AI Adoption Isn't a Software Upgrade

When we replace systems such as a CRM, a practice management system, finance platform or database, there is usually a considerable amount of work involved: understanding requirements, mapping processes, cleaning and migrating data, configuring the new system, integrating it, testing it, and training people to use it.

It can be difficult, expensive and disruptive, but the basic proposition is relatively easy to understand.

In many ways, it's like replacing your car or a very well-worn pair of trainers. The old ones have carried us a long way. The new ones are better designed, use newer technology and hopefully help us perform better. There is an adjustment period, but ultimately we understand what we're changing and why.

AI doesn't just change the system - AI reaches beyond the system

When we replace a traditional business application, much of the implementation work focuses on the system itself. What information needs to move across? How should the new platform be configured? Which workflows need to be rebuilt? Who needs access? How will people use it?

AI reaches beyond the system into interacting with this information and knowledge. It is different in that it enables people to reason, write, research and make decisions conversationally. It changes how information is found, is processed and how work moves through an organisation. It can potentially remove tasks, reshape processes and alter where people spend their time.

So AI adoption isn't simply a technology implementation - it is a conversation: an information conversation, a process conversation, a people conversation and a change conversation.

Then there are the people

AI asks something more of people than in a traditional system change.

We're asking people to work alongside technology that can generate, analyse, recommend, summarise and increasingly act, with capabilities that continue to evolve at extraordinary speed. They need to understand when to trust it, when to question it, and when human judgement needs to take over.

Some people will embrace that immediately. Others will be cautious. Some will worry about what it means for their role. Others may use AI enthusiastically without fully understanding its limitations.

People need to understand what the technology is capable of, what it isn't capable of, how it should be used, and where their own judgement remains essential.

This is not software training. It's capability building.

And leadership has a different job too

Choosing the platform matters, of course. Security matters. Governance matters. Integration matters. Cost matters. But there are bigger questions. What do we want AI to change? Where do we want it to create value? What work should remain human? What new capabilities do our people need? What risks are we prepared to accept? How will we know whether AI is actually improving the organisation rather than simply adding another layer of technology to it?

Those aren't questions for the IT department - they're leadership questions.

We need a different implementation mindset

AI still requires all the disciplines of a good technology implementation. There will still be integrations, permissions, governance, testing, training and all the practical work that accompanies enterprise technology.

But it asks something more of an organisation. It requires us to think beyond how we implement the technology and consider how the technology is going to change the way we work.