Data Finds Seven in Ten Revenue Leaders Now Let AI Inform Business Decisions.

Somewhere between the pilot programs of 2023 and today, a threshold got crossed with very little ceremony. According to Gong's State of Revenue AI report, seven in ten enterprise revenue leaders now trust AI to regularly inform their business decisions, with only 10% expressing skepticism. The number of US companies using AI to forecast and measure strategic success jumped 50% in a single year. Forecasts, territory plans, deal strategies, and resource calls that used to live entirely in human judgment now run through models first.

That trust may be earned. Gong's data ties regular AI use to 77% more revenue per rep, and there is no serious argument left that revenue teams should keep AI out of the decision loop. The question nobody seems eager to answer is different, and it is the one that will define the next two years of revenue leadership: when an AI-informed decision goes wrong, whose name is on it?

The accountability structures never got built

Trust arrived faster than governance did, and the gap is documented. Forrester's 2026 B2B predictions project that ungoverned generative AI will cost B2B companies more than $10 billion in enterprise value this year through incidents that end in stock declines, settlements, and fines. More pointed is the firm's finding about why: companies are applying governance built for internally developed software, review boards and top-down policy, to commercial AI tools that go-to-market teams adopt on their own, and those controls simply do not reach the places where the decisions are actually being made.

Consider what that means in practice. A forecast gets adjusted because the model flagged risk. A rep gets pulled off an account because the scoring engine ranked it low. A discount gets approved because the pricing recommendation said the deal would slip without it. Each of those is a business decision with a paper trail that increasingly reads "the system suggested it." In most organizations, nobody has written down whether the human who accepted the suggestion, the leader who deployed the tool, or the vendor who built it owns the outcome. Ambiguity like that is comfortable right up until the quarter it becomes expensive.

The trust is also strangely selective

Here is the part that should give every board member pause. The same research ecosystem documenting leadership's growing faith in AI also documents the front line's growing doubt. Gartner predicts AI agents will outnumber human sellers ten to one by 2028, yet expects fewer than 40% of sellers to report those agents improved their productivity. Buyers are split nearly down the middle on whether AI or a sales rep is more likely to mislead them, with 51% saying AI.

So the people using the output daily and the people receiving it downstream are both hedging, while the people furthest from the output are the most confident in it. There is a familiar organizational shape to that arrangement, and it is not one with a happy history. Executive conviction paired with front-line skepticism usually means the risk is being carried by whoever is closest to the work and least able to decline it.

Governance is the answer to the firing question

None of this argues for pulling AI out of the decision loop. The competitive math is settled, and the leaders trusting these systems are mostly right to. The argument is that trust without ownership is not a strategy, it is a liability with a delay on it, and the fix is the unglamorous work Forrester keeps prescribing: named owners for AI-informed decisions in each function, accuracy standards on the outputs that feed forecasts and pricing, and what the firm calls raising the AI intelligence quotient of the team, meaning the skill to recognize when the model is wrong before acting on it. Sharyn Leaver, Forrester's chief research officer, has argued that "accountability and clarity will be the cornerstones of competitive advantage" for B2B leaders, which is a polite way of saying the companies that can answer the firing question in advance will beat the ones improvising it after the incident.

There is a simple test for whether your organization is ready for the trust it has already extended. Pick the last significant decision your team made with AI input, a forecast call, a territory change, a pricing move, and ask out loud who owns it if it turns out badly. If the room goes quiet, the seven in ten stat is not a sign of maturity. It is a bill that has not arrived yet.