Gartner predicted 50% of organizations will use AI to replace “time-consuming forecasting approaches" by 2028 in hopes they will bring (more) accuracy and efficiency to financial forecasting and resource allocation. Executives report trouble adapting budgeting processes to the complexities of today’s dynamic market conditions given how hard it is to forecast inflation and budget in an environment of rising costs, volatile prices, and economic uncertainty. Caterpillar leveraged machine learning to cut quarterly forecasting from three weeks to 30 minutes. (But) AI tends to focus on optimizing performance indicators that prioritize short-term gains at the expense of long-term growth. Overemphasis on cutting costs could compromise critical long-term initiatives, such as innovation or customer satisfaction
In recent years, artificial intelligence and machine learning have been deployed as game-changers for corporate budgeting, in the hopes that they will bring unprecedented accuracy and efficiency to financial forecasting and resource allocation. For example, Amy Weaver, the CFO of Salesforce, has consistently turned to predictive AI as a strategic asset to enhance expense forecasting. At Caterpillar Inc., the senior VP of finance, Kyle Epley, leveraged machine learning to cut quarterly forecasting time from three weeks to just 30 minutes. Similarly, Dev Ahuja, the CFO of Novelis Inc., is using in-house machine learning for cash-flow forecasting and budgeting. In that context, Gartner predicted that 50% of organizations will use AI to replace “time-consuming bottom up forecasting approaches" by 2028.
Yet even the most well-equipped companies with cutting-edge access to AI tools are still grappling with budgeting challenges. Take Unilever, for example. In a statement in the Wall Street Journal, Graeme Pitkethly, the company’s CFO, acknowledged how hard it was to forecast inflation and budget for 2023 in an environment of rising costs, volatile energy prices, and economic uncertainty. Echoing that point, executives at BASF and Covestro have reported having trouble adapting their budgeting processes to the complexities of today’s dynamic market conditions, despite having the resources to heavily invest in AI.
These examples raise a critical question: If AI is so advanced, why do these challenges persist? Does the budgeting process just require human insight all the way through?
Tactical vs. Strategic Budgeting
Budgeting is a multi-step process that involves two key components: tactical and strategic. Tactical budgeting focuses on short-term objectives. It involves resource allocation, cost management, and performance tracking — tasks that are well-defined and driven by data. In these areas, AI excels. Its ability to process large volumes of data and make real-time adjustments allows it to optimize short-term resource allocation, improving operational efficiencies with remarkable speed and accuracy. In fact, AI can act as a direct substitute for human decision-making in these tactical tasks, providing faster, more consistent results while eliminating biases.
However, strategic budgeting is a different story. It involves long-term planning, setting broader business goals, and aligning financial resources with the company’s future vision. Strategic decisions often have to account for uncertainty, competitive pressures, and market volatility — areas where human insight remains critical. AI struggles here, because it lacks the foresight, adaptability, and creativity needed to navigate long-term strategic planning. In that arena, what’s known as the surrogation effect comes into play, as described in HBR in 2019 by Michael Harris and Bill Tayler: AI tends to focus too narrowly on optimizing specific performance indicators, leading to misaligned decisions that may prioritize short-term gains at the expense of long-term growth. For example, an overemphasis on cutting costs to meet short-term targets could compromise critical long-term initiatives, such as innovation or customer satisfaction, ultimately undermining future growth.
How AI and Human Managers Interact
Given these differences, the central question becomes: How do AI and human managers interact across these distinct parts of the budgeting process? Can AI fully replace people in tactical budgeting, and where does human insight become indispensable in strategic decisions?
To answer this, we conducted a management simulation designed to mimic real-world budgeting challenges. Our goal was to test how AI performs across both tactical and strategic budgeting and determine how well AI performs in relation to people in both areas.
In this simulation, seasoned managers were tasked with making budgeting decisions for a hypothetical automotive-parts manufacturer, balancing short-term financial performance with long-term strategic growth. The participants defined their company’s strategic goals — such as operational excellence or innovation leadership — and aligned their budget allocations with those objectives. They then allocated budgets over multiple cycles, adjusting their decisions based on performance feedback. Simultaneously, we deployed an AI algorithm to allocate budgets using the same information available to the participants. This setup enabled a direct comparison of how the AI algorithm performed relative to human participants in terms of budget allocation and creating organizational value. However, some participants selected performance indicators that were not well aligned with their strategic focus, allowing us to observe the AI algorithm’s performance in both scenarios — when they were provided with a well-defined strategic context and when they were not.
The results were clear: Although both AI and human participants improved over time, AI consistently outperformed managers in optimizing budget allocations by learning from past data and performance measures. However, when the strategic context was ill-defined, when managers selected KPIs that were misaligned with the strategy, AI did not reach its full potential. It managed the tactical aspects of evaluating past initiatives, by optimizing individual performance measures, but it often failed to align with the company’s broader strategic goals.
On the other hand, when managers provided a strong strategic framework — clear goals and relevant KPIs — AI excelled. In these cases, AI’s data-processing power was enhanced by human strategic insight, creating optimal outcomes. The complementarity between AI’s efficiency and human insight was key to balancing both the tactical and strategic aspects of the budget allocation.
New Insights
Our research challenges two common assumptions about AI in budgeting. First, the belief that AI can fully replace humans in budgeting is flawed. AI excels in tactical tasks — such as short-term resource allocation and data-driven adjustments — but without human-driven strategic oversight, it risks optimizing only for immediate wins, ignoring the broader goals. AI may boost efficiency but often fails to align with long-term value creation if left unchecked.
Second, the assumption that AI and human insight must always complement each other is equally misguided. In tactical areas, AI should replace humans. It’s faster, more precise, and immune to biases. But in strategic decision-making, where long-term goals are set and market uncertainties must be navigated, human insight is irreplaceable. AI can’t adapt creatively or anticipate future shifts without strategic guidance. To truly unlock AI’s potential, businesses must divide their budgeting processes into tactical and strategic components. AI should lead in tactical tasks, but human managers must guide AI to ensure that tactical decisions support the long-term vision.
Uber has managed to achieve this balance. With operations across 600+ cities, the company leverages AI-driven financial platforms for real-time budgeting adjustments. AI handles tactical decisions with precision, optimizing resource allocation and improving operational efficiency. But human oversight is critical. Uber’s local teams often override AI predictions based on market knowledge, ensuring tactical adjustments align with the company’s long-term goals. This balanced approach has been pivotal in Uber’s recent turnaround — leading to its first-ever $1.1 billion operating profit in 2023.
Uber’s success proves that while AI dominates tactical decisions, human insight is indispensable for ensuring those decisions serve the broader strategic vision. Businesses that get this balance right will not only thrive operationally but also secure sustainable growth.
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AI alone is not enough to solve the challenges of modern budgeting. AI can — and should — replace human managers in tactical tasks, such as short-term resource allocation and performance feedback, where data-driven decision-making leads to faster and more efficient outcomes. But in the strategic realm, where long-term planning, market adaptability, and business foresight are critical, human insight remains indispensable. To unlock the full potential of AI in budgeting, companies must integrate both the tactical and strategic components of their budgeting processes. The companies that succeed in the future will be those that learn how to effectively balance these two elements — leveraging AI where it excels and relying on human judgment where creativity and strategic vision are required.
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