Why AI is like a highly motivated intern

In this episode of The Digital Lighthouse, host Zoe Cunningham speaks with Steve Burrows, a global product delivery and technology leader with more than 20 years of experience across consulting, product development, and large scale digital transformation.

Steve has delivered digital platforms and transformation programmes for organisations including Bloomberg, Sony, Orange, and Emirates Airline. Today he works at the intersection of product delivery, AI, and emerging technologies.

Zoe and Steve explore how organisations can capture real value from AI inside complex businesses. While excitement around generative AI is high, many initiatives struggle because they begin with the technology rather than a clear understanding of the problem to solve.

Steve offers a practical way to think about AI inside organisations: treat it like a highly motivated intern. AI can take on repetitive work, analyse large volumes of information, and help teams move faster, but it still needs human oversight, clear guardrails, and thoughtful implementation.

Discover

• Why successful AI projects start with the problem, not the technology
• Why many AI initiatives fail because of unrealistic expectations
• How organisations can identify the right workflows for AI automation
• Why AI works best when it removes repetitive work but keeps humans in control
• Why treating AI like a highly motivated intern is a useful leadership mindset
• Why agile delivery matters more than ever when implementing AI

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Episode highlights:

• 02:57 – Why the problem matters more than the technology
• 09:37 – Why AI should be treated like a highly motivated intern
• 14:23 – Why “replace the business with AI” thinking fails
• 17:13 – Should we worry about AI replacing jobs?
• 20:57 – AI vs human judgment and decision making

About our guest

Transcript

[00:00:00] Zoe Cunningham: Hello and welcome to The Digital Lighthouse where we get inspiration from tech leaders to help us navigate the exciting and ever evolving world of digital transformation. I’m Zoe Cunningham.

We believe that meaningful conversations can illuminate the path forward, helping us harness the power of technology for innovation, scalability, and sustainability.

In this episode, I’m delighted to introduce Steve Burrows. Steve is a global product delivery and technology leader. His current title is AI Product, Operations and Delivery Director with a 20 plus year career spanning Europe, the Middle East Asia, and the UK. Blending, consulting, product development, and large scale digital transformation.

Steve has delivered digital projects and services for major organisations, including Bloomberg, Sony, Orange, and Emirates airline, launching global digital platforms, leading cross-functional teams, and driving complex mobile web and AI initiatives. His career is defined by a passion for emerging technologies from the early days of mobile and web applications through the blockchain boom, and now in artificial intelligence.

So in this episode, I’m gonna be chatting with Steve about how to capitalise on the potential value of emerging technologies within larger and complex environments. We’ll talk about what AI can and can’t deliver, and get his thoughts on how roles, skills and jobs are gonna transform in the future. Steve, welcome to The Digital Lighthouse.

[00:01:35] Steve Burrows: Thank you very much, Zoe. Thanks for inviting me on. It’s great to be here.

[00:01:37] Zoe Cunningham: Could you start by maybe telling us a bit about your career history and the journey you’ve been on, to get to where you are today?

[00:01:46] Steve Burrows: Sure. Yeah. you’ve given a good intro there in terms of, a little bit about my background, but I guess I summarise it as combination of three complementary kinds of skillset.

So there is this sort of problem solving consulting side of things. So I went straight out of uni into that space and spent a few years there, and then over the last 20 plus years, it’s been product development, so owning actual digital products and services and doing the whole product strategy and bring it to market and manage them in life.

And then the third party is the actual delivery stuff. So I’m very much on the agile side of project delivery. So it’s those kind of combinations of skills that’s got me into this world and as it is, yeah, I’ve always been interested in technology and, yeah, I’ve found myself at the, on each of the technology waves over the last few years. So like you mentioned in the intro. Just as IFTTT applications and tablets and stuff started emerging I was helping companies build those and deliver those to market. And then through the sort of web technology boom, broadband taking off and blockchain, now AI… So yeah, I’ve always been passionate about emerging tech. Yeah, AI at the moment seems to be the logical home for me.

[00:02:57] Zoe Cunningham: I think you’ve got this great mix of skills as well, because actually it’s one thing to know about the tech and about the kind of cutting edge or research tech, but actually once you get into a business environment, it’s the, how that technology is applied is what’s important, right? And the product design is much more about how the humans are gonna use it than necessarily about exactly what the tech does.

[00:03:25] Steve Burrows: One hundred percent yes. I’m not actually an engineer or a technologist by background, so I’m comfortable in those conversations, but they can’t ask me to deliver a line of code. It would be a nightmare. But yeah, you’re a hundred percent right. It’s not the technology that should be the focus, it’s the problem to be solved.

