Companies Measure AI Wrongly for Customer Value

More money went into AI infrastructure in the last three years than was spent building the interstate highway system over four decades.

When I asked a former CEO and friend what he thought CEOs were actually losing sleep over these days, no surprise, his list came down to five questions, all revolving around AI: profitability, core strategy, leadership AI literacy, build/buy/partner decisions, and measuring impact.

In finding answers to those five questions, I discovered a sixth question that lies beneath them all: How can AI make your company more valuable to your customers?

I guess I’m not alone. New research from Epsilon and Forrester shows that most companies are approaching AI integration the wrong way without realizing it.

Most CEOs are asking how AI can make them more valuable, rather than how AI can make them more valuable to their customers.

Making your company more valuable to customers actually solves the first problem. Epsilon’s newly published 2026 benchmark study, a survey of 257 marketing decision-makers conducted with Fuld Inc., supports this.

Seventy-one percent said their primary use of AI is productivity and efficiency, aka doing their own job faster. Only 9% said revenue generation.

Yet when the same marketers were asked how they actually measure AI’s performance, 46% pointed to revenue. Umm… seems like companies are using AI to improve their workflows, then grading it against a scorecard built for customer value.

It’s like buying a dishwasher and then measuring whether using it has driven down your property taxes. That mismatch is the real story of AI in business right now. It’s why marketing, not operations or finance, is where the AI disruption is truly landing.

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Marketing owns AI’s impact on customer value

In recent decades, marketing ran on a simple mechanic: buy attention, awareness, interest, desire, and action, mostly at the bottom of the funnel using paid digital tools.

AI answer engines break that mechanic. You can’t buy your way into an AI answer the way you bought a search results page. You have to earn it through reputational (brand meaning) proof that an AI system deems worthy of citing.

Marketers already sense this: GEO (AI-powered search optimization) is now the most widely used AI tool in marketing, with 54% adoption, edging out conversational AI and data analysis.

Content-generation tools, the No. 2 AI tool just a year ago, didn’t even crack this year’s top 10. If you believe a company’s value is directly linked to creating appreciative customers, then you’re likely on the right path to AI integration.

Indeed, it’s the lens through which I answered the five questions below.

1. How can AI improve my profitability?

First, from an operational perspective. Delta CEO Ed Bastian has said AI could lift the airline’s profitability by roughly 50% over time, moving operating margin from about 10% to 15% through better pricing and scheduling. To Bastian’s credit, he’s talking about AI in service to a better customer experience.

Second, and mostly ignored: How do you keep AI from taking your profitability?

• Gartner projected traditional search traffic will drop 25% by 2026 as AI absorbs product queries.
• Adobe found AI referral traffic to U.S. retail sites grew 693% year-over-year during the 2025 holiday season, converting 31% better than non-AI traffic. (Big stats. Double-checked.)
• Forrester‘s research finds that firms with high AI use report meaningful productivity gains, but cost savings of under 10% and revenue gains of under 5%.

Proof that AI usage and AI-driven revenue remain myopically separated from the power of customer experience.

The big point: If your brand isn’t visible inside the AI answer, none of that productivity converts to revenue.

2. How can AI enhance my core business strategy?

Depends which strategy you already have. A.G. Lafley and Roger Martin’s Playing to Win framework draws a clear line. Are you playing to win, or playing not to lose? The former suggests you have a powerful strategy. The latter suggests you’re playing it safe.

In that case, AI can help you shrink less fast and help you play better defense. But that’s not strategic leadership. If you’re playing to win, the job isn’t finding a new strategy. It’s finding where AI bears the most fruit for the one you already believe in.

The big point: Treating AI as a strategy substitute rather than an amplifier of a winning strategy is just expensive automation theater.

3. How do I get my leadership team AI-literate?

Short answer: Make them use it every day.

The longer answer: Find someone who understands the technology and can find where it works synergistically with your strategy. Be careful, since feeding proprietary data into public AI tools can make it effectively public.

Most AI literacy conversations miss one important fact: What looks like a training gap is actually a top-down perception gap. Epsilon found 67% of C-level marketers rate their organization extremely mature in AI, compared to 33% of senior managers actually doing the work.

Leadership calls their AI tools extremely valuable at 73%, compared with 25% among senior managers. Epsilon’s own recommendation for this exact gap: “get leadership to start looking at the same scoreboard” as their teams.

The big point: Confidence at the top is running well ahead of what practitioners see on the ground. Literacy programs built to fix a skills gap are ignoring this.

4. Should I build, buy, or partner for my AI usage?

This leans more toward marketing than most CEOs expect, because it’s a question of differentiation. Menlo Ventures found 76% of AI use cases are already being purchased rather than built, so most companies have already answered this by default.

Seems to me the move is to buy the common stuff, then build or partner only where it touches something proprietary.

Epsilon’s own researchers reach the same conclusion from a different angle: With 100% of marketers now using AI, “simply ‘using AI’ doesn’t earn you an advantage anymore.”

Besides, as with any tool, the differentiator isn’t the tool itself. It’s what it’s pointed at. Using AI to mass-produce your marketing content is the opposite of differentiation. It’s a blend-in strategy hiding in the clothing of efficiency.

If your content output reads like your competitors’ AI output, you’ve automated your way onto what readers of my articles here will know as the Plateau of Indifference.

The big point: Judge every build/buy/partner decision by one question: Does this make us more distinct, or just cheaper at looking like everyone else?

5. How do I measure AI’s impact on my business?

Most companies measure AI activity instead of AI impact. Epsilon’s data shows marketers already default to revenue as their top measurement lens (46%, ahead of time savings at 36% and cost savings at 16%), so the instinct is there.

The problem is what those measurements reflect: an AI use case chosen for internal productivity rather than customer value.

For marketing specifically, the outcome that truly matters is AI visibility, because AI is indifferent to your media spend. (Oooh, there’s that plateau of indifference again.)

The big point: Measuring AI impact on internal AI optimization misses a huge factor that’ll only get huge-er. More customers equals more impact. Wasn’t it always thus?

The big through-line

Every one of these five questions runs into the same wall: A company can use AI constantly, measure it diligently, and still be optimizing for “are we better at this” instead of “are we more valuable to the people paying us.”

The data says most companies are doing the former and grading themselves on the latter without noticing the gap.

Use AI to help you get more customers by creating better, more appreciated experiences for them. That makes them feel more valued. That’s what every CEO should focus on because that’s where AI’s value will truly compound.