How AI is replacing the painful, manual process of building an annual operating plan

For most finance teams, Q3 marks the beginning of budget season and the familiar scramble to produce an annual operating plan (AOP). It’s an exercise often riddled with inefficiencies: spreadsheets flying across departments, conflicting assumptions, and forecasts often shaped more by internal politics than data.

But an AI-driven model is offering CFOs a new way forward — one that’s faster, more accurate, and designed to deliver clarity and confidence.

Building a smarter forecast using AI

My late friend and colleague Pat Bajari, Amazon’s visionary former Chief Economist and more recently Chief Economist at Keystone.ai, once asked a simple question: Could algorithms generate a more accurate, defensible plan than the traditional method? Pat tapped into econometrics and machine learning techniques to decompose revenue into the company’s core growth levers such as product selection, pricing, and customer experience.

At its core, the AOP process relies on coordination across dozens of business units. Each group submits its forecast, with some executives inflating costs or lowballing metrics to guarantee success. The result is a highly manual consolidation process where inconsistencies abound and it’s often unclear why numbers are moving up or down. It’s estimated that less than a quarter of finance teams spend their time on predictive work because they rely too heavily on manual forecasting processes and lack access to the correct data.

Each unit wants to show growth but also hit their targets. Often that leads to a lot of sandbagging, overpromising, or just pure guessing — and requires CFOs to act as a sort of “referee”. Planning turns more into a negotiation and number-crunching exercise than a true strategic forecast.

 Rather than asking business units to guess next year’s numbers, the new AI-driven model asks:

• What are the key growth drivers of the company?
• How many customers will we have and how big is the market?
• What will they spend if we expand our category or product offerings?
• What is the incremental impact of investments in better service?
• What’s the expected return from each major initiative?

The result is an explainable, dynamic forecast that not only takes a fraction of the time to build but is aligned with a company’s strategic priorities and enables scenario planning that was previously impossible.

CFOs need help

Agile planning is a top priority for CFOs and many have reported they need more support. A Deloitte CFO Signals report found that 84% of CFOs said they struggle with rapidly modeling business implications, responding to external events, and performing contingency planning. 

Using AI economics, companies can adjust forecasts scientifically in response to market shocks — whether tariffs, pandemics, or supply chain disruptions. The algorithm can’t predict a pandemic, but once it’s a known event, the model can explain what it means across every product line, instantly.

Early adopters of this model include global ecommerce platforms, major CPG brands, industrial manufacturers, medtech and pharma companies — firms with complex investment needs and high-stakes forecasting cycles.

The benefits for CFOs are clear, from faster planning cycles to real-time modeling of different scenarios (e.g., tariffs, supply-chain issues) to strategic alignment, as forecasts are built around key metrics rather than aggregated numbers. This type of methodology enables more realistic precision, helping CFOs quantify risk and uncertainty.

Perhaps most importantly, the model can bridge communication gaps between finance, operations, and business units. It reimagines planning as a dynamic activity versus a one-and-done, static operating plan. 

At the end of each quarter, once the CEO and CFO report on earnings, the team will see which factors caused the company to exceed or miss expectations, and they will be able to refine the approach.

What’s Next?

Adoption isn’t always easy, especially when it comes to AI. Business leaders must take ownership of their plans, yet few are eager to have their assumptions challenged by an algorithm. Crucially, they also need confidence that any forecast includes the right execution levers to navigate uncertainty and make course corrections.

To address this, I recommend running traditional and AI-generated forecasts side by side for a couple of years. This parallel approach allows companies to build credibility gradually, giving leaders space to evaluate the model’s insights without disrupting their workflows. Over time, what may begin as skepticism will shift into trust. Leaders will come to see the model not as a threat, but as a powerful tool — one that helps them sharpen their thinking and improve decision-making.

It’s a change many CFOs and finance teams are now embracing. In fact, just about three-quarters (74%) of CFOs report being directly involved in their organization’s change management efforts this year, according to Gartner.

As companies begin their FY2026 planning, finance teams can feel empowered to consider new tools and approaches. For CFOs facing economic uncertainty, tighter margins, or pressure to do more with less, having a model that provides real-time visibility and strategic clarity is more than helpful — it’s becoming essential.

This AI-driven approach helps answer where a company’s budget is best spent with data, not guesswork. And as the line between finance and strategy continues to blur, models like these could soon become the new standard — not just among the tech elite, but also across the most intelligent enterprises in the G2000 of tomorrow. 

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