At every juncture of technological innovation, one has to decide where one lies. What position to take and more importantly, how to act moving forward. You have to do it as an individual, company leaders have to decide strategically on behalf of the organisation they lead.

The Technology Adoption Lifecycle was created by Everett Rogers in 1962 as part of his research on the diffusion of innovations. The five stages and their percentage distribution, based on Rogers’ research, are:

• Innovators (2.5%) Innovators include those that are eager to try and adopt new products. …
• Early Adopters (13.5%) …
• Early Majority (34%) …
• Late Majority (34%) …
• Laggards (16%)

When it comes to AI, there is much handwringing about job losses, immaturity of the technology, costs, etc. Less so about being left behind. Hindsight is a wonderful thing but doesn’t help you decide in the present moment.

The key question is what you decide in the present moment and where you lie and how are you looking at it [technological innovation]. Other than to look at it in the abstract, it’s useful to consider how you typically respond to technological innovations, as an individual or a collective, i.e. an organisation

Are you the proverbial frog in boiling water where you wait for things to get hot enough before jumping (the truth of this metaphor). Or will you be too late. Where do you typically lie on the technology adoption curve.

It helps to have perspective. I’ve written about that before: Thought rocket: arc of change and bending reality – InnerVentures.

Looking at past events is really helpful to understand our present and how we might respond.

“What lean did for manufacturing, AI will do for knowledge work”, said Satya Nadella at Ignite 2024. It took several decades to develop lean processes and practices.

At the same event, a Blackrock speaker mentioned that Copilot for M365 (Microsoft’s AI technology for modern, knowledge work), is currently as bad as its ever going to be and can only get better.

AI now looks more like a necessary cost to stay in the game rather than a major revenue generator. It’s the price of innovation and you have to invest in it to reap the rewards later.

Jevons Paradox has recently been talked about a lot. An economic theory which states that making a resource more efficient can actually increase the overall consumption of that resource, i.e. adoption.

There is every indication that current AI, which has been gestating for many years now, is not only here to stay but be transformative. If you don’t see that, maybe it’s time to remove the blinkers.