And that’s where I think my career has been as it has because it’s always been what is the problem we’re trying to solve. And when I’m working with product delivery teams and engineering teams, they get quite annoyed with this sometimes because that is my mantra. It’s what is the problem we’re trying to solve.

So if someone’s going off down a rabbit hole of technology, this incredible idea. My job is bring them back down to what is the problem solving, who we’re solving it for. So the technology has been different throughout those 20 plus years, but the approach has largely been the same.

[00:04:12] Zoe Cunningham: And in your kind of current industry, where have you seen the biggest impact of AI?

[00:04:19] Steve Burrows: I guess my industry at the most, is that consulting product delivery side of things. So I get to work with a lot of different companies in different sectors and different spaces. So it’s the impact of AI, I don’t necessarily think we’ve seen the full impact of it yet, and the results have been mixed from my view.

And the reason for that is largely because the companies are taking a different philosophical approach to implementing it. So you do have some that are, some senior leader’s read a blog about AI and how it’s gonna take over the world, and how it’s gonna revolutionise their P&L. There’s a, ‘we need AI!’.

Typically, those implementations are not the ones I’m going in to help deliver. They’re the ones I’m trying to fix in a large way, because they haven’t had the impact that people expect. And that’s largely because of misaligned expectations as to what Gen AI can actually do today.

The impact where I have seen success is when people have got the right mentality and the right, expectations from day one. So it’s not going in with that, this is gonna revolutionise my business completely, I can operate it with robots now I don’t have to pay humans. The successful ones are ones that have actually looked at how they work and mapped out a workflow and then thoughtfully plugged in AI into sections of that workflow where it’s strong and kept humans where it’s not strong.

So the impact it gets typically around efficiencies and time saving at the moment with generative AI and agentic AI, so that’s, primarily focusing on the stuff that humans don’t like doing, but have to do. So the drudge work, if you like the repetitive time consuming stuff, so low level checking documents, data entry, deep research, that kind of thing.

So AI being used to get humans started on their workflows, but then humans coming in and actually finishing it and putting that quality layer over the top of it, that’s where I’ve seen the most impact.

[00:06:06] Zoe Cunningham: And the judgment and oversight and…

[00:06:08] Steve Burrows: Yeah. 100 Percent.

[00:06:10] Zoe Cunningham: So I suppose, like a question I like to ask as a follow-up, is let’s say I am a CEO and I’ve read a blog post about AI and I am excited about it and I want to make sure that I’m taking advantage of it.

What should I be doing before I go out to the market and try and find someone to revolutionise my business with AI? What are the steps I should be doing internally or what, do I need to be mapping out or planning out?

[00:06:41] Steve Burrows: At the risk of annoying you in the same way that I know my teams that I work with, it would be what is the problem you’re trying to solve? CEO person that’s read the blog.

So I would be understanding their business right now, what is it that they’re trying to… what is their purpose? Why do they exist? Who are they existing for? What’s the personas? What do their personas need? Understand that in detail and map out the workflow. So similar to what I was just saying there about pinpointing the parts of the workflow where AI can help.

So looking for the drudge work sections, I guess. It’s a standard product development approach that I would take with that company. So again, technology agnostic. It’s all about the right problem solving user-centred approach to working out where to use AI. That’s probably where I’d get started.

[00:07:26] Zoe Cunningham: And I suppose that’s actually just such a good explanation of why reading a blog post or listening to a podcast, it’s hard to get started just from that.

[00:07:38] Steve Burrows: Sure. Absolutely.

[00:07:39] Zoe Cunningham: Into implementation, and it’s hard to even discuss where AI can best be applied without discussing a specific problem.

[00:07:50] Steve Burrows: Yes.

[00:07:51] Zoe Cunningham: Have you got any examples you can give of this is the best use case that I saw and I was like, if everything was like this use case, UK productivity would be through the roof.

[00:08:05] Steve Burrows: So a difficult question. One of the more successful implementations I’ve seen is in the financial services space where you can imagine investment bankers and financiers around the world spend an awful lot of time trying to dig through tons and tons of data in order to create investment thesis that they can then act upon and invest in, or divest from.

The most successful ones I’ve seen have shortened analyst workflows massively from taking days of time to create an investment thesis simply because there’s so much documentation to review and absorb and interpret, to using an AI machine to spend, I dunno, one or two hours crunching through this data. All on the basis of a natural language prompt. So it’s very easy to interact with it.

