How to Evaluate AI Opportunities: A Framework for Leaders

A CFO at a mid-size financial services firm sat in a zoom coaching session with me last month, with three AI vendor proposals on her desk. “I’ve evaluated hundreds of business cases in my career,” she said. “I know how to assess capital expenditure requests, M&A targets, technology migrations. But these AI proposals? I have no framework. I’m basically going on whether it sounds promising.”

She’s not alone. Most executives have sophisticated evaluation methodologies for every major business decision except one: AI opportunities. And that gap is costing them – in wasted resources, damaged credibility, and missed genuine opportunities.

The problem isn’t that executives lack intelligence. It’s that the standard evaluation questions – “What’s the ROI? What’s the timeline? What are the risks?” – assume you can reliably answer them. With AI initiatives, you often can’t. The technology is too new, the vendor claims too optimistic, and the implementation variables too numerous.

What executives need isn’t more data. They need different questions.

The Wrong Question Everyone’s Asking

Most AI fluency for executives content focuses on understanding what AI can do. That’s table stakes. The harder skill is evaluating which AI opportunities deserve your attention in the first place.

“Will AI work?” is the wrong question. The right question is: “Will THIS AI opportunity create value in MY specific context?”

The distinction matters because AI is not a single technology with predictable outcomes. It’s a family of capabilities that range from mature (natural language processing, image recognition) to experimental (general reasoning, complex decision-making). Vendor demos show the best case. Your reality will show the average case – or worse.

AI vendors sell possibilities. Your job is evaluating probabilities – specifically, the probability that this particular initiative creates value in your particular situation.

Most AI evaluation advice on the internet is written for investors buying stocks. They’re asking “Is this AI company a good investment?” You’re asking something fundamentally different: “Does this AI initiative deserve my organization’s attention and my credibility?”

Those require entirely different frameworks.

Three Filters for Executive AI Evaluation

You don’t need deep technical knowledge to evaluate AI opportunities effectively. You need structured skepticism and the right questions. These three filters can be applied in any order, and if an opportunity fails any one of them, it deserves serious scrutiny before proceeding.

Filter 1: Problem Fit

Does this solve a problem we actually have, or a problem the vendor wishes we had?

The first filter is deceptively simple: Can you articulate the specific business problem this AI initiative solves, in one sentence, without using the word “AI”?

If you can’t, that’s a red flag. Many AI proposals are solutions searching for problems – technically impressive capabilities that don’t map to actual pain points in your operation.

Consider a CMO evaluating AI-powered content generation tools. The vendor’s pitch centers on “scaling content production 10x.” But the CMO’s actual problem might not be content volume – it might be content relevance, or distribution efficiency, or measurement accuracy. A 10x increase in irrelevant content doesn’t solve anything.

Questions to ask:

• What specific workflow or decision does this improve?
• What’s the cost of the current approach (in time, money, or quality)?
• Why hasn’t this problem been solved by non-AI approaches?

Filter 2: Human-AI Handoff

What stays human? What becomes AI? And is that the right division?

This filter draws on the distinction between tasks and purpose that the PURPOSE AUDIT™ framework explores. AI excels at tasks – repeatable, definable, data-driven activities. It struggles with purpose – strategic judgment, stakeholder navigation, meaning-making in ambiguous situations.

A COO evaluating predictive maintenance AI for fleet management should ask: What decisions will humans still make after this is implemented? If the answer is “none – the AI handles everything,” that’s a warning sign. If the answer is “humans will still decide how to prioritize repairs when multiple vehicles need attention simultaneously, and how to communicate delays to customers,” that’s a healthier human-AI handoff.

The best AI implementations amplify human judgment rather than eliminate it. They handle the data processing so humans can focus on the interpretation.

The question isn’t whether AI can do the work. It’s whether the work AI can do is the work that actually matters.

Questions to ask:

• What human judgment does this free up, versus what human judgment does it replace?
• Who maintains accountability for outcomes?
• What happens when the AI is wrong?

Filter 3: Failure Mode Analysis

What happens when this breaks? And it will break.

Every AI system fails. The question is how it fails, and whether your organization can absorb those failures.

This filter is where executives with operational experience have a real advantage. You’ve seen technology implementations go wrong. You know that pilots succeed because they get extra attention, and production implementations fail because they don’t. You understand that edge cases multiply as scale increases.

A CFO evaluating AI for accounts receivable automation should ask: What happens when the AI misclassifies a payment from a major customer? What’s the escalation path? Who notices the error? How long until it’s corrected? What’s the relationship cost?

