Can Strategic Finance Be Systematised?

In a previous piece I argued that so-called “agentic” finance systems depend less on technology than on operational discipline. Intelligent systems only work when the economic logic of the business is coherent and embedded in data and processes. That raises a more interesting question: once that groundwork exists, what parts of the finance function could actually be systematised?

Strategic finance is a good place to explore that question.

Intelligence and judgement

Much professional work contains two distinct layers. The first is intelligence: gathering data, building models, analysing drivers and exploring scenarios. The second is judgement: interpreting the analysis and deciding what action to take.

A widely held view is that AI will increasingly handle the intelligence layer of work, while humans retain responsibility for judgement. In this model, systems perform much of the structured analytical work, gathering information, analysing drivers and running scenarios, while people remain responsible for interpreting results and deciding what to do.

Recent commentary from firms such as Sequoia Capital has revisited this idea, framing the shift as the difference between AI improving the tools people use and AI increasingly performing the work itself. Much of the discussion so far has focused on areas where work has historically been outsourced. Customer support, claims processing or bookkeeping are obvious targets because they involve large volumes of structured labour.

But there is another category of work that sits inside organisations rather than being outsourced: small, high-leverage analytical roles that support complex decisions. These roles sit precisely at the boundary between intelligence and judgement. Much of the work involves structured analysis, yet the decisions those analyses inform remain firmly human.

Strategic finance is one example of this type of work, alongside several other analytical functions that interpret how a business operates.

What these roles share is not the domain they operate in, but the structure of the work itself. Each function produces analytical views of how the business behaves. Leadership then applies judgement to decide what action to take.

Strategic finance is a particularly interesting example because it sits at the intersection of growth, economic structure and capital allocation, the three forces that largely determine the trajectory of a company.

The role of strategic finance

Strategic finance teams have become increasingly common in technology companies and private-equity backed businesses, particularly in sectors such as fintech and payments where growth dynamics, unit economics and capital requirements interact in complex ways. These teams typically sit close to the CEO or CFO and act as the analytical backbone behind major decisions.

When leadership debates pricing, market expansion, product investment, partnerships or operational restructuring, strategic finance is often responsible for modelling the financial consequences.

In practice the work tends to revolve around a recurring set of questions. How sustainable is our growth? Where do margins actually emerge as the business scales? What operational drivers constrain performance? Which initiatives deserve capital?

Although each analysis appears bespoke, the underlying questions repeat across companies.

Foundational questions

If you step back, many of these analyses reduce to a small number of foundational questions about the business itself.

• How does the company generate growth?

• What economic structure emerges as the business scales?

• How should capital be allocated across competing opportunities?

Many of the day-to-day questions asked of finance teams, such as hiring plans, pricing decisions, runway, fundraising or investment priorities, are ultimately derived from these more fundamental questions.

Strategic finance exists largely to answer them.

Where the tools stop

Despite the strategic importance of this work, the analytical infrastructure behind it has evolved in a surprisingly ad hoc way.

Financial planning platforms such as Anaplan or newer AI enabled tools like Pigment have significantly improved how companies coordinate forecasting and planning cycles. They provide shared models, connect operational drivers to financial outcomes and make it easier to update projections as assumptions change.

But these systems still depend largely on the assumptions and analyses that teams bring into them. Strategic finance teams continue to build separate models to understand growth drivers, unit economics or the financial consequences of major initiatives. Even with modern planning tools, much of that analytical work is still reconstructed repeatedly across spreadsheets and bespoke models.

The intelligence layer behind strategic finance is still largely constructed by hand.

In practice this work often lives in a collection of spreadsheet models built over time by different analysts. Each model may capture important assumptions about how the business works, but those assumptions are often embedded in formulas, worksheets and individual judgement rather than in shared analytical frameworks.

Two challenges tend to follow. The first is spreadsheet sprawl. As companies grow, new models are built to answer specific questions about pricing, growth drivers, cost structure or investment initiatives. Over time these models proliferate, often with overlapping logic and assumptions.

The second is tacit knowledge. Much of the reasoning behind these models exists in the heads of the analysts who built them. When those people move roles or leave the company, the logic behind important analyses can become difficult to reconstruct.

This matters because these models often inform strategic decisions about hiring, pricing, expansion and capital allocation. When the analytical foundations behind those decisions are scattered across spreadsheets and individual judgement, the organisation’s understanding of its own economics can become surprisingly fragile.

These problems are rarely discussed explicitly because spreadsheets remain flexible and powerful tools. But they highlight a broader point: the analytical layer behind strategic finance has never really been systematised.

The hypothesis

This raises an interesting possibility.

If strategic finance teams repeatedly answer the same analytical questions, the frameworks behind those analyses may also repeat. If those frameworks can be defined clearly enough, it becomes possible to imagine systems that generate and run the analysis directly.

Instead of analysts rebuilding models manually, analytical engines could continuously update the drivers of the business and feed those insights into the long-range financial plan.

The goal is not to replace judgement. Leadership will still decide what to do.

But if the intelligence layer of strategic finance can be systematised, the analytical capacity behind those decisions may expand dramatically.