So that’s the most successful implementation I’ve seen. It’s still early days, but that was a lot more that it can do. But right now you can see the results of that just from seeing the analyst’s responses and saying how incredible it is. So yeah, particularly pleased with that implementation let’s put it that way.

[00:09:09] Zoe Cunningham: And I think it allows you to pick out the kind of common factors that successful projects are gonna have. So like lots of data. Already established processes. Like you say, yes, you’ve not changed what the output is, or you are not trying to like re-engineer everything at once. You’re saying, here’s the thing, we know this thing delivers value to the business, but we can now do this thing in a totally different way.

[00:09:37] Steve Burrows: Definitely. And as well as that, it’s having a realistic expectation about what it can and can’t do and in what it can’t do is often more important than what it can do.

So one of the biggest learnings for me when I got involved with AI a few years ago is how many humans it takes to actually do AI. To do it properly, I should say. Anybody can spin up a very quick agentic AI, but to do one that sort of powers businesses or powers business decisions, you have to do it right. So you can’t just trust the AI bot to run with things.

I like to say you treat it like an incredibly motivated and very keen intern. So think about the kind of tasks or responsibilities you’d give that human being in your business. You’re not gonna give them strategic decisions to make. You’re not gonna give them control of your finances. You’re not gonna put them in positions where they’re having to make really critical business decisions. You use them to very carefully explain what you need. You check the output and you put guard rails around it all to make sure that if they do go off track, you can spot it.

So that’s, again, that’s what AI can’t do is equally important as what it can.

[00:10:46] Zoe Cunningham: It’s so interesting. That I thought that description was brilliant because I’ve heard of the concept of thinking of AI as an intern, but actually once you add in like ‘highly motivated’, it’s the intern that comes in and they’re like, ‘oh, I’m gonna, I’m gonna do everything while I’m here’.

And actually, while that’s a great characteristic, like a great human characteristic, there are… anyone who’s been in that position, there are downsides to this as well.

[00:11:13] Steve Burrows: Of course. Yes.

[00:11:14] Zoe Cunningham: And then you throw in some like massive over confidence in their abilities and boom, you’ve got AI and you’re like, okay, so how do we curtail, actually, not just what can you do, but how we curtail what you do is…

[00:11:32] Steve Burrows: Yes how can you channel that massive enthusiasm in the right direction?

[00:11:37] Zoe Cunningham: So are there any other kind of expectations that when you meet teams or maybe senior leaders who don’t have in-depth knowledge, are there any other expectations you find people have that you have to recalibrate before you can start work on something?

[00:11:54] Steve Burrows: It is mostly that one around expectations and assume that the machines can solve all of your problems and you can divest your entire workforce and save a lot of money. That’s, the main area.

I suppose another area that you have to get people’s mindsets around is, I come from an agile delivery background, and I see so many projects that are set up ‘agile’ but are not actually agile.

It really does help to have an agile mindset when you’re working with AI because the pace of change is so incredible that you can set out a pathway in good faith and then find out a few weeks, a month later that actually there’s another bit of the technology that’s just evolve that’s even better than the one that you’ve got.

So you need to pivot across so that you can continue to build the quality product that you want. I guess it’s encouraging these senior leaders not to think that they can have a roadmap for the next 12, 18 months of AI deployments and stick to it exactly and waterfall it all out with detailed requirements and have a team commit to delivering on this particular day.

You have to have a lot more of an agile, a pragmatic agile mindset and be willing to pivot and learn as you go. So it’s super important that your delivery expectations are equally as aligned as your product expectations.

[00:13:06] Zoe Cunningham: Which is why it’s so important to come back to what you said at the start, right? What is the problem?

I was gonna say the problem doesn’t change, and actually that’s not true, sometimes the problem does change, but when the problem changes you absolutely, in more than in any other circumstance, need to switch your delivery up right to make sure you are meeting what the new problem is. And then working iteratively rather than fixing.

And I think, I dunno, something I’m seeing a lot at the moment is that, it’s almost as if, because there’s a new… a new technology or some new words we’re using for technology. People seem to have forgotten a lot of the lessons ’cause I feel like the lessons of agile delivery were hard won. And so impactful when this, because I’m sure you are, you’re similar to me, that you’ve seen the transformation from development teams not running agile, as agile was invented and introduced and became more popular, and actually seeing the impact of that. The idea that people are suddenly saying, oh no, it’s fine.

