Managing the change: don’t turn your experts into novices

Photo credit: Sebastian Hermann via Unsplash

Technology people often forget the human side of change management. It is hard enough to design and build systems, integrate them, migrate data, configure infrastructure, and get everything working and stable. Technology can be erratic and unpredictable: who has time for even more erratic and unpredictable humans? Perhaps if we tack a training course and a couple of videos on the end of the systems rollout, we can achieve a bulk update of the human config files.

The error of thinking this way was demonstrated to me when I was working on a large scale merger between two banks. The technology choice was easy: we picked the systems of one bank, and then started planning how to migrate data from one set of systems to the other. Our designs were filled with data extracts and transforms, temporary integration layers, reconciliation and testing suites. We thought that our hardest problem was fitting all the migration work into a constrained time slot.

We did not entirely forget training. It was obvious that staff that had worked with one set of systems would need to learn how to work with a new set of systems, especially as we were effectively moving from legacy to legacy: the goal was to simplify the merged entity, not to transform it. The target systems carried all the idiosyncrasies and oddities that accumulate in  decades of changes and choices.

What we failed to consider, though, was that moving to a new set of systems did not just change the tasks that people performed: they changed the dynamics of each team. Every branch, every region, every team in the service centre or the call centre was not just a collection of resources performing tasks: it was a set of people with trust and relationships.

Furthermore, some of those people had earned respect over many years, through their familiarity and expertise with the company’s systems and processes. They were the people that others came to when they needed help. They were the ones who could fix things when they were broken, and find their way out of problems. If all we did was drop in new systems and offer some basic training then our plan would not only fail: it would remove part of the professional identity of these experts.

Fortunately, one of our business sponsors, who understood that complex business change depends on people, and that if you don’t engage people in the change then the change will fail, gave us a simple principle: don’t turn experts into novices.

This led us to change our approach to training. It made us realise that the experts in the existing systems were the best people to deliver training in the new systems. Not only were they good at dealing with the quirks and features of aging systems, they were also respected and trusted by their colleagues. When given a chance, they engaged in the design of the rollout, and quickly became as expert in the new systems as they were in the old. They were essential to making the migration a success.

That migration took place over a decade ago, with an earlier generation of technology (although much of that technology is still in use). Since then, banking and other industries have gone through the shift to mobile, the closure of branches, the deepening digitisation of the enterprise, and are now starting the adoption of AI.

There is a danger that much AI adoption will follow the crude pattern of training that we started with on the migration project: basic instructions on how to use the tools, and a number to call (or a chatbot to talk to) when things go wrong. Some of it might be more sophisticated: many organisations are running masterclasses on prompting, or teach-in sessions where AI enthusiasts share their hints and tips. It’s unclear how much of this makes any difference in practice - or whether it starts from a position of respect and understanding for the people being trained.

I am not convinced that these modes of learning alone will achieve their goals. If AI is truly going to reshape work, then organisations need to think about how it will reshape the relationships, status and identity of their teams. If the only message to those people, especially people who have invested years of their careers in understanding their organisations, is that expertise no longer matters, and that their new role is to feed prompts into chatbots and cut and paste the results, then the change is likely to fail - and to cause distress, disempowerment and a loss of dignity along the way.

AI adoption, and the change management that goes with it, is in its early stages. Aspiring enterprise AI leaders would do well to recognise this, and to realise that tools plus training does not equal transformation. They would do even better to heed the advice of that business sponsor from earlier in my career, and how to avoid turning experts into novices.