Measuring Whether AI Is Actually Working With Nexthink

Neil C. Hughes (0:00)

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What does AI success look like when license counts, usage dashboards and falling ticket volumes all give leaders maybe the wrong impression? Well, today on AI at Work, I'm going to be joined by Scott Pope, Director of Value Advisory at Nexthink. And together, we're going to discuss how companies can measure whether AI is genuinely improving work. And Scott will argue today that value has to be defined before deployment, has to be baseline against today's performance, and then translated into a currency that each unique stakeholder understands. Whether that means productivity, cost, employee time or reduce friction, value will mean so many different things to different people. And we'll also examine why one AI tool will never suit every employee. And understand what digital employee experience data can reveal about data, and why technology actually remains the easiest part of workplace change. So today's conversation may make you question whether fewer support tickets deserve the celebratory dashboard after all.

We'll deliver a few surprises along the way, I'm sure. But enough for me, let me introduce you to Scott right now.

So thank you for joining me on the podcast today. Can you tell everyone listening a little bit who you are and what you do?

Scott Pope (2:02)

Yeah. So thanks for having me on, Neil, to meet you. My name is Scott Pope. I'm the Director of Value Advisory at Nexthink. For those that don't know Nexthink, we are the market leading digital employee experience platform.

My role is all about making sure that customers see value in our product, and then we deliver that value to the customer during the implementation and post-sale piece and the customer's journey and life cycle.

My background is actually on the customer side. So I've spent 20 years in enterprise IT, and then I moved into a role with Nexthink around four years ago.

Neil C. Hughes (2:49)

Well, thank you for sitting down with me today. There's a lot I want to talk with you about, because there's been so much noise around AI, in particular about 18 months ago, there was a lot of focus on the lack of ROI from AI projects. And I was reading how you recently said that AI has changed the game because SaaS vendors and customers are increasingly trading in the exact same currency, which is now outcomes and measurable business value. It's so refreshing to get back to that. So what does that change about how enterprise technology needs to be built, sold, brought, and measured, of course, too?

Scott Pope (3:27)

Yeah, I mean, from my perspective and from our perspective, I think we do see a value gap with AI.

But I think with value, value can mean so many different things to so many different people. And it doesn't exist until you've defined it. So you have to define it before you start. And I think what we're seeing is that a lot of companies are just pushing out AI tools to the business and seeing what happens. So I really think you have to focus on the outcome and then understand the currency in terms of how you want to measure value, whether that currency is productivity. If you're working with someone in HR or CEO, that may be productivity. In other areas of a CFO, that's probably going to be cost. So it really depends on the stakeholder.

And you really have to understand where you are today. So you have to baseline. If you don't know where you are, you cannot see the improvement. And the improvement is the value. The gap, that gap in between is, is the value that you're providing to the business.

Neil C. Hughes (4:37)

I just wanted to bring some stats in here as well. I mean, the latest ONS data shows that UK businesses and AI adoption has predictably risen from 12% to 35%, but the average adopter is using only 1.6 AI technologies and just 10% report extensive use. So what does that tell us about the difference between experimenting with AI and actually changing how a business operates and getting that value?

Scott Pope (5:05)

Yeah, I think we sort of see similar from some of our own data as well. You know, the most used tool in enterprise, obviously, is Microsoft Copilot. Yeah. Like we can all guess that, right? So, but I think one of the big challenges there that we see across organization is just general visibility or observability across the estate in terms of what AI tools are actually being used. From an independent view, you can't, if you can't see it, you don't know what people are using, my crap, so I also feel, and we also see from the data, that there isn't a one size fits all to AI adoption.