Those in finance, and even those who just touch it, often hear the term "unit economics." The number of times I have had to explain unit economics is staggering. And quite often I used different ways to approach the question. This inconsistency in how I explained unit economics prompted me to sit down and think through what unit economics actually is, why it is used so frequently in finance, and what is different about it in the Physical AI space.

What is unit economics?

Unit economics is a thing or an activity that generates value for its user. A share of that value is captured by a company as revenue. Delivering that value to the user requires the company to deploy resources that either exceed the captured share of value (generating a loss per unit) or, what companies hope for, fall significantly below that share in value (generating profits per unit). It's not the same as the gross or net profit you see in financial statements, as it doesn't account for the noise of overheads, one-time costs, R&D investment, sales force, etc. It's a basic foundational atom of economic value creation at the company that is tied to the nature of the company's product. Different products within the same company will have different unit economics. It might be a commercial transaction processed, a robotaxi ride, a customer acquired, a traffic camera, a nuclear microreactor, an oil well, an AI chatbot query, a subscription seat, a mile of freight delivery, a shipping container; the list goes on. Additionally, the same activity can be looked at from different angles, resulting in different unit economics. For instance, unit economics for commercial freight delivery can be a mile, a truck, or a trip. Regardless of the unit economics a company chooses, consistent usage, appropriate revenue and cost allocation, and their inclusion in actual decision-making are crucial.

Unit economics helps companies analyze multiple questions:

1. Do we deploy resources efficiently?

2. Are we able to capture maximum value from our product?

3. Does scaling hurt or help our profits?

A business with healthy unit economics gets stronger as it grows. A business with broken unit economics loses money faster if there is no realistic plan in place to improve it. The aggregate P&L will eventually tell you which one you have. The beauty of unit economics is that it tells that story ahead of time.

Unit economics of a physical product.

In the classic world of physical goods, the concept of unit economics is quite simple. It's commonly the physical good itself (a thing that exists in the physical world as a physical object). For example, a car, a microwave, or a goat. The value is derived by taking its consumer price and deducting the marginal cost to make it and deliver it into the hands of the end consumer, including raw materials, direct labor, freight, and other variable and directly attributable costs. The unit is a discrete object created, delivered, and consumed at a point in time. Costs are primarily COGS that scale but likely improve on a per-unit basis with volume. Companies incur other costs that have a primarily fixed nature, such as building a factory, production tools, marketing, and G&A, which need to be recovered through scaling the sale of products with positive contribution margins. Value from the physical good accrues to the company at the moment of a single transaction, where ownership of the good transfers from the seller to the customer.

How did SaaS change things?

Software products are different from physical goods. Software normally comes with sizable upfront development costs to create the software, while directly attributable costs to scale the software and deliver it to end users are virtually zero. Whether software is used by 10 or 1,000 users doesn't drastically change its cost structure (primarily the data infrastructure that supports operability of the software and customer support). So investors normally expect 70–90% contribution margins from SaaS products. The unit economics in the SaaS world becomes not the piece of software, but instead the human using that software.

The focus of these companies has turned from production to customer acquisition, retention, and upsell. Value from the unit economics of a SaaS product accrues to the company over the lifetime of the customer, not at the moment of sale. The key performance metrics in SaaS are tied primarily to the customer, not the product: for instance, LTV (lifetime value), CAC (customer acquisition cost), the LTV/CAC ratio, net revenue retention, magic number, etc. Even though the focus of the company became the customer rather than the product, companies are still pursuing scale to recover their fixed cost basis (tied to development and customer acquisition costs). SaaS products generate higher contribution margins than physical products, but they also come with higher uncertainty about the total value captured, as value accrues over time rather than in a single moment of sale. The strategy of companies becomes "acquire customers fast, retain as cheaply as possible, upsell as often as you can, and increase switching costs." Unit economics with low marginal costs incentivizes companies to aggressively pursue customer growth, making it the key topic of executives' attention.

What happened when Software AI entered the market?

Large language models arrived and reintroduced the thing SaaS had spent a few decades eliminating: a meaningful, recurring marginal cost. Every interaction with Claude or ChatGPT carries a variable cost driven by model consumption of GPU/CPU and energy. The benefits of the virtually zero marginal costs of a traditional SaaS product evaporated, as each query runs the model again, and the more information that query needs to account for (for instance, the history of prior communication), the higher the cost of a new query is. Agentic AI takes it even further, as each request to execute a task triggers a chain of reasoning, tool calls, information retrievals, and retries, causing the cost of a single session to multiply. Inference cost is frequently the largest variable line that scales with usage, presenting economic friction to increased utilization.

