Controllers Council recently held a discussion entitled, What Financial Planning and Analysis Will Look Like in 2026, presented by BILL.
Panelists included Philip Peck, Vice President, Advisory Services and Finance Transformation Practice at Peloton Consulting Group, and Joe Fleischer, Webinar and Content Manager at BILL.
Following are key takeaways to this discussion. If you are interested in learning more, view the full roundtable panel video archive video here.
What is Financial Planning and Analysis?
FP&A is the function that focuses on budgeting, annual planning, forecasting, analyzing performance, supporting strategic decision making. In essence, FP&A is the analytical and advisory engine of the organization and connects finance to business operations and strategy.
Which aspects of FP&A lend themselves best to automation?
I would like to set this question up with what’s a bit of a distinction between automation and AI enablement. Primary purpose of automation is streamline repetitive and data driven tasks. AI is more oriented toward generating insights, predictions, and recommendations. From a focus perspective, automation in large part, it’s efficiency and accuracy. There’s an element of effectiveness, but primarily efficiency and accuracy. AI pivots a bit into foresight, quality of decisions. And then, of course, we’re going to speak to the human role in all of this. From an automation perspective, once you have automation in place, the role of the human is more monitoring validate versus AI outputs we are now as humans interpret, contextualize, and provide influence. So now let’s focus on what aspects of FP&A lend themselves best to automation:
- Data aggregation and preparation: It’s very well suited for extraction and consolidation of data from various types of systems, be it an ERP, CRM, HR, other systems, and even an enterprise performance management system. Very well suited for data validation, mapping to structures, scheduling things like refreshes of actuals and forecasts. AI in this case could be more anomaly detection in the data, spot errors and consistencies, automated tagging and classification using ML techniques.
- Forecasting and planning: Automation pre-populate a rolling forecast with actuals and updated drivers. Very well suited to that. Driver-based forecasting, you the classic, I’m using units sold at average price to come up with a revenue. Automated scenarios, generating the best, worst case and base case. AI then can extend that and provide a different level of enablement, predictive, using machine learning, modeling anything, revenue, expense, cash flow. Forecast bias detection, leveraging AI to help you do that, consistent under or over forecasting.
- Variance and performance analysis: Things that lend themselves to automation, automate variance reports, standardized dashboards and variance visualizations. You can extend that with AI. Let AI help you with the root cause analysis, the true causal activity, not necessarily just correlations, using natural language generation for commentary. Another one could just be trend recognition to provide a better way of doing early warning signals around performance deviations from your expected performance. The value there is turning static reporting into more proactive insights.
- Scenario modeling and decision support: Automation, predefined scenario templates that you can automate and generate and have at your fingertips. Some rule-based sensitivity could be things like plus or minus a certain threshold, similar things you could do, cost, revenue, and effects. Extending that with AI, AI-driven scenario simulation, generating and testing thousands, tens of thousands, hundreds of thousands outcomes using probabilistic models.
- Management reporting and insights: Automation automatically refresh, distribute, dashboard, scorecards. Schedule generating of the reports. So generate it, burst it, distribute it, and do it in a turnkey push button type of manner. Extending that with AI, using things like natural language generation to help with executive summaries and commentary. Accelerate your ability to produce those output books.
- Strategic and operational business partnering: Automation allows meeting packs, decks, reviews, standardized visuals, streamline automate. AI extensions, making it more predictive, predict the financial impact of business actions. Decision intelligence platform, integrating all kinds of data and information, and you move from more of a reactive to a proactive type of context.
- Continuous planning and process orchestration: Best for automation, workflow, approvals, task management. Very ripe for automation. Another one could be version control and audit tracking. Again, great areas for automation. Extending that with AI, cycle time optimization, predicting potential bottlenecks in a set of workflow or workflows, and even adjusting your calendar based on frequency volatility market signals. So instead of just staying to your same formulaic calendar, having AI help you adapt, configure somewhat on the fly based on what’s happening in the environment.
Automation makes FP&A efficient and effective, and AI makes it intelligent.
What is Agentic AI?
Agentic AI refers to artificial intelligence systems that can take initiative, make decisions, and act autonomously toward defined goals rather than simply responding to prompts or executing predefined rules.
Not that long ago, we’re like, okay, what is this now? What is this AI stuff? We’re over that hurdle and over that curve. But now this evolution, which is in some sense a revolution, agentic AI, the acceleration, we’re moving fast on that adoption curve. And granted, it’s still in the early stages of truly embedding agentic AI into our FP&A processes. The applications of agentic AI and AI in general are much broader than just FP&A. Finance the entirety of the organization. You could pick any function. I think what increasingly we’re seeing across the executives. I may not 100% appreciate it as the discipline; it as how we embedded in our processes, it on the ultimate value proposition, but they fully acknowledge it’s here and they need to make progress towards delivering on the promise and making it work inside their organization. Every organization in a sense is different. The level of maturity, where people are on their ability to acknowledge change. There are so many other factors here, but I think the emerging consensus is we need to get on board and we need to get on board now.
In what ways does a purpose-built approach to AI support FP&A?
A purpose-built approach to AI means designing and deploying AI specially for FP&A by leveraging financial logic, planning workflows, and domain data rather than using generic machine learning or analytics tools. This approach embeds financial intelligence, business context, and planning logic into the AI models, making them far more accurate, explainable, and trusted.
Looking ahead to 2026, what advice would you give FP&A leaders about using AI to amplify their skills?
I think AI is very relevant and should be top of mind for all of us in the FP&A profession. First, I would say to buckle up. I’ve got a lot of thoughts here I want to share, but I think it’s really important. One, shifting from a data preparer to a decision enabler. Huge opportunity. As we move into 2026 and beyond, increasingly, the more mechanical work, still very important, but mechanical FP&A work, collect the data, generate the base forecast, producing reports, will be in some way, shape, or form automated or AI assisted. The differentiator, which has been there but increasingly is magnified and elevated. The differentiator is judgment, storytelling, and back to our equation, influence. So, what to do? I would advocate embrace AI tools that get yourself away from the manual data and reporting work. As you’re doing that, reallocate your time. So, invest and double down on interpreting insights, guiding business decisions, moving into that business partnering realm. Position yourself as the strategic advisor, not the spreadsheet operator. The future FP&A leader won’t be the one who builds the best model, but the one who best explains what the model means for the business.
Next one: Develop AI literacy, not just financial acumen. Here’s the opportunity. AI will become a core competency for FP&A and finance professionals in a sense, much like Excel once was. I’m certainly at a point in my life where I remember the days before Excel and even some of the Excel-like tools that preceded Excel. You don’t necessarily need to be the Uber coder and have all that knowledge around that, but you do need to understand fundamentally how AI works, where it’s strong, and where it needs human oversight. So what should you do? Learn to interpret what’s coming out of AI-generated outputs, things like confidence scores, driver’s assumptions, and some of the underpinnings of the AI outputs. Be able to ask the right questions, just fundamental questions. Why is the model predicting this trend? And dig, dig. In the realm, many of the software solutions that help us as FP &A practitioners, they’re embedded with all kinds of capabilities, but you need to be able to provide the transparency, the visibility to how did we get that answer.
Also, understand the ethical and governance dimensions of AI and finance in FP &A. That’s paramount. Partner with your data and analytics teams to the extent they exist. One way to think about it is finance plus the data.
To learn more about FP&A, view the complete webcast here.
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