For decades, long-term investing has been evaluated using the same toolbox: NPV, IRR, best/base/worst case scenarios, and sometimes a little real options to make it look serious. A useful grammar – but based on a comfortable lie: the world is stable enough for our assumptions to remain so.
The academic article Artificial Intelligence in Corporate Financial Strategy: Transforming Long-Term Investment and Capital Budgeting Decisions
Authors by Ashutosh Roy et al. (JEFAS, 2025) makes a simple point: AI doesn’t replace corporate finance – it makes it practicable in an unstable world. And that changes everything, because in the real world, investment decisions aren’t problems of calculation: they’re problems of revisibility.
What literature really says (and implies)
1) Classic methods are not “wrong” – they are rigid
NPV and IRR remain fundamental because they impose a discipline: compare, update, decide. But they have inherited structural constraints:
- fixed assumptions (rates, cash flows, competitive stability)
- dependence on history (which is precisely what is least reliable at break-up)
- difficulty integrating weak signals (regulation, sentiment, techno, ESG)
In other words, they’re perfect… for a slow-moving world.
2) AI is not a “better Excel”, it’s a regime change
Roy et al. insist on a breakthrough: AI enables dynamic models (continuous updating, external signals, non-linearities). It intervenes where conventional finance suffers:
- cash flow forecasting via ML / time series
- large-scale scenario analysis (massive simulations rather than 3 hypotheses)
- integration of non-financial data (NLP on news, ESG reports, sentiment)
What this produces is not just “more precise”: it’s more adaptable.
The point that really interests a COMEX: AI restores flexibility… and values it
Finance has always had this idea (real options): the real asset is the ability to defer, extend, reduce or abandon. Problem: it’s expensive to model, and therefore rarely done properly.
The article makes a key point: AI makes this flexibility operational in real time. An option is no longer a theoretical chapter; it becomes a signal-driven mechanism:
- market volatility
- competitive pressure
- technological disruption
- supply / energy constraints
- regulation and ESG
As a result, we no longer just value a project, we value a decision trajectory.
The Tesla case: useful… but to be read as a demonstration, not as proof
The authors use Tesla (FCF over 5 years) to illustrate the difference between traditional and AI approaches.
- Classic” NPV: +$7.56 bn (fixed assumptions)
- NPV “IA”: +$8.95 bn (growth adjustments + efficiency gains)
- probability of positive NPV: 72% → 88% via simulations
It’s instructive: you can see how an “AI forecasting + Monte Carlo + ESG signals” layer shifts the distribution of results.
But beware of reading bias: it’s not “AI magically discovers $1.4 billion”. It’s more like:
AI formalizes what decision-makers already do intuitively (revise assumptions, integrate external signals, reweight risk) – and makes it calculable, traceable and auditable.
Where the article becomes (unintentionally) explosive: governance, responsibility, explicability
The ethics/governance section is the real nerve of the war, because it points out the paradox:
- AI increases analysis power
- but it can reduce the capacity for explanation (“black box”)
- and amplify historical biases (imperfect data, incentives, ESG greenwashing)
In an investment decision, this is not a detail. A decision that can’t be explained is a decision that can’t be defended – and therefore can’t be governed.
The literature converges on a few answers:
- Explainable AI (XAI ): making variable contributions intelligible
- model audit: drift, bias, robustness, stress tests
- board-level monitoring: who signs? who reviews? on which triggers?
- data governance: provenance, quality, compliance, use of “alternative” data
The subtext is clear: AI is not an analyst’s tool. It’s a governance issue.
What marketers need to capture (or miss)
Because yes: “corporate finance” sounds a long way from marketing. In fact, it goes right to the heart of marketing investment allocation.
AI applied to capital budgeting sounds exactly like what marketing teams are experiencing:
- uncertain cash flows (incremental revenues, CLV, churn)
- unstable assumptions (CAC, saturation, media mix, competition)
- a need for revision (stop rules, reallocation, timing)
The actionable lesson is as follows:
The future is not “AI does budgeting”. The future is “budgeting becomes revisable by design”.
And that implies refereeing discipline:
- Explicit assumptions (what must be true for the investment to hold)
- Massive scenarios (not 3 variants – one distribution)
- Managerial options (what we can change if the world moves)
- Reopening conditions (dated, measurable, enforceable triggers)
- Explicability (to defend, learn and iterate)
FAQ — AI, capital budgeting and financial strategy
How does AI transform capital budgeting?
AI enables dynamic models that continuously update assumptions, integrate non-financial signals (ESG, sentiment, regulation) and simulate thousands of scenarios. It doesn’t replace NPV/IRR — it makes them more robust and adaptable in an unstable environment.
What are real options and why does AI make them operational?
Real options are the ability to defer, expand, reduce or abandon an investment depending on how the context evolves. While theoretically well-known, they were too costly to model in practice. AI makes them actionable in real time via measurable signals (market volatility, competitive pressure, ESG constraints).
Why are best/base/worst scenarios no longer sufficient?
These three scenarios reduce uncertainty to three discrete cases, which is insufficient for today’s volatile environments. Probabilistic approaches (Monte Carlo) simulate distributions of thousands of possible futures, revealing the true shape of risk and the probability of investment success.
What are the governance challenges of AI in financial strategy?
AI increases analytical power but creates a black-box risk: a decision that can’t be explained can’t be defended. The key challenges are explainability (XAI), model auditing (drift, bias, robustness), board-level oversight and source data governance.
What is the connection between AI capital budgeting and marketing strategy?
Marketing teams face the same challenges: uncertain cash flows (CLV, churn), unstable assumptions (CAC, saturation, media mix) and the need for revision. Applying AI capital budgeting principles to marketing means making advertising investments and growth plans “revisable by design”.
Conclusion: AI doesn’t “decide” better. It makes the decision reviewable without losing face.
Roy et al. defend a strong thesis: AI is becoming a transformative force in financial strategy, as it improves forecasts, simulations and ESG integration, while raising issues of transparency and governance.