The unit economics in the Software AI world shifted from the customer to the query. Flat-rate subscriptions of a SaaS business break when heavy usage drives the cost per interaction substantially over the flat subscription fee, to the point where low usage doesn't offset it. That's why usage-based and hybrid pricing have returned, to price utilization closer to the cost to serve.

What also became important is whether the company selling a Software AI product owns its data processing infrastructure or rents it. Companies that are vertically integrated, owning the model and the data infra, carry the inference cost (data centers, energy) internally and have better opportunities and incentives to improve the efficiency of the model and the cost of data processing. However, building your own data centers is a huge deterrent for a software-native company, so many companies opt to specialize in the model and product, renting their data infrastructure from providers. This business model leaves companies with fewer opportunities to improve their contribution margins, as data infra providers charge per token with margins built into the pricing and have fewer incentives for optimization other than competition. More usage no longer means leverage; it means more cost. The discipline shifts from "acquire and retain cheaply" back to "serve profitably," a concept that is familiar to the physical product space.<

What is unique about economics of a Physical AI asset?

Physical AI assets include humanoids, autonomous vehicles, warehouse robots, drones, and other machines that perceive the world and act in it to fulfill a task. This class of products is commonly sold as a service: robotics-as-a-service, capacity-as-a-service, or labor-as-a-service. The unit economics that generates value becomes the work performed. The framing has shifted from "seat" in the traditional SaaS space to "work done"; value capture becomes more outcome-driven rather than customer-driven.

Economic value creation of a Physical AI asset has a hybrid nature; it doesn't fit perfectly into the profile of a traditional physical good or a SaaS product. The traditional model can't be applied because the asset is not sold, locking in margins at the point of sale; instead, it's deployed to deliver value to its consumer over time, so in that sense it resembles a SaaS product. However, the marginal cost of a Physical AI product is not virtually zero. This is an actual physical product that carries the cost of hardware, data, spare parts, installation, field operations, powering, repairing, human oversight, retiring, etc. This makes a Physical AI product unique: it has a recurring revenue stream normally associated with a SaaS product, paired with a capital-intensive production model associated with traditional physical products. The revenue from deployment of a Physical AI product now has to carry the burden of covering the upfront development costs, customer acquisition costs, installation/integration costs, cost of physical parts, cost to serve, etc. The value of the outcomes delivered to customers by deployment of a Physical AI asset has to be high enough to cover the costs and achieve the profitability expectations of investors now accustomed to SaaS margins and the adoption rates of Software AI.

The overall economic profile of a Physical AI asset is assessed by its ability to generate a stream of revenue over the full operating life cycle that covers upfront capital plus ongoing operating costs. This value converted into revenue is often tied to deliveries completed, items picked, or miles driven, but primarily to the cost of labor that the Physical AI asset displaced or the cost of the legacy machine it replaced. The core question becomes: can this unit deliver a unit of work at a fully loaded cost below the incumbent, at high enough utilization, to earn an acceptable return on the capital deployed? There are a few performance metrics that become critical to the Physical AI asset:

- Utilization. In a SaaS world, the revenue captured by the company from selling a "seat" on a subscription basis is the same regardless of how heavily the capabilities of the product are being used, or whether they are used at all. With a Physical AI asset the situation is different. If it's sold to the customer under a "$ per outcome" pricing structure, it only generates revenue when used. An idle unit might not be wasting ongoing operating costs, but it does waste the cost of capital used to develop it and the cost of a shortened lifespan, as a more advanced and capable machine is being developed to replace it. Physical AI unit economics is highly driven by uptime, operating hours, and throughput, similar to the logic that governs airlines, hotels, and equipment rentals. Utilization is a variable factor that has massive downstream consequences for unit economics and, hence, must become a key focus point for companies in this space.

