Davos 2025 - Successful AI adoption demands 'show, not tell', strong change management, and HR re-invention, advise AWS and Accenture CEOs

A welcome realization about AI that we’ve seen more of in recent months is that successful adoption is about more than technology; culture change is every bit as important - and that will mean organizations thinking again about HR and change management.

On Day One of this year’s World Economic Forum Annual Meeting in Davos, two AI industry leaders, AWS and Accenture, picked up on this idea as their respective CEOs outlined their own experiences of ‘eating their own dog food’.

For AWS, CEO Matt Garman emphasized a by now familiar theme from the firm about Amazon’s long experience in tapping in AI internally. This has made the current generative AI hype cycle something easier to manage in terms of winning take-up within AWS:

We were fortunate to have some of the best technology experts in the world, and so they jumped in, and in many ways, were driving a bunch of the gen AI technology. Getting that set of people to dive into the technology wasn't really that hard. It's like kids in a candy store for them, really, they're quite excited to dive in. We have some of the best science experts in the world, some of the best technology experts in the world. So,  that part of our business was quite easy.

But there were other factors that needed to be taken into consideration, he recalled, with customers looking to AWS to be experts and provide advice/knowledge. This impacts on more than the techies and means that the likes of sales people on the front line need to be trained to be up to speed:

Fortunately, we have that expertise in-house. We’re training the rest of our team all of the time to be at the leading edge of technology. We've built that muscle over the last 20 years that we've been building AWS, so whether it's new technologies across the board, we've kind of built that mechanism to do training.

Our customers look to us to train them as well and so we've actually built up a whole organization that's focused on training customers. It's free training. We train millions and millions of customers for free on AI because we feel like they're not going to get the most out of technology, they're not going to get the most out of what we do, if they don't.

Training considerations

Again, the idea that successful AI adoption transcends tech comes into play, he added:

It's actually not how you use the technology. That's not actually the training. The hardest bit is getting people's heads around what's possible and, and not even what's possible now, but what's possible 24 months from now, or 48 months, or two, three, four years out. I think people have a hard enough time realizing what's possible today, but as you're implementing it 12 months later, the technology is going to be much further along, so you really have to think about what's going to be possible and unlocking it.

That’s not necessarily as straightforward as it might sound. Garman explained:

It really is unlike any technology that we've had before. It really is going to change every single job in every single industry in ways that I think it's really hard for people heads to get around. It's not just doing something 5% better, it's doing something 1,000% better or more, that order of magnitude.

And it all takes time on the part of AWS, he noted:

We have to sit down and help them understand how can their business actually get that much more efficient, that much more effective? How can we change customer experiences that way? So internally, we had to get our teams to think bigger like that too, and really push them to think what's possible out there. I think as soon as your employees get their heads around that it's not that my job's going away, or it's not that this is going to save us five percent of cost, but, that we're going to fundamentally be able to go deliver things weren't possible before, if you at least have the right sets of employees, most people get pretty excited about that opportunity.

One way to convey this is to ‘show, not tell’, he advised:

Lean into a particular use case that that takes work and toil off of the rest of your team, that unblocks something. One example we had of this is we built a product that automatically upgraded Java version, If you talk to software development teams, that is a task that not one person is super excited about doing. No one's like, ‘You know what I really love is just upgrading Java’. It's boring, but it makes sure you have all the latest security, it gives you performance benefits etc.

We built an AI system called Q Developer Transform that just does that for you, and so we decided to eat our own dog food. We said, ‘OK, we're going to go do that across Amazon’. The teams internally estimated that to upgrade all of our internal systems was gonna take somewhere around 4,000+ man years to upgrade. We had a team of five people that did it in a couple of months across and so we freed up 4000 person years of effort to go build features and capabilities for all of our customers by taking away work that no-one wanted to do in the first place. It is a very good thing where you just lean in, you just say, ‘This is a capability that we can go and roll out and and it's good’. It highlights that when you find that thing, it has a massive impact on your company.

Re-inventing HR

Accenture CEO Julie Sweet picked up on the new role that existing HR teams need to face up to:

What this requires is a completely different thinking about how people work and what skills they have. In most cases, AI isn't replacing people, it's replacing tasks or parts of the processes, which means that in order to upskill people, you have to understand what skills do they have to begin with? At Accenture, we moved to skill-based HR over five years ago. I have a database of almost 800,000 people and their skills. We're systematically re-defining the skills needed at Accenture - who needs gen AI, what kinds of technology? -  but also, as we are replacing some of the things that they're doing with gen AI, we're able to identify who could be upskilled. So you have to have skills-based HR.

Enterprises also need to have HR can adapt. Sweet cited Accenture’s work in introducing AI into marketing:

We built an agent so [employees] can give feedback just like you would give feedback to someone you were supervising, or feedback to a colleague. That suddenly makes the AI very real to people, and they start to see how it can really help them.

That’s important when dealing with transformative technology, he added:

That's a totally different mindset. We didn't have a playbook that says, ‘How do we take people who are now going to use AI fundamentally in their job and make this be something that they really care about?’.

Finally, HR needs to get its head around the importance of change management, she advised:

The kinds of change management that you have to do here require you to understand the technology and understand the people. In most cases, companies use change management to train on a new system, or they use whatever partner they happen to be using. This is a skill set now that has to be at the center of your company, because it's a continuous re-invention. The amount of opportunity that AI is going to change in countries and companies is going to take decades. It's not a one and done. So building change management so it's not viewed as being something off on the side is really important.

All of this is vital for successful AI adoption, she concluded:

At the end of the day, the technology will only be effective if it's trusted and adopted, and you can then create new opportunities for your people. We have to think very, very differently about the incredible departments of HR, and the role that they need to play and how they have to re-invent themselves.

My take

Interesting perspectives from two organizations that are both users and purveyors of AI.