AFP’s Director of the FP&A Practice Says AI Is Changing FP&A from Reporting Numbers to Shaping Decisions
Bryan Lapidus explains how AI is moving FP&A beyond reporting by automating analysis while making human judgment, data governance, and decision support more important.
Bryan Lapidus
Director at The Association for Financial Professionals
Strategic Shift: AI is moving FP&A beyond reporting, positioning finance teams as partners in decisions, capital allocation, and risk management.
Human Judgment: AI handles data-heavy modeling, while finance professionals supply assumptions, context, experience, and judgment needed for reliable decisions.
Governance First: Effective AI in finance requires trustworthy data, shared metrics, disciplined processes, and flexible application across business needs.
Practical Uses: Common AI use cases include variance commentary, anomaly detection, contract review, and faster scenario analysis for finance teams.
Hiring Changes: AI may let companies scale faster than finance teams, shifting hiring toward judgment and business partnership.
As Director of the FP&A Practice at The Association for Financial Professionals, Bryan Lapidus has had a front-row seat to how AI is shifting the industry.
We sat down with Bryan to learn what he's seeing from that seat. Here's what he told us.
Accelerating Evolution
I’m Bryan Lapidus, Director of the FP&A Practice at the Association for Financial Professionals. My career followed a traditional path that included consulting, earning my MBA, and holding corporate finance roles at organizations like American Express and Fannie Mae, where I focused on building forecasts, managing budgets, and helping leadership navigate uncertainty. I also worked at two private equity-backed portfolio companies and have now worked at the Association for Financial Professionals for eight years.
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In my current role, I create and curate content for the corporate finance community to help them excel in their current and future roles. I maintain frequent contact with the finance community, approving about 50 conference sessions, 20 webinars, five guides or surveys, and numerous articles each year. I also manage five roundtable groups — most importantly, the FP&A Advisory Council — to learn what the community is working on!
FP&A has evolved and continues to evolve — from reporting numbers to explaining them, and increasingly, shaping decisions. This evolution has accelerated significantly in the last few years with AI. New tools create new capabilities, which create a new operating model for finance. This means we can do more, and our customers expect more from us.
How AI Is Changing FP&A from a Reporting Function to a Decision-Making Function
AI should be understood through the CFO’s core job: stewarding capital from source to use. Accounting explains where capital has been. Treasury manages where it is and how it circulates. FP&A helps decide where it should go next.
The tools may change, but finance’s obligations do not. Control, compliance, capital allocation, risk management, and investor accountability remain foundational. What changes is how finance delivers value. New tools, such as automation and AI create new capabilities which creates new operating models. In those new models, finance has the opportunity to move closer to business in strategy, partnership and decision support.
As routine work is automated or outsourced, finance roles become more analytical, cross-functional, and embedded in the business. Work also becomes less tied to traditional silos. Instead of organizing strictly around FP&A, treasury, or accounting, finance increasingly organizes around workflows, capabilities, and decisions that cut across functions.
The activity mix changes too. Time shifts from manual and transactional work toward scenario analysis, opportunity assessment, and risk management. That makes data, governance, and process design even more important. AI does not fix weak processes; it only makes them run faster.
The future of FP&A is not to compete with AI on analysis, but to shape the environment where analysis becomes good decisions. As forecasting, reporting, and scenario modeling become faster and more accessible, analytical output becomes abundant, and judgment becomes scarce. FP&A’s role therefore shifts from reporting performance and producing analysis to advising decisions and designing the decision envelope: establishing the assumptions, definitions, metrics, constraints, objectives, tradeoffs, risk tolerances, escalation paths, governance, and capital allocation principles that allow thousands of human- and machine-made decisions to be made consistently across the enterprise.
By encoding this financial intelligence into the systems that generate forecasts, recommendations, and actions, FP&A becomes the steward of decision integrity, ensuring decisions align with strategy and create sustainable value at scale.
The Most Common Use Cases for AI in Finance
I’ve had a front-row seat to how finance leaders are responding to AI. Some are experimenting cautiously; others are redesigning workflows entirely.
