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ZDNET’s key takeaways
Table of Contents
- Three years into the AI revolution, and people are starting to get twitchy.
- Business leaders must focus on delivering outcomes, not implementing tools.
- Successful AI is a learning process that requires significant cultural buy-in.
It’s been three years since the launch of ChatGPT. While the pace of change can sometimes seem incredible, there’s now a growing concern that generative AI might not be the revolutionary step change that some people believed.
Experts are warning that the AI investment bubble might be about to burstand many chief executives are becoming frustrated with gen AI, having seen their companies invest in projects without visible returns.
Also: Is your company spending big on new tech? Here are 5 ways to prove it’s paying off
For digital and business leaders charged with creating value from gen AI and other emerging technologies, such as agentic AI and deep researchhere are five ways to ensure your AI revolution is more boom than bust.
1. Strive to avoid ‘pilotpalooza’
Diana Schildhouse, chief data and analytics officer at Colgate-Palmolive, said the key to AI success is identifying the business problem that needs to be solved and finding the right technological solution.
“We need to make sure it’s tangible and tactical in what it does,” she told ZDNET. “Our approach isn’t about having an AI team that’s off in an ivory tower building something that we think is a brilliant solution, but then, when it’s time to talk to the business, it doesn’t actually help solve what they’re going after.”
Schildhouse works with her team and the business to run pilots that show value. Once that value is proven, they consider how to scale up their fit-for-purpose solutions.
“Organizations can get into this trap of what I call ‘pilotpalooza,'” she said. “It’s also sometimes called ‘A thousand flowers bloom.’ It’s where everyone is testing out every possible way you could do something.”
Also: 5 ways to feed your AI the right business data — and get gold dust, not garbage back
Schildhouse said big companies could end up with hundreds of pilots doing similar things around the globe. Careful targeting leads to bigger business impact.
“Being very thoughtful about how we set up pilots, what we’re learning, and then the scaling plan is something that’s helped drive success for us, because we’re not testing every shiny new object in an ungoverned way.”
2. Deploy mature solutions
Ian Ruffle, head of data and insight at UK auto breakdown specialist RAC, said that high AI failure rates occur when professionals focus on the technology rather than the business challenge.
Hype is one significant issue for professionals who dabble in AI. A desire for first-mover advantage means software vendors have been busy ensuring their products are marketed as being AI-enabled, whether or not there’s a step change in capability.
While there has been some pushback against AI hyperbole, Ruffle told ZDNET there are useful technology solutions on the market, and professionals must remember how far emerging technology has come in a short space of time.
“I think three years is quick for where we’ve got to,” he said. “There’s a huge amount of capability. However, organizations that are going to do well in AI will be the ones who wait until they find a problem and deploy a mature AI to solve that challenge.”
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Ruffle said he works closely with business stakeholders, his team, and key technology suppliers, including Snowflake, to find AI-enabled solutions to business challenges.
He said organizations are rightly concerned about governance, but he expects to see an escalation in the pace of deployment in the next two years.
“The capability will continue to move forward,” he said. “When you focus on the process of finding a solution to a problem, I think AI is hugely capable.”
3. Learn from your challenges
Paul Neville, director of digital, data, and technology at UK agency The Pensions Regulator (TPR), said it’s important to recognize that boom or bust aren’t the only potential outcomes from an AI investment.
“There’s quite a big distance between those two things, and I think we need to be visionary, thoughtful, and be prepared to think differently,” he told ZDNET.
Neville has created a user-centered design team that ensures the organization is focused on the right outcomes for users. These outcomes, including for AI projects, are measured, and the business reflects on the progress.
“We work in an extremely iterative way, and we’re learning,” he said. “We will try things that don’t work the first time. We need to plan for that, because that’s part of this world. It’s part of innovation. You don’t achieve anything without sometimes things not working. The most important thing is to learn from that process.”
Also: No ROI in AI yet? Try these six proven tactics for creating real business value
As part of this iterative approach, Neville is leading research work outside IT, encouraging TPR to become a learning organization.
“I don’t like the phrase ‘fail fast’ because I don’t think you’ve failed if you’ve learned something along the way,” he said, before outlining how that approach applies to AI deployments.
“Success is about learning as we go and not trying to do everything at once. You need good control, good governance. And that’s coupled with having a long-term vision. When these things come together, you can deliver the incremental results that ultimately deliver better value and tangible difference to the people that matter.”
4. Overcome cultural resistance
Mike Bray, VP of innovation at manufacturing specialist RS, recognized that AI continues to capture headlines and excitement among business leaders, and now CIOs are grappling with the reality of exploiting emerging technology.
“Investment in AI tools is just the first step; implementation is the bigger challenge because it requires deep-rooted cultural change,” he said.
Recent RS research suggested that resistance to change is the biggest internal obstacle senior leaders face when it comes to innovation.
Bray told ZDNET that cultural resistance among teams, managers, or frontline staff can slow the adoption of new tools, technologies, and processes, making it tough to scale AI pilots and proof-of-concepts.
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He said CIOs must prioritize purposeful AI execution without being carried away by the hype.
“Success hinges on solving genuine business challenges that matter to employees, and on incentivizing staff to embrace new processes by demonstrating clear, tangible benefits in their daily roles,” he said.
“When employees see how AI can make their work easier, more efficient, or more rewarding, adoption follows naturally. Ultimately, an AI initiative will only deliver lasting value if it is implemented with a clear focus, not just for the sake of it.”
5. Concentrate on integrating systems
Steve Lucas, CEO of technology specialist Boomi, said the AI revolution is occurring in different ways and at multiple speeds. As technology firms push ahead with innovation, some end users can be left perplexed.
“I think there are certain things where the pace of experimentation is accelerated and high with the model vendors,” he said.
“More generally, I think the pace of adoption is a little bit behind because many people in business don’t fully understand, on the left hand, how we feed AI the proper data that these models need.”
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Lucas told ZDNET that one of the big issues is integration. CIOs and their vendor partners must work together to fill the gaps.
“We have all these enterprise systems. We’ve had those for a long time, and perhaps we have too many of them,” he said.
“We also have AI in the models, but we’re lacking the connective tissue between enterprise systems and AI. People are finding that there’s an activation stack they’re missing in the middle. Integration will be the key to success.”
