Your buyers are stuck between AI activity and AI value. That's your opportunity.

A recent article in McKinsey Quarterly makes an uncomfortable observation about enterprise AI. In McKinsey's 2026 state of AI survey, 80 percent of respondents said AI had improved their individual productivity. Only 37 percent reported a positive impact on EBIT, and just 6 percent qualified as AI high performers.

The authors' explanation is simple. Most organisations have put AI tools in people's hands without changing the workflows, decision rights and roles around them. Nearly three-quarters of the high performers had substantially redesigned their workflows, compared with about a quarter of everyone else.

The research spans every industry and a very wide range of organisations, so I'm wary of reading too much into the numbers for any particular sales environment. But the pattern it describes will be familiar to anyone selling complex solutions into large organisations today.

Your buyers are living in that gap. Many of them have seen what AI can do for an individual. Most of them haven't yet worked out how to turn AI adoption into results their board will recognise. For sellers in complex B2B markets, that creates a particular kind of buying opportunity, and a particular kind of selling challenge.

Why this is unfamiliar territory

Most B2B purchases follow a path the buyer has walked before. They're replacing a system, renewing a contract or adding capacity, and they know roughly what good looks like.

Turning AI activity into enterprise value is different. The buyer can see that something needs to change, but nobody in the organisation has done it before. There's no previous project to learn from, no established budget line and often no obvious owner. McKinsey's readiness research found that while 70 percent of respondents felt personally ready for AI, only 27 percent of leaders thought their organisations were ready to make the shifts required.

That leaves the buyer facing three hard questions at once. What should we change? How should we decide? Who can we trust to help us?

These decisions are discretionary in an unusual way. In a growing number of organisations the board has already made it clear that something must be done about AI. In a recent BCG survey, around 60 percent of CEOs said their boards were rushing AI transformation, and nearly 40 percent said their boards lacked an informed view of how AI is reshaping growth strategy. BCG also found that board members who were less confident in their AI knowledge were more likely to think their organisations were moving too slowly.

So the pressure to act is often real, but the mandate rarely says what to do, where to start or how to prioritise competing investments. With tools already in use and individuals reporting gains, the easiest response is more of the same activity. The case for going further has to be built, and it has to survive scrutiny from finance, IT, HR, risk and the business units whose work would change.

The stakeholders carry personal risk as well. Redesigning workflows around AI touches roles, reporting lines and careers, possibly including their own. Anyone sponsoring the initiative is putting their reputation behind a decision they can't easily benchmark.

In this environment, the usual sales motions struggle. Product demonstrations answer a question the buyer isn't yet asking. Competitive comparisons assume the buyer has already decided what they're buying. Discovery questions built around known pain points miss the mark when the buyer can't yet describe the problem (or the opportunity) clearly.

What the buyer needs first is help making sense of the decision itself.

Helping the buyer make sense of the decision

Outcome-Centric Selling® is built around four questions every buying group has to answer before committing to significant change: why act, why now, why us and why trust? In familiar purchases, buyers often arrive with partial answers already in place. In this situation they usually have none, and the seller who helps them work through the questions earns a very different role in the decision.

Why act? The outcome gap here is the distance between what individuals are gaining from AI and what the organisation is actually getting from it, and the size of that gap shows where the greatest potential for systematic improvement lies. Help the buyer describe that gap in their own terms: which workflows still run the same way, where individual gains are absorbed without reaching the P&L, and what it costs to stay on the current path. If the buyer can't articulate this, nothing that follows will hold.

Why now? A board mandate creates pressure, but pressure isn't the same as urgency around a specific outcome. Urgency has to connect to something the business already cares about: a committed priority, a margin target, or competitors who are already redesigning their workflows. Part of the seller's job is to help the buyer turn a general instruction to "do something about AI" into a specific case for acting on this outcome first. The cost of delay is real but rarely visible, so it also has to be made visible without exaggeration.

Why us? This is where many AI-related sales conversations go wrong. If the buyer's problem is that tools haven't translated into value, another tool is unlikely to be the answer. The seller who can show how their approach helps redesign the work itself, including the workflows, decisions and roles around it, is differentiating on the thing the buyer lacks. That depends on knowing which options the buyer is really considering, including doing nothing or trying to build it internally.

Why trust? Sponsors of these initiatives are taking a career risk on a decision with few precedents. They need confidence that the outcome is achievable, that the seller will stay involved through implementation, and that they won't be left exposed if progress is slower than hoped.

McKinsey's article makes a parallel point about employees. When they trusted that their organisation would support them through AI-driven change this was associated with greater enterprise value at every stage of AI maturity the researchers measured. That finding concerns employers and employees rather than buyers and sellers, but the underlying logic carries over. People commit to change when they believe someone will support them through it.

The seller's credibility problem

There's an uncomfortable implication in all this. A seller who wants to guide buyers through AI-driven redesign needs to have started that journey themselves, and buyers will notice quickly if they haven't.

Most sales organisations sit on the same side of McKinsey's gap as their customers. Sellers use AI to research accounts, draft emails and summarise calls, but the way deals are run often looks much as it did three years ago. Three symptoms stand out.

The first is the handoff chain. Many sales processes still pass the customer from SDR to account executive to solutions engineer to customer success. At each step the buyer's reasons for acting get compressed or lost, which is the opposite of what an unfamiliar, cross-functional decision needs.

The second is where the time saved by AI goes. If it's spent sending more outreach, buyers experience more noise and no better help, and many will react against it.

The third is commercial judgement. Junior sellers have traditionally learned the craft through account research, call preparation and discovery work. As AI absorbs more of that work, it isn't obvious where their judgement will come from. McKinsey's authors warn that "today's productivity gain can become tomorrow's judgment gap", and sales teams are not exempt.

None of these problems is solved by buying more tools. Each calls for the same kind of redesign that sellers are asking their buyers to consider.

Three questions for sales leaders

If you lead a team selling into complex, discretionary decisions, these three questions are worth asking now.

• Where do the handoffs in your sales process dilute the outcome story your buyer needs to hear? Take a recent deal and compare what the buyer told you at the start with what your team was still saying about their outcome by the time they signed.
• If AI absorbs the work your junior sellers learned from, where will their commercial judgement come from? Practice, coaching and exposure to consequential decisions won't happen by accident. Someone has to design them in.
• Where is your team's AI productivity dividend going? If the answer is more activity, ask whether your buyers would rather have deeper help with the decision they're trying to make.

By the way, I want to acknowledge the help and support of my colleague Claude in writing this article. But the brief was mine, the final edits were mine, and I own the conclusions.

Outcome-Centric Selling® was designed for complex, discretionary decisions in which buyers are travelling an unfamiliar path. You can download the Mastering Outcome-Centric Selling® eBook here.