Why Your AI Pilot is Worth More Than You Think
There once was a product manager who proposed a $100,000 AI pilot project. The team was excited. The technology was promising. The use case was clear: deploy a chatbot to handle tier-1 customer service inquiries, learn from six months of real usage, then decide whether to scale.
The finance team ran a traditional NPV analysis. If they committed to everything—pilot plus the $500,000 scale-up investment—and achieved the expected $800,000 in value, the NPV would be $200,000. Not terrible, but when you factor in the risk? When you consider that AI projects fail more often than they succeed? The expected NPV might actually be negative or barely break-even.
The project was rejected.
Which raises an interesting question: when we evaluate pilots—especially AI pilots—are we using the right framework? Traditional NPV analysis works well for many investments, but it treats every decision as a now-or-never proposition. For pilots with high uncertainty and staged decision-making, there’s a better approach.
A pilot isn’t a mini-commitment. It’s an information purchase that creates decision rights.
Let’s look at why this matters, and more importantly, how to think about it correctly.
The Limitation of Traditional NPV for Pilots
Net Present Value is one of the foundational tools of corporate finance. Calculate the present value of expected cash flows, subtract the investment cost, and you get a number that tells you whether to proceed. Simple. Elegant. Effective for many investments—but incomplete for pilots.
Here’s what NPV assumes: you’re committing to a fixed path. You invest now, the project unfolds, and you collect your cash flows. There’s no flexibility, no learning, no option to change course. For a factory or a building lease, this might be reasonable. But for an AI pilot? It’s fundamentally the wrong model.
Consider our customer service chatbot. Traditional analysis says: “We’ll spend $600,000 total ($100K pilot + $500K scale-up). We expect $800,000 in benefits. NPV = $200,000.”
But wait. What if the pilot reveals the technology doesn’t work? What if customer satisfaction drops? What if the bot creates more problems than it solves? In traditional NPV, you’re assumed to have committed to the full $600,000 regardless. That’s not how pilots work.
A pilot gives you something NPV completely ignores: the right, but not the obligation, to proceed.
What Are Real Options?
If you’ve ever bought a financial call option, you understand the basic principle. You pay a premium for the right to buy a stock at a specific price by a specific date. If the stock goes up, you exercise the option and capture the gain. If it tanks, you walk away and your loss is limited to the premium you paid.
Real Options Analysis applies this same logic to business investments. The “option” isn’t on a stock—it’s on a real asset, a real project, a real decision. And just like financial options, real options have measurable value.
For pilots and R&D projects, the framework maps beautifully:
- The premium = your pilot cost ($100,000)
- The strike price = your scale-up investment ($500,000)
- The underlying asset = the project’s potential value ($800,000)
- The expiration = your decision deadline (say, 1 year)
- The volatility = your uncertainty about outcomes
The key insight is this: uncertainty isn’t just a problem to be managed. It’s actually what makes options valuable. The more uncertain the outcome, the more valuable it is to have the option to wait and see before committing.
Traditional finance intuition says “reduce uncertainty.” Options logic says “embrace uncertainty, but preserve flexibility.”
Why AI Projects Are Perfect for Real Options
AI and ML initiatives have three characteristics that make them ideal candidates for Real Options Analysis:
High Uncertainty: Will the model actually work in production? Will users trust it? Will it integrate with our systems? Will the business process adapt? These aren’t risks you can fully quantify upfront. They’re unknowns you discover through doing.
Natural Staging: Most AI projects follow a pilot-then-scale structure. You start small, learn, then decide whether to expand. This sequential structure is exactly what Real Options was designed to value.
Asymmetric Payoffs: The downside is capped (you lose the pilot cost), but the upside can be substantial (you scale to the full organization). This asymmetry—limited downside, meaningful upside—is the hallmark of valuable options.
Let’s add one more factor: Learning Value. A pilot doesn’t just de-risk the technology. It teaches you about your users, your processes, your constraints. That information has value even if you don’t scale this particular project—you apply those lessons to the next initiative.
Traditional NPV captures none of this.
A Worked Example: The AI Customer Service Pilot
Let’s return to our customer service chatbot and work through the numbers properly.
The Setup:
- Pilot cost: $100,000 (6 months, 100 users)
- Scale-up investment: $500,000 (full deployment, CRM integration)
- Expected value if successful: $800,000 (PV of cost savings + quality improvements)
- Uncertainty: High (σ = 40%)
- Decision timeline: 1 year
Traditional NPV Analysis:
NPV = $800,000 - $100,000 - $500,000 = $200,000
This looks okay, but it assumes you commit to everything upfront. In reality, there’s a 40-50% chance the pilot reveals this isn’t worth scaling. Factor that in and your expected NPV is closer to zero or negative.
Real Options Analysis:
Using a binomial option model (or Black-Scholes for simplicity), we value the option to scale after learning. The inputs:
- Underlying asset (S) = $800,000
- Strike price (K) = $500,000
- Time (T) = 1 year
- Volatility (σ) = 40%
- Risk-free rate = 5%
The call option value comes out to approximately $398,000.
Now subtract the pilot cost:
Expanded NPV = $398,000 - $100,000 = $298,000
The Learning Premium—the value you gain from flexibility—is:
Learning Premium = $298,000 - $200,000 = $98,000
Here’s what this tells you: by treating the pilot as an option rather than a commitment, you’ve unlocked nearly $100,000 in additional value. That’s a 50% increase over traditional analysis.
More importantly, if your traditional NPV was marginal or negative (which it often is for risky pilots), Real Options might flip the decision entirely. Projects that look like “no” become “yes”—not because you’re being less rigorous, but because you’re accounting for the full value of staged decision-making under uncertainty.