Like it’s AI so I can instead, like you say, just have one developer will write, replace entire business with AI into, ChatGPT and suddenly it’ll all be done.

[00:14:23] Steve Burrows: Meet the new CEO. It’s Chat GPT. It’s… that’s not gonna work. It’s not gonna work.

[00:14:28] Zoe Cunningham: So how do… what are the kind of signals we can look at?

Say someone is bringing ideas to us and saying, this is a great idea. We should use AI for this. This is a great idea. We should use AI for this. What are the kind of signals we can look at to see whether something will…you know where to invest our time and efforts as a leader?

[00:14:51] Steve Burrows: I would say there’s probably a few areas, and again, at the risk of now annoying your listeners.

It’s probably saying, what is the problem that you are solving? If they can’t answer that question in a way that links it back to a user problem, and a defined outcome, not an output, an outcome. Then that’s a signal. That’s a big signal to me that the business doesn’t have the right delivery culture in place.

Other signals… I wouldn’t see data being presented forward to say, we’ve came out with this hypothesis to fix this particular problem. We launched a lightweight solution for it. Could be as simple as a tappable prototype. or some other thing just to validate with a real life user from your persona group If I’m not seeing that kind of behaviour in a company in a product delivery function, that’s another signal for me that perhaps the, there AI is more of a distraction than it is a solution.

It’s usually a signal that you’ve got someone that’s got the mindset of ‘this is incredible technology, how do we use it?’ As opposed to, we’ve got this problem to solve, how do we solve it?

Yeah. And it’s a tough thing to change, particularly for senior stakeholders because they’re the ones that read the blog, they’re the ones that have their picture on the website, the leaders of the business, they have to be seen to be on top of things.

So it’s tough for them to remember that they’re not the user, they’re not the people using the product necessarily and they should let the user dictate the direction more.

[00:16:11] Zoe Cunningham: Yeah. Although it’s, I like that way of thinking about it, because it actually gives you something concrete to, rather than replying, no, you can say great, I love your enthusiasm. Go away and bring me it back with the problem, the well-defined problem and some data around, proof of concept. And then we can talk about it.

I think that’s constructive on the people side as well as, maybe setting out your boundaries right on the… on the tech side.

Have you got any thoughts on worries people have about AI that maybe are not the right things? Because I think we’ve talked there a bit about what are the right things to be worrying about.

So are there any things where you’re like, actually there’s a lot of talk about this, but… or a lot of people worry about this, but that’s something that we either can worry about later or it’s just not in reality gonna be a problem.

[00:17:13] Steve Burrows: I mean we’re not short of media articles and social media clickbait saying humanity is doomed because of AI and we’re all gonna reporting the bot in a few years time.

That’s obviously an extreme example. At the ground level, there is this sort of undercurrent and nervousness about the impact on the job market, particularly in that layer of kind of office white collar workers that could get replaced. There’s a lot of concern there.

I suppose I’ve reflected on this a lot. Should we be worried? I suppose to an extent we should think about it but we shouldn’t panic about it. It’s not something, in my opinion that is gonna happen in the relatively near future where you lose entire swathes of the working population. We are some way off that in terms of development.

Reading Pascal Bornet’s book recently, and he talks about five levels of AI and you’ve got the base level, level one. It’s all the basic I guess algorithmic rule based automation that we’ve had for years and years.

And then at the top end, level five, which is the fully autonomous system that it’s no longer your intern, it’s your CEO. We are so far from that.

I think if it’s done right and AI is rolled out in the right way, in the right cultural, philosophical way. You will always need humans in there because AI can’t replicate and it’s may never be able to replicate what humans bring uniquely, which is creativity, empathy, feeling… And how that actually applies in the business world is important because we can think about a goal that we’ve got and we can think about ways to implement it, but we can also think what’s the impact on that beyond that pure decision? AI struggles with that.

If I give an extreme example, which isn’t a real one, I’m making this up, but if you gave a really, be a bad prompt to an AI bot and said, your job is to help us clear the stock of product X from the supermarket shelves, AI would quite happily go, let’s price it at zero then. And they’re sure, the shelves are empty, and the AI has succeeded in its goal, but the knock on effects are that the company’s lost money. A human being wouldn’t do that.

So at the moment, AI can’t really think in that sequential long term impact way. So there’s always gonna be role for humans in a good AI deployment.

So I guess the short answer to your question is we should be worried and keep an eye on it because it does have the potential to change the way society runs and economies run, but it’s a long way off from being replacing humans fully.