- Scale. SaaS products don't get impacted by economies of scale as much as a Physical AI product does. Integration of SaaS products into the software universe of an enterprise is usually a tailored endeavor that creates meaningful revenue streams from software implementations and customizations. As the production volumes of Physical AI assets increase, Wright's Law kicks in, experience improves the efficiency of production methods, and the cost of components goes down with volume discounts. Physical AI assets experience a similar effect to what we observed with wind turbines, solar panels, and electric batteries. The more units are built, the cheaper each unit becomes. For companies building Physical AI products, the ability to scale, optimization of hardware architecture, and investment in production methods become critical factors impacting unit economics.

- Autonomy. Physical AI assets quite often operate with a human in the loop who supports the operational capabilities of the asset when the software lacks the capacity to handle a specific edge-case scenario of reality. Think of Waymo's teleoperation, where a remote assistance operator gets involved in moving a vehicle out of a situation that the software can't handle. The labor costs of human supervision go into operating costs, dragging margins down. As software capabilities improve, allowing for more autonomous operation, unit margins improve. Quite often, improving the software model requires data coming from deployment of the assets; this is how scale becomes another contributor to the unit economics, by creating a data flywheel that feeds data into model training, which in turn increases the autonomy of the Physical AI asset.

- Upgrades. When companies improve the cost per unit of a traditional product through volume discounts, production methods, etc., those improvements do not go backward to improve the cost of units that have already been produced. There is no backward cost improvement. Physical AI assets, however, do enjoy the benefits of software improvements, provided that the hardware of the units allows for it. When the capabilities of the software improve, those improvements can be sent wirelessly to the entire fleet of Physical AI assets, raising the unit economics of all of them. Scale and a proper data flywheel enhance their importance through their ability to impact the unit economics of the entire fleet of Physical AI assets. What also becomes important is designing hardware in a way that accounts for future software improvements, not just current software capabilities at the moment of unit production to fulfill current customer requirements.

- Ownership and financing. With traditional products, ownership is usually transferred to the customer at the moment of sale, enabling the customer to capture all benefits from using the product. On some occasions, the seller finances the product, either itself or through a participating financial institution, but value still accrues to the customer after the transaction closes, as the product is being used. The seller captures the value at the moment of sale. With a SaaS product, both customers and sellers capture the value over time. Customers use the product to achieve certain outcomes over time and pay a subscription fee to the seller; in other words, financing is in a sense included in the subscription fee structure. The ownership and financing structure of a Physical AI product can fall anywhere between the two models. Let's consider two scenarios:

1. The seller retains ownership of the assets, selling their utilization to the customer on an "as-a-service" basis. This resembles a SaaS model, where the seller accrues value over the useful life of the asset. However, if the revenue is tied to the outcome and not to time periods, the revenue stream is not as predictable as that of a SaaS product. What complicates the matter even further for the seller is that the seller has less control over utilization of the asset by the customer, so while the seller retains ownership of the asset and all the risks and costs associated with that, the customer is given a lot of control over the revenue stream that affects the unit economics of the asset on the seller's books. The seller carries the upfront development costs, the cost of improving capabilities, the cost to serve, and capital costs, but doesn't fully control the revenue stream needed to cover those costs, and also needs to carry the cost of incentivizing increased utilization by the customer.

2. The seller transfers ownership of the Physical AI asset to the customer. In this case, the customer has incentives to utilize the product to the maximum of its abilities, as they capture full value over its useful life. However, if the seller accrues its value at the moment of the transaction, it loses the incentive to invest in improving asset capabilities. Those incentives and performance requirements become hours of contractual negotiations between the parties, turn into pages of legal language, extend time to adoption, hinder the pace of scaling, create the need for compliance monitoring, and create all sorts of friction.

3. A hybrid model, where either the seller or the customer takes on the burden of ownership and associated risks and gets compensated for those risks accordingly. The business model and pricing architecture are designed in a manner that creates alignment of incentives between the seller and the customer and allows for distribution of the value from deployment of a Physical AI asset between the parties, where each party gets rewarded fairly for the contributions they make and the risks they carry. This structure should be carefully constructed by finance that understands the complexities of economic value creation and distribution in the Physical AI space.

The economics of a Physical AI asset depend on the cost to develop, the cost to build, the cost to deploy, the cost to serve, the cost to scale, and the cost to improve (capabilities, autonomy, utilization). They account for the full life cycle of the product, similar to how project finance for real estate projects works. The usual metrics of LTV/CAC or gross margin at the point of sale lose their relevance in favor of asset-level NPV, IRR, cost per unit of output, payback period, utilization rate, gross operating margins, etc.

