# Why AI Isn’t Making Sales Teams More Productive — Yet - Forbes

*Источник: Forbes*
*Дата: 2026-07-14*
*Язык: en*

**Кратко:** I’ve spent decades studying what separates the highest-performing go-to-market organizations from the rest. What I’m seeing in this AI moment is the same mistake leaders make in every hype cycle: they add new tools before challenging how the work gets done.

I’ve spent decades studying what separates the highest-performing go-to-market organizations from the rest. What I’m seeing in this AI moment is the same mistake leaders make in every hype cycle: they add new tools before challenging how the work gets done. They make broken processes move faster, confuse more activity with more value, and call it productivity. That is why so much AI investment in sales is creating activity, but not revenue.
Measure Output, Not Activity
The starting point for AI in sales cannot be adoption. It has to be output. Art Harding, GVP, GTM Strategy, Performance and AI Operations at ClickUp, offered one of the best definitions I’ve heard. He says it comes down to four levers: contract value, deal count, win rate, or cycle time. That’s it. Every AI investment in go-to-market should trace back to one of those four. The two metrics that matter most are bookings per seller and bookings per total go-to-market headcount.
Harding is unsparing on this point: "Productivity is not a concept. It's a vocabulary definition. If you tell a CFO you've improved productivity, she's going to expect you to point to more sales for the same heads, or dramatically lower cost. So we're all 10x-ing ourselves. But does anyone out there have a seller doing $3 million a quarter instead of $1.5 million a year?"
The Coaching Trap
Once you define productivity as output, not activity, a lot of popular AI use cases start to look different. Coaching is one of them. Ask yourself: if your reps weren’t listening to their frontline manager, why would they listen to an AI coaching agent? AI coaching tools are only as powerful as the coachable talent beneath them. The real leverage goes beyond using AI to deliver more coaching. It is using AI to identify who is coachable in the first place, where they are stuck, and which interventions will actually help them improve.
The best managers are not administrators. They are talent acquisition and acceleration experts. If AI can absorb the administrative burden, managers can focus on people development and performance acceleration.
Give the Seller Back to Selling.
The same principle applies to sellers. AI should not pile more tasks onto their day. It should remove the work that keeps them away from customers.I recently heard a senior enterprise rep, exactly the kind of sales professional every organization wants more of, describe losing a day and a half prepping for a multimillion-dollar meeting: researching the account, building slides, hunting competitive intel. That's the wrong use of elite talent.
The first opportunity for AI is obvious: remove the preparation burden that keeps great sellers away from buyers. AI should be able to synthesize account history, surface buyer priorities, prepare meeting briefs, draft materials, identify risks, and recommend the next best move before a seller ever walks into the room.
But the bigger opportunity is not just before the meeting. It is inside the meeting itself. Real-time AI assistance during live negotiations, including in-moment pricing guidance, deal coaching mid-call, and competitive positioning surfaced on the fly is where the leverage becomes extraordinary. This gives the seller the information they need at the moment they need it, so they are not forced into the old fallback of, “Let me get back to you.”
The right question for every sales leader is simple: what can AI remove from the seller’s plate so they can spend more time in front of prospects selling.
The Reset Is Coming.
We are entering the trough of disillusionment with AI in sales. The first wave was full of excitement: more tools, more automation, and more coaching. But more is not the same as better. What needs to be identified is where AI actually improves sales performance.
That means forcing every AI investment through a tougher filter. Which parts of the sales process genuinely require a human? Which are legacy artifacts no one has questioned because they’ve always existed? Which investments can trace a straight line to one of the four output levers: higher average contract value, more transactions, better win rates, or shorter sales cycles?
Harding offered a great analogy: we’ve mass-produced food, but there’s less nutrition than ever. We’ve mass-produced messages, but communication has gotten worse. “Why are we all so excited about more software? It is a time for rethinking how we work differently from past software. It is time for 1st principle thinking not implementing SaaS memes.”
AI can help you produce more emails, generate more call summaries, and build more dashboards. But AI is an asymmetric amplifier. Put it in the hands of your best people and aim it at the few actions that actually generate revenue, and the gains can be extraordinary. Spread it indiscriminately across a broken process, and you will spend more money getting to the same place. Before your next AI investment, ask one question: are we amplifying excellence, or automating mediocrity?

[Оригинал](https://www.forbes.com/sites/keithferrazzi/2026/07/14/why-ai-isnt-making-sales-teams-more-productive---yet/)