Artificial intelligence has moved well beyond the stage of curiosity within finance organizations. Many accounting and finance teams have tested AI tools, experimented with automation, and identified promising use cases. Even so, relatively few organizations have translated those efforts into consistent business outcomes. The challenge is rarely access to technology. More often, it comes down to governance, process design, data quality, and thoughtful implementation.
Those themes were at the center of the Controllers Council and Prophix webinar, How to Deploy AI for Results: CFO and Controller Panel Discussion, featuring Joy Mbanugo, CFO at CXAI, Genevieve Hancock, Principal Consultant at G Hancock Advisory, and Kierian Davis, Senior Product Marketing Manager at Prophix. Moderated by Controllers Council Executive Director Neil Brown, the discussion explored how finance leaders can move beyond experimentation and begin realizing measurable value from AI investments.
AI Adoption Has Begun, but Maturity Remains Limited
The webinar opened with polling that reflected a familiar pattern across finance organizations. More than half of attendees reported using AI in some capacity, while only a small percentage described their organizations as significant AI users. Others remained in the evaluation stage or had not yet adopted AI.
Kierian Davis noted that this uneven progress is not surprising: “Finance teams are at different stages of that journey and there’s no right or wrong stage of that journey to be on.”
He explained that many organizations are successfully testing AI, but far fewer have reached enterprise-wide maturity because foundational work must come first.
“The market is describing the destination of where you can get with autonomous and agentic, but it’s without ever talking about that journey.”
The panel agreed that finance leaders should resist comparing themselves to headlines or vendor marketing. Successful implementation depends less on speed and more on readiness.
Individual AI Use Often Outpaces Organizational Strategy
One recurring observation was the gap between personal AI adoption and company-wide deployment.
Genevieve Hancock sees that distinction across nearly every client engagement.
“The individual adoption is much higher than institutional adoption right now.”
Professionals are already using tools such as Claude and other large language models to improve productivity, while many organizations continue evaluating governance, security, and compliance requirements before approving broader deployment.
Joy Mbanugo shared a similar experience from previous leadership roles.
“I highly encourage people…to at least give your enterprise some limited access to an LLM because some of your junior staff may already be putting your data in there.”
Rather than assuming employees are waiting for formal approval, finance leaders should recognize that AI is already entering the workplace and establish clear policies before sensitive financial information is exposed.
Governance Must Come Before Automation
Throughout the discussion, governance emerged as the single most consistent recommendation.
Before selecting software or launching pilots, Genevieve encouraged finance leaders to ask three practical questions:
- Can the process be audited?
- Is the work deterministic or judgment based?
- Who ultimately owns the result?
As she explained: “If you can’t audit it and you can’t include it, then frankly, you can’t rely on it.”
Routine activities such as reconciliations, invoice processing, or transaction matching may be excellent candidates for automation. Judgment-heavy responsibilities, however, continue to require experienced finance professionals reviewing AI-generated output.
Kierian reinforced that point by reminding attendees that control should never be sacrificed.
“Control isn’t a trade-off between what you can delegate. Control should be the foundation that makes delegating possible.”
AI Belongs Where It Solves a Real Business Problem
The panel also addressed one of the questions many finance organizations continue asking: Where should AI actually live?
Rather than promoting a single answer, the speakers emphasized that placement depends on the organization’s size, complexity, and existing technology environment.
Genevieve recommended evaluating governance requirements first before deciding whether AI belongs inside an ERP platform, within financial performance management software, or as a specialized solution.
Joy added another consideration: “I’d also add just the sophistication level of the team in the company.”
Smaller organizations may achieve meaningful results using spreadsheets supported by enterprise AI tools, while larger companies with multiple business units often require dedicated planning platforms alongside their ERP systems.
Kierian summarized the decision simply: “It’s not about picking a winner, it’s actually matching the right AI to the right workflow or use cases.”
Measure Business Outcomes, Not Technology Adoption
When discussing return on investment, the conversation moved well beyond software licenses or user counts.
Finance teams should certainly monitor time savings, process improvements, and productivity gains. However, the panel encouraged leaders to think more broadly.
Kierian noted that AI’s value extends beyond reducing labor hours.
“It’s not just about I want to reduce my close period by so many days.”
Reducing month-end stress, improving employee experience, and allowing finance professionals to focus on higher-value analysis all contribute meaningful business value.
Genevieve cautioned organizations against skipping baseline measurements before implementation.
“The biggest mistake that I see made is that they’re not accurately measuring what they’re trying to accomplish on the front end.”
Without documenting current cycle times, error rates, or manual effort, organizations struggle to demonstrate whether AI actually improved performance.
Joy observed that boards increasingly expect finance leaders to connect operational improvements to financial performance.
“I think one way to get to the ROI is through cost savings.”
Demonstrating how AI contributes to productivity, efficiency, and ultimately financial results will become increasingly important as organizations expand their investments.
Vendor Selection Requires Healthy Skepticism
With hundreds of AI vendors entering the finance market, selecting the right partner has become more challenging.
Joy offered perhaps the simplest piece of advice from the session.
“I would probably never buy a tool without having a friend said that it’s good.”
References from trusted finance peers often provide more practical insight than polished product demonstrations.
Genevieve recommended asking vendors difficult questions about governance, model oversight, and how incorrect outputs are identified and corrected over time.
Kierian encouraged buyers to evaluate whether solutions are truly integrated into finance workflows or merely layered on top.
He suggested asking several practical questions during demonstrations:
- Is the AI built into the platform or simply added afterward?
- Can users understand how answers are generated?
- Would the process withstand an audit?
- How is company data protected?
Those questions often reveal far more than feature lists.
AI Still Depends on People and Process
Perhaps the strongest consensus of the webinar was that successful AI adoption begins long before software implementation.
Organizations should first understand existing workflows, document dependencies, and simplify inefficient processes before introducing automation.
Genevieve described one exercise she regularly uses with clients: “Have them map out all of their financial processes and post-it notes on the wall.”
That simple approach often uncovers hidden dependencies that technology alone cannot solve.
Joy summarized the implementation sequence clearly: “People, process, then tech.”
When organizations reverse that order, technology frequently amplifies existing problems instead of resolving them.
Looking Ahead
Finance leaders no longer need to decide whether AI belongs in their organizations. That question has largely been answered. The more important decision is how to implement AI responsibly while preserving governance, financial controls, and confidence in reported information.
As the discussion made clear, organizations that invest in clean data, disciplined processes, measurable objectives, and thoughtful change management will be far better positioned than those chasing the newest AI feature without first establishing the proper foundation.
The opportunity is substantial, but lasting results depend on careful execution rather than rapid adoption.
Watch the full webinar on demand.
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Prophix® is a global leader in financial performance management, empowering finance teams to lead with clarity, capacity, and confidence. From planning and budgeting to forecasting, reporting, reconciliation, and consolidation, Prophix brings it all together in one intelligent platform.
Prophix One™, the flagship Autonomous Finance Platform, combines AI, automation, and intuitive technology to simplify complex work and elevate finance to a more strategic role. By automating routine tasks, delivering predictive insights, and enabling real-time collaboration, Prophix empowers finance teams to focus on driving business growth.
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