The most common use cases I see in the finance community improve individual member productivity. Examples include drafting commentaries to explain variance reports and checking outputs by having AI adopt personas to challenge those reports.
Anomaly detection is another effective use case: AI can identify outliers in reports and models that require further scrutiny. This accelerates error checks and saves people from certain embarrassment!
I even spoke with one CFO who told me she uses an LLM to read contracts, provide summaries, flag risks, and identify potentially tricky language. She completed her review in one morning, instead of sending it to legal and waiting three days for a response.
Why Humans Must Apply Their Experience and Judgment to AI Outputs
The throughline is that AI handles data-heavy work, while humans apply experience and judgment.
I always assumed experience was inherent in financial models because experienced people always built the models.
That's no longer the case. AI is building models now, so experience has been separated from the mechanics of model building. It now resides in the assumptions, the framing of the initial prompts, and the rigorous interpretation and interrogation of the output.
Side-by-side comparisons may be illustrative:
- AI generates base-case forecasts; humans then layer in potential future investments or risk "haircuts" where challenges exist.
- AI generates and evaluates multiple "what-ifs" and potential trends; humans then decide which risks to focus on or which outcomes are most important.
- AI pulls in data and parameters for models; humans then decide which to build into the model.
The throughline is that AI handles data-heavy work, while humans apply experience and judgment.
Why AI Workflows Need both Discipline and Flexibility
Finance must lead the development of a consistent analytical framework that defines metrics, calculations, data sources, and the process for translating data into information.
I remember speaking to one CFO who recommended "discipline at the core, flexibility at the edge." That stuck with me, because it provides enough control for everyone to trust the data and enough agility to apply it quickly.
When done well, this kind of governance allows finance to be an accelerator, not a brake on company processes.
Why CFOs Must Work Cross-Functionally to Remove Dirty Data
Speaking of discipline, every CFO needs to get disciplined with their data! It's incredibly important — and even more so with AI. A chef would not make soup with dirty water, and you should not make financial decisions with dirty data or misunderstood metrics.
Finance is often described as the function that turns data into decisions. But before finance can generate insight, someone has to ensure the underlying data is trustworthy, consistent, and understood the same way across the organization. That's why leading finance teams increasingly play a role that extends beyond reporting and analysis. They become part of a cross-functional capability that defines key metrics, governs data standards, and establishes the rules for how information moves from source systems to business decisions.
Think of it as a chain of custody for information. Data originates in operational systems, is transformed through business logic and calculations, and ultimately appears in forecasts, dashboards, management reports, and strategic decisions. At every stage, definitions, assumptions, and governance matter. If revenue, customer, margin, or productivity mean different things to different groups, the organization spends more time debating numbers than acting on them.
Finance is uniquely positioned to help prevent that outcome. As the steward of enterprise performance, finance can partner with IT, operations, and business leaders to create a common analytical framework — one that ensures data remains consistent from source to usage, allowing the organization to focus less on reconciling information and more on making better decisions.
How AI Affects Hiring and the Finance Team's Size
As a result of AI, I commonly see companies growing much faster than their finance teams, increasing operating leverage.
That isn't because of layoffs — CFOs are seldom laying off people on their teams. It's because they hire differently now. Before hiring, they must ask, "Can AI do this or do I need a human?"
Focus Your AI Usage on Real Business Challenges
Here’s my advice: Anchor your AI efforts in real business challenges, rather than starting with the technology and then looking for a problem…Remember, the obligations of the CFO don’t change. The only thing that changes is the tooling we use to deliver our insights!
Here's my advice: Anchor your AI efforts in real business challenges, rather than starting with the technology and then looking for a problem.
This will help you avoid scattered experimentation and ensure that you build solutions that deliver measurable outcomes. It'll also shift the conversation from “What can this technology do?” to “What decisions can we improve?” — and that’s where AI makes a meaningful impact.
Remember, the obligations of the CFO don't change. The only thing that changes is the tooling we use to deliver our insights!
Follow Along
You can follow Bryan Lapidus' work on LinkedIn.
More expert interviews to come on The CFO Club!
