The way I see most organizations roll out AI feels more like a farce than a strategy. If it were turned into a movie, reviewers would say it was unbelievable. Nobody could be that stupid.

Yet here we are. And what makes it all the more frustrating is that we have been here before.

Back in 2014, I wrote a book for Smashing Magazine called Digital Adaptation, looking at the mistakes being made in how organizations were approaching digital transformation and what they should be doing instead. The similarities between then and now are striking.

So in this post, I want to look back at Digital Adaptation and see how the lessons in that book apply to the AI era and what we can learn from the past to help us navigate the challenges of AI today.

The biggest mistake, both then and now, is approaching each as a technological challenge.

It’s About People, Not Technology

Sure, both digital transformation and AI are technological advances, and yes, some level of technological implementation is required. But that is the tip of a very large iceberg.

Back in the midst of the digital transformation era, IT departments were rolling out CMSs, CRM systems, intranets, and so on. Meanwhile, on the customer-facing side, organizations were rolling out websites, apps, and personalization. But they were putting almost no emphasis on how people were going to use these systems.

Internal systems were hard to use, and customers either didn’t care what was being built or wanted something else entirely.

The same is happening with AI. Senior management are buying into the hype, and IT teams are rolling out Copilot Studio licenses company-wide and considering the job done. Sure, there is a bit of organization-wide generic training, but that’s about it.

Just like before, staff are left wondering what to do with these new systems and feeling under pressure to use them and deliver results.

Organizations need to recognize that AI is a cultural shift as much as a technology, and that shift has enormous implications for their staff.

After all, this time around, it is even worse, because this time, staff fear for their jobs. They have read the same headlines that you have, and no amount of promises from senior management is going to make them feel any better.

Why Would Staff Want To Adopt AI?

I mean, think about it from a staff member’s point of view. AI offers them very little personally, even setting aside the fact that management may be intending to use it to replace them.

The main reason senior management are excited about AI is that it provides additional productivity. People can do more in less time. But that does not benefit staff. They are still required to work 9 to 5, but now they are expected to do more in the same time. They don’t get any time back, and they don’t get paid more, so why would they embrace AI?

So before asking staff to embrace AI, organizations need to ask themselves how they intend to motivate them to do so.

One strategy is to help staff identify the parts of their job they hate and how AI can help them do those tasks more quickly and less painfully. People tend to be a lot more receptive to change when it is presented as an opportunity to improve their working lives.

But, that should just be the beginning. Organizations may need to consider providing incentives for staff to adopt AI in terms of career progression, compensation, or shorter working hours.

Staff Need Time And Support To Adopt AI

Also, adopting AI is not a simple task. It involves challenging every working practice they have built up over (in some cases) decades. All of that procedural knowledge has to be rebuilt. That is a lot to ask of anybody in even the best of circumstances, and most employees do not find themselves in the best of circumstances.

Most are overworked and struggling to stay on top of their to-do lists. They don’t have time to sit down, learn a new tool, and figure out how it fits into their workflow.

That doesn’t just take time. It also takes a lot of consideration and experimentation. It is not enough to just send a member of staff on a training course and expect them to be able to use AI to its full potential.

At a minimum, staff need training specific to their roles, such as the AI training I provide to UX teams. But organizations should be going even further.

They need to develop sophisticated training programs that help staff from different departments understand how to use AI in their specific roles. They need ongoing support and the time to experiment with AI to find the best ways to use it.

But formal training will only take you so far. In my experience, the most effective support comes from colleagues, which is why I would start with the enthusiasts rather than the resisters. Every organization has a handful of people who are already experimenting with AI, often without anybody asking them to. Bring those people together, give them the time and space to share what is working for them, and let their enthusiasm spread to everyone else.

I saw this work during the digital transformation era too. In Digital Adaptation, I described an internal conference where staff showed their colleagues what they had built, and it did more for adoption than any memo from the top ever could. People are far more likely to try something when they see a colleague in a similar role using it successfully than when they are told to by senior management.

Rather than trying to win over the most resistant staff first, organizations should find the people already experimenting with AI, give them time to share what they have learned, and let social proof do the heavy lifting.

Unfortunately, experimentation comes with a big downside… mistakes will be made.

It Needs To Be Safe To Fail

Many organizations have, to be frank, a culture of fear where mistakes are seen as a sign of incompetence. However, by its very nature, learning a new tool like AI will involve making mistakes, and this can be a massive barrier to adoption.

I saw this during the era of digital transformation. Senior management would demand digital transformation and were confused when it didn’t happen.

