Most enterprises believe they’re executing well.
According to Highspot’s 2026 GTM Performance Gap Report, 96% of organizations say they’ve established a clear growth and revenue strategy. 98% say execution is standardized across the business. Yet only 53% report highly consistent outcomes over the past year.
At the same time, organizations are investing billions in AI to close that gap.
Some are seeing real returns. Most aren’t.
That split raises an interesting question: Why do some companies realize meaningful business value from AI, while others invest just as heavily and see little change at all?
AI magnifies what’s already there
When AI initiatives underperform, organizations often question the technology itself.
Did we choose the wrong model? The wrong vendor? Did we source unwisely?
The data suggests something else.
Gartner® reports that, “In a May 2025 Gartner survey of 506 CIOs and other technology leaders, 72% of CIOs reported that their organizations are breaking even or are losing money on their AI investments.”
Highspot’s own research found that 76% of GTM leaders say AI adoption is outpacing their operating model.
AI doesn’t create value on its own. If execution is inconsistent, AI scales that inconsistency faster. If execution is uneven, AI accelerates that too. The result is faster decisions built on the same inconsistent foundation, but at a greater scale.
That reframes the real question: What determines whether AI has something worth amplifying in the first place?
Not all GTM systems are built to perform
The organizations seeing the greatest return from AI are making different architectural decisions about how AI connects enterprise knowledge, guides execution, and learns from outcomes. A strong GTM AI architecture does three things consistently:
- Connects signals across the revenue lifecycle, so AI is working from what happened in the business, not a fragment of it.
- Guides execution in the moment, surfacing what a seller, manager, or leader should do next, not just what happened last quarter.
- Learns from outcomes, so guidance gets sharper over time instead of running the same static playbook indefinitely.
Without that foundation, AI becomes another disconnected tool.
Highspot’s research backs this up directly: 85% of leaders say they now have more performance data than they know how to use. More AI, layered onto a system that isn’t built to perform, just produces more data nobody can act on.
Execution is the differentiator
This is where the gap closes or widens. AI’s job is to help revenue teams take the right action, consistently, at the moment it matters.
True business value comes from consistently executing the behaviors that already win. It comes from the messaging that resonates, the deal risk caught early, the coaching that changes what a rep does next.
The highest-performing GTM AI architectures generate insight, operationalize it, and embed it directly into how sellers work rather than leaving it in a dashboard someone has to remember to check.
That’s what turns AI into something teams can act on.
What the gap is costing
The financial stakes of getting this wrong are concrete. In a $500 million business, a 5% forecast miss represents a $25 million unexpected shortfall or overcommitment. This is a direct consequence of teams executing inconsistently across regions, managers, and deals.
The upside is just as real.
Our GTM Performance Gap Report found that organizations believe they could improve performance by an average of 8.5% within 12 months through more consistent execution.
For a $1 billion business, that’s roughly $85 million in recoverable value.
And it’s not from more AI, but from a system built to turn the AI they already have into something worth trusting.
Where to go from here
If AI investment alone isn’t enough, what does a high-performing GTM AI architecture look like? And how does an organization decide what to build, what to buy, and how to bring both together?
Our guide, Build, Buy, or Both? How to Make AI Work for Modern GTM, answers both of the questions and shares a framework for deciding what to build, what to buy, and how the two work together.
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Gartner Press Release, Gartner Survey Finds All IT Work Will Involve AI by 2030; Organizations Must Navigate AI Readiness and Human Readiness to Find, Capture and Sustain Value, October 20, 2025 GARTNER is a trademark of Gartner, Inc. and/or its affiliates.
