AI adoption is a bit like flossing, says David McCarthy – it’s great in theory, but we don’t always do what’s good for us.
I’ve spent an embarrassing amount of time in boardrooms where AI is the hottest topic in the room, second only to whether we’re ordering pastries or sandwiches for lunch. It starts the same way every time: a well-dressed executive clicks through slides about AI transformation, efficiency gains and the impending revolution of work. The nodding is enthusiastic. The investment is inevitable.
And yet, somewhere between the second Q&A and the fifth buzzword about ‘unlocking productivity’, I can already tell how this story ends: nobody is actually going to use the tools they’re about to buy.
I know this because I see it every day. AI adoption is one of those things like flossing or turning off the immersion – everyone nods along in agreement, but bad habits die hard. Companies assume the problem is education: if we just train people more, they’ll use AI! But that’s not how this works.
The real problem? Employees aren’t adopting AI because they don’t see what’s in it for them.
Most corporate technology roll-outs are pitched as gamechangers. To employees, they feel more like gym memberships – sounds great in theory, rarely used in practice. They nod, smile and ignore it. Unless, of course, it helps them get ahead.
I was working with a marketing team at a major global brand – one of those companies where executives say ‘learnings’ unironically. They were launching an AI initiative that sounded great on paper: streamline workflows, personalise content, optimise spend. The usual suspects.
But after an hour of corporate buzzwords, we broke for coffee. And that’s when the most important insight of the day happened – not in the boardroom, but in the hallway.
One of the marketing leaders turned to me and said, “I don’t care about automation. I care about getting a bigger budget.”
That’s when it clicked. She wasn’t resisting AI because she didn’t understand it; she was resisting it because nobody had explained how it would help her.
This is the real issue with AI adoption: we’re telling employees that AI is a job skill, when it’s a career tool.
Why companies keep getting AI adoption wrong
The current consensus around AI adoption focuses on technical enablement: launch an internal training program, introduce a generative AI policy, ensure compliance. But this assumes the barrier is complexity. It’s not. The tools themselves are simple. The real issue? Employees don’t see a direct reason to use them.
This isn’t a skills problem. It’s a motivation problem.
Employees don’t adopt AI because it’s available – they adopt it when it directly helps them achieve their goals. And in a corporate environment, those goals tend to be: doing more with less (ie making daily work more efficient) and making better strategic decisions (ie providing leadership with sharper insights).
Companies assume that training alone will drive adoption. It won’t. The real unlock is helping employees understand how AI can accelerate their careers – whether that’s through proving increased efficiency, improved decision-making or greater visibility with leadership.
From task enabler to career accelerator
Let’s take a social media manager – someone responsible for keeping a brand’s voice sharp across platforms, juggling agency partners and ‘maximising content performance’.
AI can help them write captions faster, schedule posts more efficiently and generate variations at scale. But that’s not how they’ll get promoted.
The real career accelerator? Communicating how this will help their team make a better budget decision.
Instead of simply generating more content, we started thinking about how AI could help them:
- Analyse performance patterns: Should we continue investing in high-end video production or does lower-cost, creator-led content deliver the same impact at a fraction of the cost?
- Optimise budget allocation: Are we spending too much on paid amplification when organic engagement signals indicate a shift in content preferences?
- Demonstrate return on investment (ROI) to leadership: Can we correlate content performance directly with audience behaviour shifts to justify increased budget allocations?
Imagine this conversation happening in a monthly marketing performance review: ‘Over the past six months, our second-best performing content format – short-form, creator-driven videos – has delivered 85pc of the engagement at 40pc of the production cost. By reallocating just 20pc of our spend, we could maintain performance while freeing up a budget for this other project.’
Suddenly, this social media manager isn’t just executing campaigns – they’re steering the conversation about marketing efficiency and strategic investment.
And in a world where visibility and impact drive career growth, AI is no longer just an automation tool – it’s a leverage tool.
The AI adoption playbook: two simple frameworks
If you’re trying to drive AI adoption inside your company, forget about extensive training modules and start with two simple questions: Can this tool help me do more in the same amount of time? And can this tool help me provide better insights to leadership?
These are the two primary use cases for AI adoption today. Everything else – policy frameworks, governance models, vendor selections – are secondary.
And if you’re an employee trying to figure out how to future-proof your career in an AI-driven world, the best question you can ask isn’t ‘How do I use AI?’, it’s ‘How do I use AI to make my manager’s job easier?’
Because if you’re making your manager’s job easier, you’re making yourself indispensable.
David McCarthy is an Irish technology executive living in New York, advising Fortune 100 brands on AI adoption and digital strategy. His work has shaped AI transformation at some of the world’s most influential companies.
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