[00:19:46] Zoe Cunningham: And actually perhaps that’s something else to share with people when they’re maybe bringing ideas to you is that we often, don’t really understand how we are making decisions as human beings, or at least not in an algorithmic way that we can explain to something that is not a human.

Because like you say, another human will have a lot of the implicit understanding that we all share. And make decisions… Another example I heard was, ‘clean this room’ and that clean this room, an Artificial Intelligence could easily throw out a baby with, along with trash or whatever, because we’ve got all these implicit understandings of what terms mean.

And actually it’s an opportunity for us to break those down and say, what do we really mean when we say this? And actually, what is an algorithmic instruction and what is a human judgment instruction?

[00:20:44] Steve Burrows: Yeah.

[00:20:45] Zoe Cunningham: Like cleaning a room. What are the things we want a human, even if we were asking a human to do it, actually, how can we make this less ambiguous or clearer or, and essentially solve the problem better.

[00:20:57] Steve Burrows: Yeah, absolutely. And that, that, that kind of room cleaning example is good because if you approach that from a, we’ve gotta clean this room, how can we use AI? So you map out the workflow again of cleaning that room. You can identify the drudge work parts that humans don’t like doing. They don’t like the mopping, they don’t like the tidying up bit.

They might like looking after the baby whilst it’s being cleaned. So that’s how we would apply AI. Map the workflow out. Look at where it can logically pick up. Now the time that… the significant time that you’ve freed up from the human being, they can now be how use this room when it’s clean. How can we build more rooms? How can we do the creative side of things. How can we drive value from this room instead of spending time with the mop. And that’s the right implementation.

[00:21:37] Zoe Cunningham: And so just finally the flip side of that, we’re saying that jobs might not be lost, or at least not in the short term. But they will change.

[00:21:47] Steve Burrows: Yes.

[00:21:48] Zoe Cunningham: So what, if you are worried about your own job or worried about how, at all stages of engineering, As a junior engineer or a senior engineer, an engineering manager or CTO, what do you need to be worrying about in terms of your own personal skills?

[00:22:07] Steve Burrows: I would say get comfortable with AI right now because a lot of these people that are worried about their job, perhaps don’t have the full understanding of its capabilities.

So I would recommend dive in, play with agentic AI, understand that actually if you can, how AI works, I’m not talking about the actual coding level, but if you understand how AI and generative large language models, sorry, do their work and how they think, how they process, not think, it’ll help you to understand their limitations, where it can be good and where it can’t be good.

And if you can get that ground level of understanding. It’ll help you when you… it’ll not only calm you down, but you’ll know how to use AI in your job to become better at your job. So even though somebody isn’t telling you to do this, you are able to sudden get rid of the drudge work from your day to day.

Get an AI agent or an LLM to handle that for you under control, allowing you to do the more high value task. So I would recommend people jump in, learn about it, how it works, its limitations and apply it to your world. So I wouldn’t sit and wait to find out what happens. I would jump in and try and shape that for myself.

[00:23:12] Zoe Cunningham: And I love the idea that just by going in and playing around with it and and seeing what it can do, you’ll be building up this implicit knowledge in this human way that we do as humans, so that it will just shape how you view challenges and what solutions you come up with, and it’s kinda magic, isn’t it?

[00:23:33] Steve Burrows: It is. It is magic, definitely. Especially the sort of emerging agentic AI space. It’s very easy, you don’t have to be a coder these days to create agents. Have a play around. Just get stuck in, see what can, see what magic you can create.

[00:23:46] Zoe Cunningham: Fabulous. Thanks so much Steve. I think that was, just an extremely useful and precise summary of a kind of almost workbook, I think for leaders to, to start thinking and to set things on the right track from the start. And… but while iterating

[00:24:05] Steve Burrows: Absolutely.

[00:24:06] Zoe Cunningham: to go forwards. So thanks very much for joining me.

Absolute pleasure.

[00:24:10] Steve Burrows: Thank you for having me.

[00:24:11] Zoe Cunningham: This Digital Lighthouse episode was edited by Steve Folland and produced by Patrick Anderson. The theme music was written and recorded by Ben Baylow. A huge thanks to our sponsor Softwire for their continuing support from the inception of the show in 2019 to the present day.

If you love the podcast, please let us know with a rating and review on your platform of choice. We’re always looking for feedback to ensure we’re making the best show possible. And if you’d like to take part, please drop us a line at [email protected].

You’ve been listening to The Digital Lighthouse with me Zoe Cunningham. Thank you for sharing your time with us and stay safe on this wild technological ride we are all on.