What does this mean?

Physical AI doesn't introduce a new kind of unit economics so much as it inherits every kind that came before. It carries the capital intensity of physical goods, the recurring value capture of SaaS, and the comeback of the marginal cost of Software AI. The businesses that win will be the ones that create business models addressing all of these forces at once. A Physical AI product has complex unit economics that take into account the entire lifecycle of the product and the interdependencies between the various factors that impact a company's ability to capture the value the product creates. There's a reason why getting the unit economics of a Physical AI product right from the onset matters more than it did in software. When a SaaS company gets its unit economics wrong, it burns cash, learns, and can often pivot its way out. When a Physical AI company gets its unit economics wrong, the mistake is already sunk into hardware architecture, production facilities, field operations infrastructure, price per outcome expectations, etc. That's why a finance function fluent in the economics of innovation isn't a back-office hire in this space, it must be part of the founding team. The cost of getting unit economics wrong substantially outweighs the cost of bringing finance into the conversation early enough to do it right.

June 2026

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.

Before we dive into use cases for AI in strategic finance, let me first explain how I see the role of strategic finance in the organization.

What is strategic finance?

So many times in my career people used strategic finance and FP&A (Financial Planning & Analysis) interchangeably. I can see how for someone outside of finance, those two sound the same. The scope is presumed to include budgeting, forecasting, management reporting, and ad-hoc analytics. And that’s all true. Those are primary activities covered by the FP&A function.

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 within the capitalistic system,

- aligning functional activities within the organization around a single vision of the company’s financial future,

- developing and leveraging core organizational competencies to generate value demanded by the market,

- structuring value capture mechanisms, including business model and pricing architecture, best suited for the nature of the product and market environment,

- utilizing market forces to capture and retain value in a capital-efficient manner, and

- investing in a competitive moat to secure market positioning and future prospects.

Strategic finance is a broker of trust between capital and the organization that deploys it. The role of strategic finance is very similar to the role of government that acts as a broker of trust between people paying taxes and government’s bureaucratic machine that deploys those taxes to supply citizens with food, shelter, safety, and social structure that provides opportunities for a better life. A similar line of thinking applies to the venture capital industry. VC firms exist to broker trust between capital owners (investors in VC funds) and entrepreneurial opportunities with high potential of outsized economic returns. Investment banks broker trust between institutional and retail capital and the public market. Governments, venture capital, investment banks, and strategic finance serve a similar purpose just on a different scale. Strategic finance is embedded inside companies with a mandate to ensure that the company deploys capital in a manner that has the highest chance of generating maximum return.

How can AI help?

This is where I see AI adding value to strategic finance.

- Scaffolding of the financial models.

I always built my own financial models from scratch. There is a certain level of creativity involved in building the structure, assessing the need for its depth and complexity, investigating the key assumptions, and envisioning the conversations the model will facilitate. I never needed to use AI to help me with that. Recently, as I investigated the economic potential of various frontier technologies, I tested the model-building capabilities of AI tools, asking them to build financial models using publicly available information. They did build the models quickly, burning almost all of my token allowance, but they did a good job creating draft versions. File structure, assumptions, three-statement linkages, scenarios, sensitivity architecture, Monte Carlo setup, etc. A few errors here and there, but generally speaking, it produced a solid foundation for financial models that can be made useful via refinements that reflect nuances of the specific business and its environment, use case, target audience, granularity, and support for the key assumptions driving the financial outcomes.

- Research assistant.

AI chatbots have replaced Google for me as the go-to source of information for intelligence gathering exercises. AI is the most powerful technology I’ve seen for analyzing massive amounts of data in a short period of time to extract relevant insights and package them in the format convenient for consumption. I use it for analysis of market research, industry trends, government regulations, tailwinds and headwinds, competitive dynamics, and assumptions that go into financial models. It’s a great tool for synthesizing market intel, drafting narratives, creating storylines, helping with investor Q&A prep, and condensing content into bite-size pieces. However, there is human judgment involved in making use of the intelligence gathered by AI. There is also a very strong need to verify sources and the accuracy of interpretation of information by AI. 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 of AI. I recommend tracing every model assumption and critical input back to their sources that can survive legal and investor scrutiny. And AI cannot account for all relevant information and intuition that it doesn’t have access to.

