Your AI Metrics Might Not Work for GTM Messaging

Technology marketers are under serious pressure to promote their companies’ AI-powered models, tools, and platforms. And their marketing efforts must be working, because adoption seems to be increasing. A report from Stanford University shows that 88% of organizations have adopted AI.¹ Meanwhile, Gartner predicts that worldwide end-user spending on AI models and platforms will reach $64 billion in 2026, up 63.4% from 2025.²

While organizations might be accurately reporting their own adoption rates, some of the sales successes reported by AI companies might be inflated. In a Wall Street Journal opinion piece, Robert Pozen, a former Fidelity president, argued that AI sales figures have been driven, in part, by AI companies subsidizing the distribution of their AI tools.³

At the very least, adoption and sales numbers do not tell the whole story. An Adobe report suggests that while 89% of marketers plan to increase AI investment in the next one to two years, for example, only 7% have actually embedded AI in workflows and are delivering measurable impact.⁴

For go-to-market (GTM) professionals tasked with marketing AI solutions, it’s important to understand the limitations of adoption and sales metrics. When GTM teams use inflated claims to create a sense of urgency or to address perceived concerns about AI, they put their own credibility at risk. And when they use those metrics as their own baselines for success, they are setting themselves up for failure.

The Problems with Using Performance Benchmarks

Organizations are not only holding themselves responsible for hitting certain (inflated) sales and adoption numbers. They also believe they need to match or beat AI performance benchmarks from competitors, but those too are often flawed.

AI performance benchmarks don’t accurately predict how a product will perform in production—and buyers are catching on. They recognize that AI companies don’t know how every customer will use a particular model, so those performance claims are suspect.

Even without the implementation context, the benchmarks that vendors reference often have limited value. A model’s HumanEval score, for example, tells you roughly as much about its coding ability as a spelling test tells you about someone’s writing.

GTM teams aren’t expected to create new benchmarks or qualify every claim according to possible deployment scenarios. But they should avoid conflating benchmark performance with production performance. Teams that make claims in an effort to gain a competitive edge will lose credibility the moment a technical buyer pushes back.

Build Your Case with Business Benefits

If performance benchmark reports are flawed, how do you convince prospects of the value of your model or platform? Metrics remain useful, but those metrics must be tied to real business benefits—and preferably financial ones. Organizations need to know that a new AI tool will help them expand their margins, reduce costs, or deliver some other monetary value.

Consider focusing on the CFO’s point of view. They want to know whether a new tool will be a revenue generator or another sunk cost.

Rather than promoting dubious performance benchmarks, focus on highlighting these business—and financial—benefits. Re-evaluate your case study pipeline. Can each outcome be expressed in terms that will interest a CFO? Does the customer in the case study save more, spend less, or generate new monetizable value? If the answer requires a lot of nuance or context, the story is not ready for an enterprise buying conversation.

As you create your GTM strategy, emphasize quality over quantity. Technology buyers do not need more AI case studies filled with marketing fluff. They need proof that a product will deliver quantifiable outcomes. A few select wins with clear evidence of value will outshine mounds of vague or inflated claims.

Redefining GTM for the AI Era

How can your team deliver credible AI GTM messaging and avoid falling flat with overhyped claims? Three best practices can help in this new era:

• Highlight economic value instead of focusing on features. CFOs in particular want to see proof of value. Translate AI capabilities into business metrics that you can demonstrate. Can your platform save a team a certain number of hours every quarter by automating tasks? Has it helped generate more revenue for companies than they have spent on the platform? Does it cost less than other platforms to run?
• Drill down on automation savings. CFOs—and procurement teams—might hesitate when they see the price tag of AI solutions. To overcome objections, position your model or platform first and foremost as a cost-saving engine. Show that the costs are justified by the savings that customers are already achieving through AI-driven automation.
• Provide proof that champions can share. Make sure the CFO can make your case for you when they meet with colleagues and decision-makers. Your champions need assets that will clearly demonstrate the economic value of your platform.

Promoting over-the-top adoption statistics, sales metrics, or performance benchmark claims will only get you so far. The most successful GTM strategies will emphasize the quantifiable business benefits that your customers are achieving with your platform. Articulating value in business—and economic—terms will help win over the people who are ultimately responsible for AI investments.

1. Stanford University, Human-Centered Artificial Intelligence, The 2026 AI Index Report, June 2026
2. Gartner, Gartner Forecasts Worldwide AI Platforms and Models Market to Grow 63% in 2026, July 2026
3. Robert Pozen, Can Investors Trust AI Sales Figures? Wall Street Journal, May 2026
4. Adobe, The State of Marketing in an AI-Driven World, April 2026