The Framework: How to Apply This
You don’t need a PhD in finance to use Real Options thinking. Here’s a practical framework:
Step 1: Structure the Decision
- What’s the pilot cost?
- What’s the follow-on investment if you scale?
- What are your decision points?
Step 2: Estimate Uncertainty
- What’s your best-case outcome value?
- What’s your worst-case?
- What’s your base case?
Use these three points to estimate volatility. A rough approximation: σ ≈ (best - worst) / (4 × base). This gives you a ballpark for the uncertainty parameter.
Step 3: Calculate Option Value
- Use a binomial model or Black-Scholes (plenty of free calculators online, or use the tool linked at the end of this post)
- Compare the option value to traditional NPV
- Quantify the learning premium
Step 4: Make the Decision
- If Expanded NPV > 0, proceed with the pilot
- If Traditional NPV < 0 but Expanded NPV > 0, you’ve found a “hidden gem”
- If both are negative, reconsider (though you might still do it for strategic or learning reasons)
Step 5: Set Triggers
- Define what “success” looks like for the pilot
- Establish clear criteria for the scale/no-scale decision
- These become your option exercise conditions
Common Pitfalls
Real Options Analysis is powerful, but it’s not a magic wand. Here’s where people go wrong:
Over-Engineering the Analysis: You don’t need six decimal places of precision. The value is in the framework—thinking about flexibility, learning, and asymmetry—not in false precision. If your volatility estimate is 35% vs. 45%, it doesn’t change the fundamental insight.
Garbage In, Garbage Out: If your base case numbers are fantasies, Real Options will just give you a more sophisticated fantasy. The discipline is in honest scenario planning.
Ignoring Option Expiration: Options expire. If you have indefinite time to decide, the option is worth more. But if market conditions or competitive dynamics force a fast decision, option value declines. Don’t assume you can wait forever.
Forgetting Organizational Reality: An option only has value if you can actually exercise it. If your organization can’t make adaptive decisions—if every strategic review takes 18 months and requires three layers of approval—your real options are worth less than the model suggests.
Using ROA to Justify Bad Bets: The point isn’t to greenlight every risky project by claiming “option value.” The point is to correctly value flexibility when it exists. If a project is genuinely terrible, Real Options will still tell you that.
When NOT to Use Real Options
Real Options isn’t always the right tool. Here’s when to stick with traditional analysis:
- Small, Low-Risk Projects: If the investment is tiny and the uncertainty is low, the option value is negligible. Just use NPV.
- Must-Do Work: Compliance projects, critical infrastructure, keeping-the-lights-on investments—these aren’t about optionality. You’re doing them regardless.
- No Real Flexibility: If you can’t actually walk away, pivot, or scale, there’s no option to value.
- Minimal Learning Value: If the pilot doesn’t teach you anything, it’s not buying information—it’s just a small version of the full project. Scale it or skip it.
The key test: Does uncertainty create opportunity? Does waiting or learning change your decision? If the answer is no, Real Options probably isn’t worth the effort.
Making It Actionable
Theory is nice. Application is better. So here’s what you can do:
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Take your next pilot proposal and run both analyses. Calculate traditional NPV. Then calculate expanded NPV with Real Options. Compare them.
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Look for hidden gems in your portfolio. Projects that were rejected on traditional NPV grounds might actually be valuable when you account for flexibility.
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Reframe the conversation with your finance team. Instead of “This project has an NPV of X,” say “This pilot costs $Y and buys us the option to invest $Z if we learn it’s promising. That option is worth $W.”
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Use the calculator I’ve built (link below). It’s an interactive tool where you can plug in your own scenarios, adjust assumptions, and see how option value changes. It takes 5 minutes and will immediately clarify whether Real Options thinking matters for your project.
The Deeper Point
Real Options Analysis isn’t really about the math. It’s about how we think about uncertainty and learning in strategic decisions.
Traditional finance treats uncertainty as something to minimize or discount. Real Options treats uncertainty as something that creates strategic value—as long as you preserve flexibility.
In a world where AI experimentation is increasingly critical, where the pace of technological change demands adaptive strategies, and where the cost of waiting for perfect information is often strategic obsolescence, this shift in thinking matters.
A pilot isn’t a mini-project with mini-ROI. It’s an information purchase that creates decision rights. It’s buying the right to be smart later after learning something valuable now.
Rigorous financial analysis is essential. But rigorous doesn’t mean “use NPV for everything.” Sometimes rigorous means “use the framework that matches what we’re actually doing.”
And for AI pilots with high uncertainty and staged decision-making? That framework is Real Options.
Try It Yourself: I’ve created an interactive Real Options calculator you can use to analyze your own projects. Download the Jupyter notebook here or explore pre-built scenarios including AI pilots, R&D investments, and platform technologies. The tool walks you through the analysis step-by-step and generates visualizations you can use in your own business cases.
Further Reading: If you want to dive deeper, the academic foundations come from financial options theory (Black-Scholes, Cox-Ross-Rubinstein) applied to strategic decisions. Practitioners like Tom Copeland, Martha Amram, and Nalin Kulatilaka have written excellent books on Real Options for business. For a more philosophical take on optionality and decision-making under uncertainty, Nassim Taleb’s work on convexity and John Kay & Mervyn King’s Radical Uncertainty provide valuable context.
The key insight across all of them: In an uncertain world, flexibility has value. Let’s start valuing it properly.
https://en.wikipedia.org/wiki/Real_options_valuation
https://web.mit.edu/ardent/www/RO_current_lectures/borison.pdf
https://www.columbia.edu/~mh2078/FoundationsFE/RealOptions.pdf