For instance, take a frontier hardware company — a robotics startup. Building a credible financial model means making assumptions about things that don't have clean historical data yet: component cost curves as production scales, rate of improvement of human-in-the-loop intervention, supply chain dependencies, useful life of components and rate of repairs, customer adoption rates, willingness to pay, revenue structure, milestones unlocking new revenue streams. A human analyst could spend a week pulling this together from different sources scattered across the internet. AI can compress that into an afternoon, surfacing the comparable cost curves, flagging assumptions that lack public data and need a judgment call, and drafting a first-pass sensitivity range for the assumption. What it can't do is decide whether the rate-of-repairs assumption is aggressive or conservative given what the VP of Engineering said in last week's product review, whether the assumption on customer willingness to pay on a recurring basis is realistic based on a conversation that the VP of Business Development had at a conference last month, or whether the investors will find the path to breakeven credible enough to fund the next raise. The judgment calls — what public source to rely on, what historical precedent to use as a reference point, what logic to deploy where data doesn't exist, which market forces shaping the assumptions deserve attention, what feedback from internal stakeholders to incorporate or dismiss — those are still the strategic finance leader's to make.

- Automation of routine tasks.

Agentic AI is useful for automating routine, repetitive tasks of high value. For instance, combining data from different sources to automate management reporting, variance analysis, and re-forecasting. AI can be used to automatically update KPI dashboards; it can analyze market signals, signals from internal communication channels, and competitors’ moves to raise alarms that a specific scenario is unfolding. It can even automatically suggest strategic responses to external and internal events to trigger further discussions amongst stakeholders. This is the area where agentic AI can be incredibly useful to strategic finance, increasing the velocity and depth of relevant action-focused conversations.

- 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, of course, as long as you have your token allowance.

What are humans better at?

This is where I see strategic finance requiring human touch.

- Making financial models useful.

Financial models do not exist in a vacuum. They are not created to impress investors during fundraising and put on a shelf. They are created to communicate current financial reality, present versions of the future out of the many possibilities that could unfold, and facilitate conversations. Financial models serve multiple purposes:

- inform the leadership of the company’s financial performance,

- identify key operational drivers that impact that financial performance,

- interpret relationships between different operational drivers,

- keep a placeholder for key assumptions for the values of those operational drivers,

- suggest behavior patterns for how those values will change over time and how that will impact the company’s financial results,

- demonstrate different versions of reality in response to the behavior of different operational drivers (for instance, via Monte Carlo simulation or scenario planning),

- present trade-offs and financial outcomes of different decisions,

- become an anchor of conversations amongst the key stakeholders to align on the operational areas with the highest economic value to the company.

The true value of a financial model comes not so much from the mechanics and formulas, but from the hypothesis behind its assumptions, logic incorporated into inputs-outputs relations, and usefulness in discussions with stakeholders. Model assumptions can be backed by historical numbers, tied to a credible source, hypothesized to reflect market forces and competitive dynamics, or simply act as the targets for what the respective stakeholders should strive them to become. 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 that it had access to when making that reasoning. What it cannot account for is the multi-faceted nature of the organizational communication web, differences in how different stakeholders consume information, who needs to be brought in to create initial inputs, and who prefers to have their inputs incorporated into the pre-final draft of the model, how to tailor information and narrative behind it to a specific audience, 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. Organizational behavior and the realm of ideas are still intangible objects that exist in the collective consciousness of the employees. AI simply cannot have access to those (yet) and cannot apply judgment about how to leverage the financial models to drive productive conversations and influence decision-making.

- 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 the investors, knowing whose number to trust and whose to discount, understanding whose support to obtain before bringing up a strategic discussion to the key decision-makers. The success of strategic finance in guiding or influencing decisions in the company is heavily dependent on its relationships with stakeholders, ability to read the room, skillful balancing of when to push back and when to let go, historical knowledge of what worked and didn’t work in the past, and intuition that is built over time. AI lives in the soft world, while decisions are made in the physical realm, at least for now. I know how to identify relevant intelligence, how to read competitive dynamics, when to treat a signal as noise and when to treat it as an indication to take action, when to raise red flags, when to pivot, when to stop the cash bleeding, when to throw more capital into the initiative, when to reallocate resources, when and how to initiate decision-making conversations. AI is not capable of any of that.

