You Might Also Like: The Next Five

**Zack Kass** (0:03)
The most important things that happen in the history of our species is technological progress.

**Giles Bryan** (0:09)
No AI project should get to the end of 12 months, and someone says, oh well, that hasn't worked. It's all about tracking it, modifying it, changing it, adjusting it, all the way through until it does work and starts to deliver benefits.

**Chris Herbert** (0:22)
AI doesn't necessarily remove people. It changes where you can use people to create value, and that value is around how do you drive a differentiated customer experience.

**Zack Kass** (0:32)
The companies who get it right are going to solve the answer to the question, if you could automate everything in your life, where would you stop?

**Tom Parker** (0:40)
For the past few years, the corporate world has been boldly surfing the initial wave of AI excitement. Boardrooms worldwide have poured hundreds of billions of dollars into artificial intelligence, fueled by grand promises of economic revolution. We were told productivity would skyrocket, costs would vanish and businesses would effortlessly scale. But as the fiscal years roll over, executives are searching for the next wave of provable returns and exploring what they will need to do to catch them and serve them to the beach of productivity gains. There's a multitude of data out there tracking the hype versus reality. A year ago, MIT's The Gen. AI Divide State of AI in Business 2025 report found a 95% failure rate for enterprise generative AI projects, defined as not having shown measurable financial returns within six months. With the speed of AI evolution, what does it look like now? According to McKinsey's The State of Organizations 2026 report, while nearly 88% of organizations are actively experimenting with generative tools, still over 80% have yet to see any meaningful bottom line or EBIT gains.
While the figure is still high, it shows there has been improvement and there are opportunities to focus on.
The challenge for this next generation of technology, specifically autonomous agentic AI, is to prove it can deliver measurable, repeatable business value at scale. But unlocking the value requires a total architectural overhaul. It means completely re-engineering the internal human workforce, and ultimately altering how the customer experiences an organization from the outside.
Welcome to The Next Five Podcast. I'm Tom Parker, and today we're cutting through the AI noise to find measurable business value. In an episode, we're calling AI Returns, separating value from hype. Joining me on this journey are three leaders at the very heart of this topic. First we have Giles Bryan, General Manager for CX at NICE. Giles, welcome to the show.

**Giles Bryan** (3:04)
Pleased to be here with you, Tom.

**Tom Parker** (3:06)
Next is Chris Herbert, Customer Service Director at Openreach. Chris, thanks for joining us.

**Chris Herbert** (3:11)
Thanks for the invite, Tom.

**Tom Parker** (3:13)
And finally, Zack Kass, author, podcaster and former OpenAI executive. Zack, it's a pleasure.

**Zack Kass** (3:19)
Likewise, thanks.

**Tom Parker** (3:21)
Well, Zack, let's start with you. Having been on the inside of the AI frontier, you've watched this wave build from the ground up. As mentioned at the top of the show, McKinsey put an 80% failure rate for enterprise-generative AI initiatives when measured by short-term financial returns.
On the question of cost, PwC's 2026 Global CEO Survey showed that only a quarter of CEOs say costs have decreased due to AI, while 22% report an increase. More than half, about 56%, say their company has seen neither higher revenues nor lower costs from AI, while only one in eight, 12%, report both of these positive impacts. What's behind these early stage results, Zack? Is the issue technological, operational, or is it simply that returns are harder to measure on such a nascent technology?

**Zack Kass** (4:19)
In the avoidance of saying obvious things, I think we should call it a couple of problems. The first is people got so excited about AI that they were willing to do non-economic things that would obviously skew a lot of the return on investments that we would have normally expected. The second problem is no one knows how this technology works.
And it's moving so quickly that the experts aren't in the room. Everyone is trying to figure this out in real time. So it's a bit inevitable when you have companies willing to spend any amount of money to get ahead and employees who don't actually know how the technology works that you'll have a gross overspending. And we've seen that. But the third issue I think is a principle one, which is that companies don't actually agree, often internally, on what they should build and where they should go.
And when you actually look under the hood, as often as I have, you discover that companies are not actually pointing employees at a North Star anymore. Employees are working individually on small KPIs that don't actually always point to a broader goal or outcome. And so it actually represents, I think, a broader corporate failure right now, which is a lack of courage to describe what could happen. Ultimately, the best companies we're going to see are going to start to make this technology invisible. Requiring every employee to click around and do a bunch of their own work inside of a technology hub was never going to be the panacea. The best technology becomes infrastructure.