The Global Intelligent Delusion: 5 takeaways shaping the future of enterprise AI
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The signal is clear. Leaders aren’t debating if AI will matter; they’re betting how soon it will impact the P&L. In our new report, The Global Intelligent Delusion, 77% of the 751 leaders surveyed say they expect new AI solutions to drive revenue within the next 12 months. That optimism is healthy, and it raises the bar for execution.
Here are five findings that stood out to me, plus my take on what they mean for enterprises looking to turn AI investments into measurable outcomes over the coming 12 months.
Top 5 takeaways
1. Expectations are racing ahead of capability
Leaders are leaning in; most expect AI to generate revenue soon. At the same time, 57% say expectations are outpacing their ability to deliver. That tension explains why pilots look promising yet stall at scale: the strategy-to-execution gap isn’t closing fast enough.
What it means: Scrutiny will shift from “Can we ship a model?” to “Can this change an outcome reliably, week after week?” Teams that tighten outcome ownership and learning loops will outpace those scaling proofs of concept.
2. Value is real, but unevenly distributed
Here’s a nuance that matters: 81% of organizations reported a positive ROI on AI in the past year. Yet returns are often concentrated in narrow use cases or teams with stronger product and data disciplines. The challenge now is less about proof and more about repeatability.
What it means: The frontier isn’t proving AI works; it’s making wins repeatable across business lines. Codifying decision points, guardrails, and measurement patterns will matter more than piling on new use cases.
3. The human bottleneck is product judgment
More than half of leaders (55%) say they won’t hit their AI objectives without stronger human capability, specifically in problem framing, outcome-focused design, and market integration. In short: access to models isn’t the blocker; applying them in ways customers and the business actually value is.
What it means: Invest in capability where decisions are made, i.e. product managers/owners and domain experts, so they can design experiments, interpret signals, and adjust guardrails responsibly.
4. Product management is stepping to the front, and fast
Leaders are voting with budgets: 87% increased investment in product management in the last 12 months, and 65% now say product management will be critical to company strategy in the year ahead, up from 26% the year before. That’s a dramatic shift, and it aligns with what we see in the field: when product leads the operating rhythm, AI moves from showcase to system.
What it means: Expect operating rhythms to look more product-led: clearer outcomes, tighter feedback cycles, and cross-functional ownership that spans strategy, data, engineering, and go-to-market.
5. The CPO is becoming a cornerstone of AI-era leadership
In the past year, 43% of organizations appointed a Chief Product Officer (up from 31% the prior year). That rise reflects a pragmatic reality: orchestrating AI across strategy, data, engineering, and customer value demands a leader accountable for the whole product system – not just technology acquisition.
What it means: Titles aside, the trend points to accountable ownership for AI outcomes at the top table. Companies that empower this role to set guardrails and enforce learning loops will scale value faster.
What this signals for enterprise AI strategy
The pattern is consistent: AI success looks like disciplined product leadership meeting ready data and teams that learn weekly.
The companies compounding value aren’t necessarily the ones with the most advanced models; they’re the ones with the clearest outcomes, the tightest feedback loops, and the courage to change how work is done when signals demand it.
Download The Global Intelligent Delusion report for the full picture, including where leaders see the biggest capability gaps, and how investment priorities are shifting. It’s a short read with the stats you’ll want at hand as you shape your 2026 plan.
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