The Myth That Spending More on AI Puts You Ahead

TLDR: The data is in: spending more on AI does not separate winners from the pack. What does? Who owns the results and whether anyone is actually measuring them. A Harvard Business Review survey of 1,006 executives found that companies where the CFO owns AI accountability are 43% more likely to report high value than those where the CIO or CTO leads.

The myth

Sixty-eight percent of CFOs plan to increase AI and digital spending this year. That’s the highest figure in 21 quarters of Grant Thornton’s survey tracking. Over 75% of CIOs expect significant AI investment by the end of 2026, according to Info-Tech Research Group’s CIO Priorities report published March 18.

The assumption behind all this spending is simple: more budget, more advantage. If we outspend, we outperform. I hear this logic in nearly every board conversation I sit in on. AI is the competitive lever of the decade, so the companies that invest the most will pull ahead the fastest.

It sounds right. It also happens to be wrong.

Why it sounds right

The AI market is genuinely enormous. Spending is projected to hit $2.52 trillion this year, a 44% year-over-year increase according to Gartner’s 2026 Trend Report. When that much capital is moving, it’s natural to assume that being on the right side of it means spending aggressively. And there’s a grain of truth here: companies that invest nothing are falling behind.

The problem is the leap from “investment is necessary” to “more investment is better.” That’s where the logic breaks.

At Series B scale, this belief is particularly seductive. There’s real pressure to show operational maturity to investors and enterprise customers. So teams add tools. They buy platforms. They stack agent frameworks on top of existing workflows. The spend goes up. The dashboard gets busier. And somewhere in the noise, the question nobody asks out loud: is any of this actually producing results?

What the evidence says

Harvard Business Review published a survey of 1,006 global senior executives in March, and the findings should give every scaling company pause. Forty-five percent report getting a “great deal” of value from AI. Another 45% say “moderate.” The majority of companies are investing heavily and landing in the same middling range. Spending more did not separate them.

Two things did.

First, ownership structure. Companies where the CFO is accountable for AI value are dramatically more likely to succeed: 76% achieve high value when the CFO leads, compared to 53% when CIOs or CTOs are in charge. That’s a 23-percentage-point gap, and it has nothing to do with budget size. It has everything to do with who is asking “what did we get for this?”

Second, measurement maturity. The HBR survey categorized companies into stages from 0 (unmeasured pilots) to 5 (formal value reporting). At Stage 5, companies achieve high value 85% of the time. At Stage 0? Four percent. The difference between those two groups is not how much they spent. It’s whether anyone was keeping score.

One more finding challenges the spending narrative. Only 9% of executives report generative AI as their most valuable AI investment. Fifty percent point to analytical AI, and 40% say traditional rule-based systems deliver the most. The companies pouring the most into generative AI tools may be investing in the flashiest category while the real returns sit in work that never makes the keynote.

The agentic AI data is worth noting: adopters are 22% more likely to report high value. But once again, the differentiator is not spending on agents. It’s having the governance and measurement infrastructure to make agents productive.

Info-Tech Research Group’s CIO Priorities report underscores a related gap. Enterprise architecture importance was rated 8.7 out of 10, while effectiveness scored just 6.3. Data governance showed a 2.8-point gap between importance and effectiveness. Companies know what matters. They just haven’t built the scaffolding to make the spending count.

Note on sourcing: a temporary service disruption prevented me from accessing the full source texts during this research session, so I cannot provide a verbatim quote with the exact original wording. The statistics above are drawn from detailed research summaries. I would rather be transparent about that than present a fabricated quote.

The reframe

The competitive edge in AI is not a spending problem. It’s an ownership and measurement problem. The companies pulling ahead are the ones where someone with P&L responsibility asks: what value did we get, specifically, from this investment? And they have a system for answering that question honestly.

For Series B companies, this reframe is freeing. If your competitor raised a bigger round and is spending more on AI, that probably feels threatening. But the data says their budget advantage means very little unless they also have CFO-level accountability and honest measurement. Most don’t.

The better mental model: AI spending is table stakes. Everyone is spending. The differentiator is the feedback loop between investment and evidence of value. Think of it less like an arms race and more like a fitness regimen. The person who shows up at the gym every day and tracks their progress will outperform the person who buys the most expensive equipment and never logs a workout. The budget is the equipment. The accountability structure is the habit.

So what

Here’s what I’d say to a Series B founder over coffee this week: stop comparing AI budgets. Start comparing AI accountability structures.

Three questions before the next board meeting. Who owns AI value measurement at your company, by name? What’s the reporting cadence, and does it connect to actual revenue outcomes? And which of the current AI tools have produced measurable results in the last 90 days? If the answer to that last one is “I’m not sure,” that tells you exactly where the work is.

The answers to those three questions will tell you more about competitive position than any spending benchmark ever will. And getting them right costs nothing.