What they didn’t realize was that they were sending mixed messages to staff. Sure, they were asking for digital transformation, but they were still demanding that the same key performance indicators (KPIs) be met, and they still pointed out any mistakes in their annual reviews.

There was no space to fail, as the pressure to deliver was relentless.

This time around, it seems to be even worse. I hear horror stories of staff being reprimanded for using too many tokens just weeks after being told to adopt AI. Or staff being lectured so much about what they should or should not do with AI that they are afraid to try anything. It is safer to just do what they have always done.

If management want staff to adopt AI, then they need to make staff feel safe to experiment. They need to adjust KPIs, change how staff are reviewed, and relax some of the governance while staff are learning.

Handle Governance With Care

Look, I get it. AI cannot be rolled out without some governance in place, but again, this is an area that organizations have typically handled poorly.

In the midst of digital transformation, governance would either be non-existent, or it would be so strict that it would stifle innovation, and unfortunately, I see the same pattern emerging with AI adoption.

Staff need some governance to help them know where they stand. It helps them understand what is expected of them and what they can and cannot do.

But too often organizations have been so strict that it is easier not to do anything at all. Pages of instructions about what people should and should not share with AI create that sense of fear that I mentioned earlier.

Organizations need a short set of principles that would fit on a handful of posters, similar to gov.uk’s design principles. Beyond that, they need to demonstrate a degree of trust in their staff to use common sense.

Of course, mistakes will get made, but that is the cost of change, and so organizations need to accept that and do what they can to mitigate that in how they roll out the technology.

Adaptive Technological Rollout

That said, organizations will need to be careful in how they roll out AI from an IT perspective. Once again, it is easy to lock everything down and stifle the adoption of AI.

Again, this is something I highlighted back in 2014 with digital transformation. Overstretched IT teams would pick a set of technologies and roll them out organization-wide, often with little or no consultation with the individuals who would be using them. Anything outside of that set of technologies had to go through a complex approval process, normally involving a steering committee. The result was a glacial pace of change that was one of the major contributing factors to the failure rate of digital transformation projects.

Today we are seeing the same thing. IT teams are opting into a tool like Copilot and calling it a day, but that often falls massively short.

Take, for example, a UX team I am working with at the moment. Management is pushing hard for AI adoption, and the team is excited about the potential of creating an AI-powered research repository and even AI personas. It sounds like a wonderful case study in AI adoption, with enthusiastic management, an excited team, and innovative ideas.

Unfortunately, the reality is that nothing is happening because Copilot cannot deliver on the team’s vision, and so they are currently bogged down in bureaucracy as they try to persuade the AI steering group that they need to use a different toolset. Understandably, the team’s enthusiasm is waning. Why bother?

And that team is one of the more patient ones. In many organizations, staff are not waiting for the steering group at all. Just as we saw shadow IT during the digital transformation era, with people signing up for Dropbox and Google Docs because the official tools could not do what they needed, we are now seeing shadow AI. When the in-house AI tool will not let people do their jobs, they open a personal ChatGPT or Claude account on their phone or home laptop and get on with it.

That is far more dangerous than the problem the restrictions were meant to prevent. Company data ends up in tools the organization has no control over, with no oversight and no record of what has been shared. In trying to lock everything down, organizations end up pushing the risk somewhere they cannot see it.

The instinctive reaction is to clamp down with stricter policies, blocked websites, and warnings about disciplinary action. But that rarely works, and it drives the behavior further underground. Shadow AI is a symptom, and a useful one, because it shows exactly where the official tools are falling short.

Instead of trying to stamp out shadow AI, organizations should learn from it. Talk to staff about what they are using personal accounts for and what they feel they cannot do with the AI they have been given, then use that to shape what gets rolled out next.

Look, I understand the cost of a messy, out-of-control tech stack. It is not ideal. However, in the early days of adoption, we may have to live with a bit of chaos while people explore the edges of what is possible and work out common use cases.

Organizations need to allow staff to explore different uses of AI and even different AI apps to find what suits their specific needs in their specific roles. They can always start locking things down later as the real organizational needs become clearer.

A Dangerous Time

I admit all of this feels dangerous to some organizations. They want to control the AI adoption process centrally and make sure nothing goes wrong. I understand that. But change comes with a degree of risk, and the risk of moving too slowly is greater than the risk of moving too fast.

However, a bigger danger still is when organizations see this as a technological challenge and overlook the human side of things. AI adoption is not simply about rolling out new technology. Just like digital transformation, it is about people, culture, and change management. The sooner organizations realize the scope of what they are dealing with, the better.