Stop Leaning Into AI. Start Leaning Into Outcomes.
Every generation of business technology produces the same exhortation. Lean into social. Lean into content. Lean into the internet. Lean into spreadsheets. Now it’s AI’s turn.
And like every cycle before it, the people shouting loudest about the tool might just be the ones least clear about what they want it to do.
The problem isn’t enthusiasm. It’s myopia. When you fixate on the input‚ tool, process or platform‚ you lose sight of the output. More importantly, you’re encouraging others to lose sight of the output, too. And the outcome is what matters most. Not whether you used AI to get there, but whether you got there at all.
That myopia shows up differently depending on where you sit.
The leader who mandates adoption
When a CEO or senior leader tells their people to “lean into AI” – let’s call them Token Max – what they’re really doing is outsourcing strategy to machine that, it could be said, is merely supremely confident about opinions. It sounds progressive. It fills a slide. But it says nothing about what good looks like. If you can’t articulate the outcome you want - faster decisions, sharper analysis, fewer wasted hours, happy customers, X points more margin – then telling people to use AI is just telling them to ‘act busy’ in a new way.
The best leaders don’t ask “are you using AI?” They ask “are you getting better results?” and leave the method to the people doing the work.
The employee who performs adoption
On the receiving end, the pressure to “lean into AI” creates a perverse incentive: demonstrate that you’re using the tool, regardless of whether it’s helping. So paste things into ChatGPT and copy them back out again. Generate reams of content or plans that nobody reads. They automate steps that didn’t need automating.
The appearance of AI usage becomes a proxy for competence, and an excuse for not pursuing the actual outcome‚ the real work. The employee who picks up the phone and solves the problem in five minutes looks less innovative than the one who spent an hour prompt-engineering their way to a mediocre first draft.
The product team that builds for the feature
Product developers can be particularly susceptible to AI myopia because the technology is genuinely exciting to build with. The temptation is to start with “what can we do with AI?” rather than “what problem are we solving?”
You end up with AI-powered features that exist because they can, not because they should. The chatbot nobody asked for. The recommendation engine that recommends things people were already going to buy. Every feature that starts with the technology and works backwards to the user need is a feature that’s solving the wrong problem fluently.
The marketer who does more
Marketers have been here before‚ arguably more than anyone. They leaned into social and we got vanity metrics. Leaned into content and we got slop (long before ChatGPT, I might add). Leaned into programmatic and got ad fraud.
Now they’re leaning into AI-generated content and we’re getting... more content. The pattern repeats: the tool and the process becomes the strategy, volume replaces value, and nobody stops to ask whether any of it is moving the number that matters. Give a marketer a clear outcome‚ and they can figure out whether AI helps them get there. A marketer told to “use AI more” will produce more stuff, faster.
The consultant who sells the transition
Consultancies have a structural incentive to keep the focus on the tool because it’s what they’re selling. AI readiness assessments. Transformation roadmaps. Maturity models. All of it oriented around adoption rather than impact. The question is never “what does your business need to achieve in the next twelve months?”‚ it’s “how AI-ready are you?”
The myopia is the message
The common thread across all of these scenarios is the same: a focus on the mechanism at the expense of the meaning. And it’s not a new disease‚ it flares up every time a new technology arrives and makes people feel like they need to be seen doing something with it. Like they might miss out.
The fix isn’t complicated. It’s just unfashionable. Start with the outcome. What are you trying to achieve? What does good look like? What’s the fastest, most reliable route to getting there? If the answer involves AI, great. If it involves a spreadsheet, a phone call, an abacus, or a conversation over coffee, then that’s great too.
The more important effect of reminding - and keeping - people focused on outcomes is that they spend more of their time being focused on outcomes. Whether it’s a more efficient version of the same outcome you currently create, or a new way of delivering the outcomes your customers seek, that’s always the mission, isn’t it?
The people who got the most out of every previous technology shift weren’t the ones who leaned into the tool. They were the ones who knew what they were aiming at‚ and used the tool as a better way to get there.