From my own experience, I believe that in the coming years, AI will take over many of the repetitive tasks in FP&A. We’ll see more FP&A AI specialists supporting finance teams by automating the close process, generating variance explanations, building KPIs and KRIs, and even preparing responses for analyst calls.

These specialists won’t replace people — instead, they’ll equip finance professionals with faster insights and better data so they can make smarter business decisions. While AI handles the mechanics, humans will focus on driving growth and shaping strategy. Their role will become even more important in validating AI outputs and ensuring they truly reflect the company’s objectives.

Looking back, forecasting used to happen monthly or quarterly. Now, with AI, FP&A teams can forecast daily and see instantly how market or supply chain changes affect the P&L. This real-time visibility is transforming how finance supports the business — moving from explaining the past to actively shaping the future.

FP&A sits at the heart of how organisations plan, measure, and steer performance. Yet, in many companies, FP&A teams still spend too much time collecting data and explaining the past, rather than shaping the future. Spreadsheets, manual reconciliations, and backwards-looking reports often slow down decision-making at a time when business conditions change rapidly by the hour.

To stay relevant, FP&A must modernise how it works. The next evolution is about using technology, smarter processes, and integrated data to move from reporting to real insight. When finance professionals can access timely information and focus on forward-looking analysis, they become true partners to the business — driving growth, improving agility, and helping leaders make better choices faster.

When people and technology work together, FP&A can evolve into a real hub of insight — one that connects data, automation, analysis, and reporting in a way that supports faster, smarter decisions across the business.

Expanding the Human-AI Partnership

From what I’ve seen, many people still think AI will eventually take over finance jobs — but that’s not what’s happening. The truth is, the best results come when humans and technology work side by side. AI provides speed, scale, and analytical rigour. Humans bring judgment, intuition, and the ability to interpret business context. When both work together, finance teams can shift from reactive number-crunching to proactive value creation.

AI is at its best when it helps connect plans across the business and explain what changed and why it matters. That’s where finance professionals come in — using their experience and understanding of context to guide the right response.

For instance, AI might detect a 3% margin erosion in a product line and trace it to changes in commodity pricing. However, it takes a human business partner to decide whether the right move is to renegotiate supplier contracts, adjust pricing, or rethink the product mix. This partnership between technology and human judgment ensures decisions are both quick and meaningful.

FP&A as a Strategic Value Driver

The traditional role of FP&A has often been described as backwards-looking: explaining variances, tracking budgets, and producing static reports. The modern, AI-enabled FP&A team is forward-looking. It answers questions such as:

• How will today’s operational choices affect profitability three months from now?
• What scenarios could disrupt our supply chain, and how do we prepare for them?
• Which markets, products, or segments hold the highest potential for growth?

AI allows FP&A to model such scenarios quickly and continuously, not just once a quarter. This responsiveness helps organisations stay ahead of competitors, navigate uncertainty, and capture opportunities before others do.

Practical Applications of AI in FP&A

Beyond theory, there are already tangible examples of AI reshaping finance functions:

1. Automated Forecasting: Machine Learning algorithms ingest sales, marketing, and external data (like weather or macroeconomic indicators) to produce rolling forecasts with higher accuracy than manual spreadsheets.
2. Expense Management: AI-driven anomaly detection instantly flags unusual spending patterns, reducing fraud and compliance risks.
3. Cash Flow Prediction: By analysing customer payment histories and market cycles, AI improves working capital management.
4. Investor Relations Support: GenAI tools draft Q&A for earnings calls based on financial data and analyst expectations.

Each of these applications frees human capacity to focus on storytelling, decision-making, and driving outcomes.

Challenges and Governance

Of course, the journey is not without obstacles. Organisations must address:

1. Data Quality: AI models are only as reliable as the data they process. Inconsistent or siloed data undermines trust.
2. Change Management: Finance professionals may fear job displacement. Leaders must emphasise how AI empowers rather than replaces them.
3. Governance: Ethical use of AI in finance is critical. Transparency, explainability, and compliance with regulations must be built into every system.

Strong governance frameworks are therefore essential to ensure AI is trusted and its recommendations are used responsibly.

The Evolving Role of Finance Leaders

As AI adoption grows, finance leaders will need to rethink their roles. Tomorrow’s CFOs and FP&A leaders will not just manage numbers but orchestrate a hybrid workforce of people and digital agents. Their focus will shift to:

1. Designing and monitoring AI-driven processes.
2. Building data literacy and analytical skills within teams.
3. Acting as translators between machine outputs and business strategies.
4. Ensuring ethical and sustainable deployment of technology.

Tomorrow’s finance leaders will combine technical curiosity with empathy and change management skills, guiding their teams through this AI-augmented transformation.

Closing Thoughts

In my experience, the real strength of FP&A has always come from people — not from the systems or tools we use. Technology is changing fast. The 2025 FP&A Trends Survey shows that while some companies are beginning to use Generative AI, only a small number have truly reached the level of strategic advisors. To me, that says something important: progress in FP&A isn’t just about adding new technology — it’s about giving people the time and trust to think, question, and lead.

AI can sort through data in seconds, but it can’t understand what that data means for the business or what to do next. That still requires experience, judgment, and curiosity — the things only people can bring. The future of FP&A belongs to teams that can use technology to see clearly and then use human insight to decide wisely.