AI Can Build the Model. It Can't Broker the Trust Between Capital and Organization. That's Strategic Finance's Job.

Every week brings a new headline about AI taking over finance jobs. I spent the majority of my career in strategic finance. Like many people in intelligence-heavy fields, I felt uneasy with the narrative of AI commoditizing intelligence. However, after diving deeper into transformer architecture, learning more about the foundational principles of AI, and extensively using its capabilities myself, my view on AI's role in finance has shifted. AI has commoditized language, but not the financial intelligence that is at the core of strategic finance's value proposition. Judgment, relationships, accountability, creativity, seeing a bigger picture — these are uniquely human qualities that make up the bulk of the strategic finance skillset. I see AI as a great collaborator for strategic finance, not its replacement.

What is strategic finance?

So many times in my career people used strategic finance and FP&A interchangeably. The scope of strategic finance, however, is much broader than that of pure FP&A. Strategic finance is primarily concerned with shaping the strategic direction with an overarching goal to achieve maximum return on capital deployed, aligning functional activities around a single vision of the company's financial future, structuring value capture mechanisms, and investing in a competitive moat to secure market positioning.

Strategic finance is a broker of trust between capital and the organization that deploys it. VC firms exist to broker trust between capital owners and entrepreneurial opportunities. Investment banks broker trust between institutional capital and the public market. Strategic finance serves a similar purpose — embedded inside companies with a mandate to ensure that capital is deployed in a manner that has the highest chance of generating maximum return.

How can AI help?

• Scaffolding financial models. AI can quickly produce draft versions of financial models — file structure, assumptions, three-statement linkages, scenarios, sensitivity architecture, Monte Carlo setup. A solid foundation that can be made useful via refinements reflecting the nuances of the specific business, its environment, use case, and target audience.
• Research assistant. AI chatbots have replaced Google as the go-to source for intelligence gathering. AI is the most powerful technology for analyzing massive amounts of data in a short period of time — market research, industry trends, regulations, competitive dynamics, and assumptions that go into financial models. However, strategic finance influences multi-billion-dollar decisions; the tolerance level for hallucinations or misreading the data is much lower than in most other use cases. Every model assumption and critical input must be traceable to sources that can survive legal and investor scrutiny.
• Automation of routine tasks. Agentic AI is useful for combining data from different sources to automate management reporting, variance analysis, and re-forecasting — automatically updating KPI dashboards, analyzing market signals, and suggesting strategic responses to external events.
• Always on. It has no ego, no ambitions for career growth, always happy to receive feedback and criticism, and has infinite patience to redo the job as many times as needed.

What are humans better at?

• Making financial models useful. The true value of a financial model comes not from the mechanics and formulas, but from the hypothesis behind its assumptions, the logic incorporated into inputs-outputs relations, and its usefulness in discussions with stakeholders. The model is not meant to be a static tool — it's meant to be a dynamic asset that facilitates decision-making. And that requires judgment.
• Applying judgment. Judgment is not the same as reasoning. AI has learned to reason, but its reasoning is based strictly on data it had access to. What it cannot account for is the multi-faceted nature of organizational communication — what stakeholders actually think but don't want on the record, what they shared in a water-cooler conversation about the ground truth of reality.
• Influencing decision-making. Strategic finance is primarily a contact sport: getting the GM to agree to and own their budget, negotiating pricing guidelines with business development, narrating the financial story to investors, knowing whose number to trust and whose to discount. The success of strategic finance in guiding decisions is heavily dependent on relationships, ability to read the room, and intuition built over time.
• Taking ownership and accountability. The head of strategic finance signs their name to the forecast, acknowledging the reasonableness of key assumptions. They carry real consequences that impact their promotion, pay, and power. AI doesn't carry any accountability for its advice or its consequences. It can't be fired, can't lose credibility with investors, and doesn't sign its name to a forecast.
• Reproducing thinking patterns. AI can change its reasoning on its own, taking different paths to produce results. When strategic finance engages in alignment of stakeholders around capital allocation decisions, that variability is not helpful. Strategic finance needs a single foundational narrative and to tailor its delivery to stakeholders' roles.
• Leveraging creativity. Beginner's mind — an attitude of openness, curiosity, eagerness, and a total lack of preconceptions — is a uniquely human quality. Strategic finance often works with incomplete information in ambiguous settings to imagine what the future might look like, finding novel solutions that no training set contains.

So where does that leave strategic finance in the age of AI?

AI leaves strategic finance leaders with a powerful set of tools enabling focus on higher-value-added activities. The division of labor works as follows:

• AI drafts the models; the human refines them and owns the assumptions.
• AI surfaces the market signals; the human decides which signals to treat as noise vs. which warrant action.
• AI produces the analysis; the human updates the forecast, develops recommendations, orchestrates decision-making, and carries the consequences.

The narrative of AI taking over finance is overblown. In the 1980s, spreadsheets were supposed to eliminate accountants. Instead, they eliminated menial bookkeeping tasks while multiplying the productivity of the profession. The same is happening in strategic finance. The tangible products — the model, the presentation, the dashboard — are not the point of the function. The point is to manage trust between capital and the organization guiding its deployment. Judgment, relationships, accountability, creativity, seeing a bigger picture — all of the work that happens to turn the model into the decision is what strategic finance owns. AI can build the model. It can't broker the trust. And as long as capital is deployed by humans, it's the humans with skin in the game who need to stand behind the numbers.