Driving AI Adoption, From Resistance to Results

If you're reading this, it's likely that you've encountered people resisting the adoption of AI tools into their daily work or are struggling to gain AI literacy. Resistance to change is a natural human instinct, but in the AI era, clinging to the old way of doing things or ignoring AI literacy could be detrimental to your business.

Some of this resistance is down to lack of interest in diverting from the norm, some is reluctance to learn something new, and some to a genuine fear that AI will replace jobs. Whatever the reason, business leaders have to find ways to drive AI adoption while bringing employees along on the journey.

That's easier said than done, but there are proven approaches that work.

Define Your AI Strategy

The best place to start is to define and clearly communicate that adopting AI is a company strategy, not an experiment. This top-down approach effectively mandates that all teams adopt AI into their ways of working. It involves updating roles and responsibilities and linking AI-related goals to performance reviews, making engagement a part of future career growth and compensation.

Some CEOs at well-known companies have taken this approach, and in some cases their internal communications have been shared publicly. It sets a clear expectation that this is not optional.

That said, a mandate alone will not cover all bases. A subset of people will embrace the challenge and get stuck in, while others will say "I'm too busy" or "I tried it and it didn't get it right." Both objections are worth addressing directly.

The time objection is understandable but misplaced. Even with minimal effort, anyone can get a return on the time invested, even if it's as simple as using AI to draft an email.

The accuracy objection is more instructive. If someone enters a poor prompt, AI is unlikely to produce a great result. A certain amount of trial and error is required to learn how to write a good prompt, and some people will need guidance to get through that learning curve.

Apply a Structured Change Framework

To take it to the next level, a structured change management framework can be effective. Kotter's 8-Step Model translates well to AI adoption programmes, with practical application at each stage:

1. Create a Sense of Urgency Share hard-hitting stats and news headlines about competitors' AI wins. Create a sense of FOMO by sharing internal success stories: "Team X cut development time by 60% using an AI agent."

2. Build a Guiding Coalition Identify AI champions across functions and give them a formal role. Run Train-the-Trainer sessions so each department has its own go-to expert. The goal is distributed ownership, not a centralised AI team that everyone else waits on.

3. Form a Strategic Vision Define a clear AI Transformation roadmap for the company and AI Literacy for everyone, with milestones that people can relate to. For example, "AI in 6 months": from quick-win email drafting through to agentic process automation. Tie the vision to business KPIs such as time saved, products delivered, and revenue uplift.

4. Communicate the Change Vision Repeat the vision consistently across every available channel: all-hands meetings, team stand-ups, internal newsletters, leadership updates. The message should be the same each time: where we're going, why it matters, and what it means for each team. Consistency over time is what moves people from awareness to belief.

5. Empower Action by Removing Barriers Publish an internal guide explaining which tools can be used for which use cases. Purchase AI service licences for defined roles where applicable. Instruct managers to schedule dedicated time for teams to work with and learn about AI tools.

6. Generate Short-term Wins Identify quick win use cases: AI drafting first versions of documentation, generating talking points and visuals for slidedecks, summarising meeting notes. Celebrate each milestone and consider rewards like an earned day in lieu to reinforce the behaviour.

7. Sustain Momentum Keep AI on the agenda as the programme matures. A standing slot in all-hands meetings signals that this is not a one-off initiative. Cross-functional events that mix technical and non-technical participants help broaden engagement beyond the people who were already enthusiastic.

8. Institute Change Embed AI usage into the structures that govern how people work and grow: performance reviews, career-path conversations, job specifications, and onboarding materials. When AI-first thinking is reflected in how people are hired and developed, it stops being a change programme and becomes business-as-usual.

AI Transformation Initiatives

The Kotter framework provides the change management scaffolding. What sits inside that scaffolding is the actual programme of work, the specific initiatives that build AI literacy across your organisation. Here are the most effective ones.

AI Steering Committee

An AI Steering Committee provides the operational backbone of your adoption programme. It owns the budget, coordinates planning across teams, and manages communications. Without a body like this, AI initiatives tend to be ad hoc and short-lived. The committee gives the programme structure and accountability, and signals to the wider organisation that AI adoption is being taken seriously at a leadership level.

Communities of Practice

A Community of Practice (CoP) is a group of AI champions drawn from across the business who collaborate on initiatives and then cascade knowledge to their own teams. Unlike a top-down training programme, a CoP generates organic peer learning. Members bring real problems from their day-to-day work, experiment together, and share what actually works. This format tends to produce more durable behaviour change than a one-off training session.

Small Team Workshops

Generic AI training rarely lands well. What works much better is a targeted workshop built around the specific use cases relevant to a given team. A customer success team's AI problems are very different from an engineering team's, and the training should reflect that. Keep the groups small, make it hands-on, and ensure participants leave with something they can use when they get back to their desks.

Lunch & Learn Talks

Lunch & Learn are a low-effort, high-impact format where AI champions present techniques, share process changes, and run live demos. They work well as a recurring slot, perhaps monthly, and create a natural venue for sharing wins and new capabilities as the AI landscape evolves. The show-and-tell format tends to spark more curiosity and discussion than a formal training session.

Hackathons

A well-run hackathon can shift culture in ways that a policy never will. The key is to make them fun and genuinely cross-functional, with each team including at least one non-technical participant. This breaks down the assumption that AI is "a tech thing" and surfaces use cases that engineers would never have thought of on their own. Keep the problem statements broad enough to allow creativity, and celebrate the process as much as the output.

Weekly Newsletter

A short, regular newsletter is one of the most underrated tools in an AI adoption programme. It does two things: it celebrates wins, which reinforces the behaviours you want to see, and it keeps teams informed about new tools, policy changes, and upcoming events. It does not need to be long. A few well-chosen items, consistently delivered, will do more than an occasional deep-dive.

Peer Prompting Sessions

This is one of the most effective formats, especially for people who are finding it most challenging to adapt. Pair an experienced prompter with someone less experienced in a one-to-one session where they work through a real task together. The experienced person shares their thinking process; how to structure a prompt, when to add more context, when to stop and steer the AI when it goes off course. This rapidly closes the gap between people who have found their footing with AI and those who have not. These sessions also tend to surface the most honest objections, which you can then address at a programme level.

Career Planning Conversations

AI literacy should feature explicitly in career development conversations. The framing matters here. This is not about pressure to adopt AI or risk being left behind. It is about helping people develop a high-agency mindset, the habit of actively looking for ways that AI can help them do better work, take on more interesting problems, and grow their capabilities. People who approach AI with curiosity tend to get significantly more value from it than those who approach it with scepticism or compliance.

More on this topic here: Career Planning in the AI Era

AI Governance

Don't let the enthusiasm for adoption outrun your AI governance. Establish an AI Governance Group to define policies and standards, assess risks, and promote ethical use of AI across the organisation. The Steering Committee handles operational execution; the Governance Group handles the guardrails.

More on this topic here: AI Governance & the Journey To ISO 42001

Conclusion

There is no magic approach to overcome resistance to AI adoption. It takes time and repeated reinforcement. The “rule of seven” suggests that people need to encounter a message at least seven times before they take action or remember it, so keep working at it!

By combining a top-down mandate with a structured change framework, and backing both with a concrete programme of literacy initiatives, you give yourself the best chance of moving from isolated experiments to genuine, sustained transformation. The goal is not just that people use AI tools. It is that AI-first thinking becomes the default way of working.