There is an argument that AI is capable of agency to make decisions and it’s up to organizations to leverage that capability. I don’t see that being the case. If AI is allowed to execute its agency in the strategic finance field and make decisions on deployment of capital with strategic and financial consequences on its own, then we don’t need the C-suite, we don’t need the Board, we don’t need venture capital, we don’t need investment bankers, we don’t need government. As I mentioned before, strategic finance is a broker of trust between capital and the organization with the mandate to ensure its efficient deployment. If capital deployment decisions are outsourced to AI, then the entire financial system must be managed by AI to deliver optimal outcomes. Perhaps that's the future we'll build — one where AI manages the entire economy. However, for now, the entire financial system is an architecture of accountable trust, and you can't delegate trust to something that can't be held accountable.

- Taking ownership and accountability.

The head of strategic finance signs their name to the forecast, acknowledging the reasonableness of key assumptions and the high likelihood of reality unfolding according to the projections based on the best available information at the moment and the best assessment of the commitments from the key stakeholders with the power to shape that reality. They carry real consequences that impact their promotion, pay, and power within the organization. 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 or a decision.

I have been in numerous conversations with lawyers and investors defending the company’s unit economics, financial projections, and key assumptions driving those projections. I cannot imagine lawyers and investors having those conversations with an AI chatbot and relying on that conversation to sign off on numbers that go to the SEC or to invest in a fundraising round. Strategic finance is capable of painting a bigger picture, emphasizing points that are relevant to the conversation counterpart, applying different approaches to instill confidence in numbers based on the circumstances, and incorporating feedback or valid points raised in the conversation into the next iteration of the financial model, often in the same meeting.

- Reproducing thinking patterns.

Reproducibility of the outcomes of AI models is one of the key sources of friction in adoption of AI in finance. Stakeholders of finance expect reliability of data, judgment, logic, and advice. One of the criteria that establish the reliability level is: “can you do it again?” While AI can replicate creation of assets — KPI dashboards, board financial update slides, budget vs. actual review decks, and other management reports — it has a hard time replicating the reasoning and thinking patterns. I know the support behind each financial number communicated to any stakeholder in any situation and I can explain how it came to life. AI can change its reasoning on its own, taking different reasoning paths to produce results. When strategic finance engages in alignment of stakeholders around capital allocation decisions, that variability is not helpful. Strategic finance needs to have a single foundational narrative and tailor its delivery to the stakeholders’ roles. Reproducibility of thoughts and logic is still a human quality.

- Leveraging creativity.

AI-generated language content is not original.The models are built with the information that already exists. Data that feeds the algorithm of an AI model has already been created. Since AI models are trained on the data available to all AI model producers, they are generally producing homogenized commoditized outputs — more of the same. Humans, however, have unique abilities to find creative solutions out of pure imagination, finding inspiration in sources that we don’t fully understand. Beginner’s mind, a concept defined in Zen Buddhism as “an attitude of openness, curiosity, eagerness, and a total lack of preconceptions when approaching a subject”, is a uniquely human quality. Strategic finance often works with incomplete information in ambiguous settings to imagine what the future might and should look like and this often involves finding novel solutions and trying out new approaches to problem-solving. The imagination and creative force of the human mind are among the key skills deployed by strategic finance leaders to tackle situations not reflected in the existing data. The human mind can tap into a much vaster pool of data, feelings, imagination, and intuition, something that AI simply cannot do due to the limitations of its training set and underlying architecture.

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. When AI capabilities are used well, AI can deliver the output of a small team of analysts: it can scaffold the models, synthesize the market intel, draft the commentary, and automate the routine so that human attention flows to what actually moves the needle. I see the division of labor between humans and AI in strategic finance working 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 signals 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 the field of strategic finance. The tangible products of strategic finance — the model, the presentation, the dashboard — are not the point of the function. The point of strategic finance is to manage trust between capital and an organization guiding the deployment of that capital in a financially sound manner while accounting for the context in which the organization operates. 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 is owning. With the cost of capital rising, organizations need to apply a more rigorous financial discipline to the deployment of capital. 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. Powered by AI, strategic finance can stand on firmer ground, investing more time in the areas that matter to the financial success of organizations.

July 2026