Artificial intelligence has moved well beyond experimentation. Finance leaders are now evaluating practical ways to incorporate AI into accounting operations while maintaining the governance, controls, and oversight that define the controller’s role. During the Controllers Council and Paystand webinar, Evaluating Agentic Finance Platforms: A Controller’s Guide to AI, Philip Peck, VP Finance Practice at Peloton Consulting, and Gregg D’Eon, VP Controller at LTK, discussed how controllers can evaluate emerging AI platforms, identify meaningful use cases, and build a disciplined approach to adoption.
Rather than focusing on technology for its own sake, the discussion centered on measurable business outcomes, responsible governance, and preparing finance organizations for the next stage of AI maturity.
AI Adoption Is Accelerating Across Finance
Early audience polling reflected how quickly AI adoption is expanding. Most attendees indicated their organizations are already using AI in some capacity, while only a small percentage reported no current usage. As Philip Peck observed, adoption is following an increasingly steep growth curve.
“There could be people on your teams out there in the audience who are actually using it, but they don’t necessarily talk about it. So, they could be using it independently outside of the blessed ecosystem.”
Gregg D’Eon explained that finance organizations often adopt AI more cautiously than other departments because accounting teams must preserve strong controls and reliable financial reporting. At LTK, however, AI has become part of everyday operations across the business.
“We use it a lot for pretty much every meeting that I have.” He highlighted AI-generated meeting transcripts, summaries, and action items as one example of immediate productivity gains.
Within accounting, LTK is also applying AI to automate reconciliation work, analyze journal entries, and streamline repetitive close activities.
Understanding the AI Maturity Curve
One of the session’s most practical discussions focused on viewing AI as a progression rather than a single destination.
Philip Peck described a continuum that begins with simple AI assistance, where employees use AI to draft emails, summarize policies, or generate formulas. From there, organizations advance toward AI augmentation, where systems recommend actions such as variance explanations or anomaly detection.
The next stage introduces AI automation, allowing systems to execute structured processes such as invoice processing, journal entry preparation, or cash application while humans monitor exceptions. Beyond that lies agentic AI, where multiple AI agents coordinate activities across workflows under human supervision.
Fully autonomous finance remains a future objective rather than today’s reality, but organizations can begin realizing meaningful value well before reaching that point.
Start with Business Problems, Not Technology
Both speakers emphasized that successful AI initiatives begin with operational challenges rather than software demonstrations.
Gregg encouraged controllers to examine the work that consumes disproportionate time across their organizations.
“As a controller, you’re the one who knows best what areas of your close need improvement.”
He also noted that many organizations already own AI capabilities embedded within existing finance platforms. Before investing in new technology, controllers should determine whether current systems can solve existing pain points.
Philip reinforced this approach by recommending that organizations first identify where they experience excessive manual effort, operational risk, or reporting delays. Once those priorities are established, controllers can evaluate which AI capabilities best address each challenge and define measurable outcomes before implementation begins.
Build the Business Case with Meaningful Metrics
Several attendees asked how finance leaders should justify AI investments. Rather than relying on broad productivity claims, both panelists recommended creating clear financial measurements.
Philip advised organizations to establish baseline metrics before introducing AI. Close cycle length, reconciliation effort, reporting preparation time, processing accuracy, compliance performance, and manual hours all provide measurable starting points that allow finance teams to demonstrate improvement over time.
Gregg offered a practical perspective grounded in financial leadership.
“I used to have an old CFO who always told me, Greg, there’s a dollar value tied to everything.”
He recommended translating time savings directly into labor cost reductions or additional capacity so executives can immediately understand the financial impact of proposed AI initiatives.
Controllers who present improvements in terms of business value, rather than technical features, are more likely to secure executive support.
Governance Must Come Before Deployment
The conversation repeatedly returned to governance as one of the controller’s most important responsibilities.
Gregg stressed that organizations should ensure employees only have access to information appropriate for their roles and that AI systems follow the same security principles already applied across finance.
Philip expanded on that recommendation by describing AI governance as an extension of traditional financial governance rather than an entirely separate discipline. Organizations should define approved use cases, establish human approval requirements, protect financial data, evaluate vendor security practices, monitor AI outputs, and maintain accountability for every significant financial decision.
As AI becomes more deeply integrated into finance operations, audit expectations will also evolve. Controllers should expect increased scrutiny around how AI systems are governed, what decisions they influence, and how organizations validate AI-generated outputs.
AI Will Change Finance Roles Rather Than Replace Them
The webinar concluded with an important discussion about the future of finance talent.
Gregg believes AI will allow accounting professionals to spend less time on repetitive activities and more time performing analytical, judgment-oriented work. Rather than eliminating finance teams, AI should enable employees to contribute at a higher level while improving job satisfaction.
Philip echoed that perspective, emphasizing that successful implementations are primarily business transformation initiatives rather than technology projects. Clean data, well-designed processes, effective governance, continuous measurement, and thoughtful change management remain the foundations of long-term success.
Both speakers also acknowledged that finance professionals will need new capabilities, including stronger data literacy, AI prompting skills, critical thinking, and a deeper understanding of how systems and data interact across the enterprise.
Final Thoughts
Agentic AI represents an important development for finance organizations, but successful adoption depends far more on disciplined execution than on selecting the newest technology. Controllers who begin with clearly defined business objectives, establish measurable outcomes, strengthen governance, and keep experienced professionals involved throughout the process will be well positioned to capture meaningful value while maintaining confidence in financial reporting.
AI will continue to reshape finance operations, but the controller’s responsibility remains unchanged: protecting the integrity of financial information while helping the organization operate more effectively.
To hear the complete discussion and explore additional insights on evaluating agentic finance platforms, governance considerations, AI use cases, and implementation strategies, watch the on-demand webinar here.
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