Klarna’s over-automation offers a cautionary tale. The company eliminated 700 customer service roles, only to find that the efficiency gains came with customer satisfaction costs that forced partial reversals. They optimized for one metric while ignoring the failure modes.

Questions to ask:

• Who maintains this system long-term?
• What’s the training data, and does it reflect our actual business conditions?
• What’s the error rate the vendor acknowledges, and what’s the error rate we can tolerate?
• What’s the opportunity cost of focusing on this versus other priorities?

Red Flags That Signal Hype Over Substance

Beyond the three filters, certain warning signs should trigger deeper scrutiny – or immediate skepticism.

The broader evidence on AI implementation success rates is sobering. According to MIT research, AI project failure rates show that 95% of generative AI pilots fail to deliver measurable P&L impact. Only 5% achieve rapid revenue acceleration. Meanwhile, S&P Global data reveals that 42% of companies abandoned most AI initiatives in 2025, up sharply from 17% the year before – with the average organization scrapping 46% of proof-of-concepts before production.

These numbers aren’t reasons to avoid AI entirely. They’re reasons to evaluate more carefully.

Red flags to watch for:

The vendor can’t explain what happens when the AI makes mistakes. If the failure mode question gets deflected or minimized, the vendor either hasn’t thought it through or doesn’t want you thinking about it.

The ROI projections assume best-case adoption. Most AI implementations experience slower-than-projected adoption, more-than-expected maintenance, and less-than-promised accuracy in production. Vendors who don’t acknowledge this are selling a fantasy.

The demo uses curated data rather than your data. AI demos are designed to impress. Ask to see the system perform against your actual edge cases, your actual data quality issues, your actual business rules.

The implementation timeline ignores integration complexity. Getting AI working in isolation is relatively easy. Getting it working within your existing systems, processes, and governance requirements is where timelines explode.

A polished demo is evidence that a vendor knows how to build demos. It’s not evidence that the solution will work in your environment.

Questions to Ask Before Committing Resources

Effective AI evaluation isn’t about becoming technical. It’s about asking questions that reveal whether the technical team – internal or vendor – has thought through the real challenges.

Questions for vendors:

• What’s the typical time from pilot to production deployment with clients like us?
• What’s the most common reason implementations fail or get abandoned?
• Can we speak with a reference client who’s been using this in production for more than six months?
• What training data was this built on, and how does it get updated?

Questions for your technical team:

• What’s our maintenance burden for this system ongoing?
• Do we have the internal capability to troubleshoot when this fails?
• What happens to this investment if the vendor changes their model or pricing?

Questions for yourself:

• Does this align with where I’m taking my function?
• What’s the opportunity cost of focusing on this versus other priorities?
• Am I championing this because I’ve evaluated it carefully, or because I want to seem forward-thinking?

Executives who develop strong strategic decision-making capabilities bring the same rigor to AI evaluation that they bring to any major business decision. The technology is new, but the discipline of structured evaluation isn’t.

When to Say No (And How to Say It)

Saying no to an AI initiative doesn’t make you a Luddite. It makes you a leader who knows the difference between hype and substance.

The challenge is saying no without damaging relationships or appearing obstructionist. A few principles help:

Frame rejection as prioritization, not opposition. “This doesn’t fit our current priorities” is different from “This won’t work.” The first keeps doors open; the second creates defensiveness.

Distinguish “not now” from “not ever.” Some AI opportunities are premature – the technology isn’t mature enough, your data infrastructure isn’t ready, or other priorities demand attention. Others are genuinely poor fits. Be clear which category you’re in.

Ask for conditions under which you’d say yes. “I’d be more interested if we had better data quality in this area” or “Let’s revisit this once the pilot at Company X has six months of production data” turns rejection into a constructive path forward.

The executives who navigate AI effectively aren’t the ones who say yes to everything. They’re the ones who know when to say yes, when to say not yet, and when to say no – and can articulate why.

The Competency Behind the Framework

Effective AI evaluation isn’t a one-time skill. It’s an ongoing competency that executives need to develop and maintain as the technology evolves.

This evaluation capability sits within a broader set of AI fluencies that executives need. The AI FLUENCY MAP™ framework identifies five distinct competencies, of which evaluation is one. Where are your other gaps?

If you’re evaluating AI not just for organizational efficiency but for how it changes your own role, the Transform path offers a framework for thinking about role evolution in an AI-augmented environment.

The good news: you don’t need to become an AI expert to evaluate AI well. You need to apply the same structured thinking that’s served you throughout your career – just with new questions and new awareness of where vendor optimism outpaces reality.

The AI opportunity landscape will keep growing more complex. Your ability to separate signal from noise – hype from substance – will determine whether AI becomes an asset or a distraction.