Co-authored by Gerard Pietrykiewicz and Achim Klor
A lot of AI adoption conversations start with the same question:
How much time does this save?
Not a bad question. But incomplete.
It assumes the main problem is speed. The work was already happening. The person was effective and just needed to go faster.
What it misses? The work that doesn’t happen at all.
For a GTM team, that looks like the competitive battlecard that’s been on the backlog for the last quarter. The customer story nobody has time to write. The post-mortem that doesn’t happen after a lost deal. The onboarding content that’s always months out of date.
The work is valuable. It just never gets started.
Kristina Khutsishvili, writing in LSE Business Review last February, argues that mainstream AI productivity frameworks, including OECD and IMF models, measure value almost entirely through time savings or quantity increases in output. She calls that a serious limitation. It says nothing about quality or novelty. And nothing at all about work that was blocked, avoided, or too painful to start.
Section’s 2026 AI Proficiency Report found that 85% of enterprise employees aren’t using AI to drive real business value. If time savings is the whole story, most of the investment is measuring the wrong thing.
The better question: what work is now possible that wasn’t before?
There’s a word we use for the weak parts of a draft: slop.
And slop is not exclusive to bad AI output. Humans are just as guilty.
The parts that are unclear, unfinished, or not yet honest. The structure that jumps too fast. The argument that sounds right but isn’t specific enough. The wording that’s polished but doesn’t sound like anyone actually said it.
The first AI draft surfaces all of that, even if you give it context and guardrails. It’s nowhere near the answer. It’s a diagnostic. Something to react to, disagree with, push back against. Once there’s a draft on the page, you can see exactly where the thinking is still soft. And the more critically you think, the more you can see.
That’s much harder when nothing exists yet.
In More Output, Less Thinking, we uncovered how AI removes the friction of producing something; how the danger shifts to producing the wrong thing confidently. The diagnostic only works if someone is still doing the critical thinking.
A Writer’s POV: Gerard wrote six articles in ten years. Not because he lacked ideas, but because writing in a second language from a blank page rarely happened. After building a workflow with AI, Notion, Manus, and n8n, he wrote twelve articles in twelve months. The simple process was faster. It just didn’t work.
AI makes it easier to start. It does not make judgment optional.
The teams getting into trouble aren’t the ones moving too slowly. They’re the ones who removed the human layer and called it efficiency. It’s worth repeating: delegation is not abdication.
HubSpot’s 2026 State of Marketing report notes that more content is now generated by AI than by humans, but most of it is average. Frequency without quality control is how you produce a lot of noise fast.
Frequency is not the win. Better work, done more consistently, is the win.
Our workflow, for example, doesn’t end with AI. Gerard and I read each draft and flag what doesn’t sound like us. We meet and debate about the actual point. The final version gets rewritten until it actually says something.
That’s where the work becomes worth reading. The judgment, the cuts, the rewrite: that’s the point. AI made it possible to get there. It didn’t do it on its own.
Note: We are transparent about how we use AI by adopting the Authenticity Commons Framework. You will always find the relevant marker at the end of every article.
Add these to your AI scorecard:
Where is valuable work not happening? Not because people are lazy. Because the first step is too hard. Think about which assets your go-to-market team keeps deprioritizing: the battlecard, the case study, the lost-deal debrief. That’s where AI’s leverage is highest, and it’s one of the core barriers leaders miss when adoption efforts focus on tools instead of friction.
Is frequency increasing? Are your reps publishing more relevant outreach? Is your team producing fresh proof points more than once a quarter? If the answer is no, ask why the first step is still too hard.
Is the thinking getting sharper, or just faster? AI surfaces the slop. What you do with it is still on you.
An efficient process that never happens is not productive.
An effective process that creates useful work is.
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Cheers!
Achim is a fractional CMO who helps B2B GTM teams with brand-building and AI adoption. Gerard is a seasoned project manager and executive coach helping teams deliver software that actually works.
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