.
BILL’s approach reflects those priorities. Their strategy is not perfect, and no AI solution is. But the focus on data quality, safety, fraud protection, and responsible rollout aligns with what firms tell me they need to feel confident adopting AI.
When AI is designed this way, it becomes something firms can rely on to support high-quality work, not replace it, but strengthen it.
That is the direction I believe the profession needs to move toward, and it is encouraging to see a company approach AI with this level of responsibility and care.