3 considerations for AI adoption

AI adoption is more than a tech adoption project. It’s a powerful technology that is changing how people and organisations interact with knowledge and data – from personal counselling to skill development and business strategy.

Treated as a tech adoption project, the likelihood is that organisations won’t realise the potential of the tech and it will get bogged down as (yet another) a complex IT initiative. That’s why we have created resources to help organisations understand how workers are using AI and how to harness and amplify this innovation 👉 https://lnkd.in/egYM8Pmp

If you are exploring AI adoption then take a listen to this recent CIPD podcast – From AI experimentation to AI maturity. Three things stood out for us:

1 Understand how people use AI and build on that

That means you need to know how to gather participative intelligence – what people are doing with the tech as it evolves – and act on it as well as helping peers learn from each other.

As Graeme Burns, AI People and Communications Leader at Nationwide Building Society, says: “With our software engineers, we found that initially they were doing certain things with it, and then actually as time went on, they were finding new ways to use it, new ways to improve it. And actually the peer-to-peer learning would have been a really, really beneficial thing to have set up as people went on that voyage of discovery at different paces.”

2. Managers are key to the success of AI adoption

As Burns say, “It’s not like you’re just deploying a piece of IT, you know exactly how it’s going to work on day one and you can expect certain kind of outputs from it because when you’re using AI, what you’re going to find is that actually on day one it does something, by day 100 it’s doing 1,000 different things that maybe you weren’t anticipating. So having that employee involvement, having that base to build from, making sure that you have managers who can work quite closely and coach and facilitate that change within the organisation is something that we’ve found is going to be super important.”

3. AI adoption is not equal

Really understand who is and isn’t using it and why.

Burns adds, “I’m going to use a brief example again with the software engineering activity that we had 800 engineers using AI tools. If we had maybe thought about what the implications of that deployment would be, we may have anticipated some other things. For example, gender adoption, there’s different speed of adoption of these AI tools, potentially across genders.”

🎧 Listen to the podcast 👉
https://lnkd.in/daC6jjZH