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A new podcast focused on issues around technology and change in organisations, hosted by Public Digital.
Tom Loosemore (00:00)
Hello, you're listening to PD Cast, the podcast from Public Digital, where we talk about technology and change in organisations. I'm Tom Loosemore, one of PD's founders, and this episode is all about how senior leaders can actually see meaningful results from their organisation's investments in AI. I'm
joined by Adam Maddison, a digital transformation expert and PD network member.
Who is also the author of a new book entitled 'The Intelligence Era Organisation: Creating the Conditions to Thrive in the Age of AI'.
I'm also delighted to be joined by Nora Bereczkei, a product and delivery leader with over a decade of experience building and scaling internal platforms, operating models and productivity products at places like Spotify, the BBC, BT, and now at Gusto. So quite the CV there. Thank you, Nora, and hello for today and thank you for joining us on this podcast.
Nora Bereczkei (00:54)
Hi, Tom thank you. I'm super happy to be here. I'm very excited about the conversation.
Tom Loosemore (00:58)
Cool. Alright, well let's crack on, let's get stuck in. And in this episode, we're gonna be digging into the core ideas behind Adam's book to really understand what transforming your organisation to be ready for the intelligence era, the AI era. What does that look like in practice, not just in theory? So, Adam, I'm gonna start with you, if that's alright. and just the very basics. What's meant by this phrase, the intelligence organisation? What do you mean by that?
Adam Maddison (01:14)
Yeah, of course.
That's a good place to start, given it's the title of the book. I guess let's compare it to historical eras, like starting with the industrial era, which was...
an interesting period where we mastered the means of mass production. It was a heady mix of human labor and raw materials and industrial machinery and quite a lot of burning of fossil fuels. Maybe we'll come back to that regarding AI. That led to this extraordinary world that we live in. But there was a lot of hard manual labor involved in it and a lot of people
worked really hard to build this world that we're still living in, still got the industrial era machinery around us all the time. But then that moved into the information era. I keep referring to it as the internet era. Maybe we'll talk about the internet era and what that means for you Tom. But the information era where the advent of computers, connected through networks.
The invention of the internet that completely changed the world around us and completely changed the work that a lot of us do. It involved the digitisation of data and information and the democratisation of access to that data and information.
but they were still heavily dependent on human labor. So we talk about knowledge work, we talk about cognitive load. There's still a lot of human labor involved in getting the real value out of that information. Humans doing the heavy lifting to synthesize data, to design the systems, to write the code if we're talking about computer software, which quite often do in this world. But now we've moved into the intelligence era where
advent of AI. AI has been around for some time. told you, as you know, I studied AI some 30 years ago. But it's obviously, you know, taken off in the last few years with the advent of large language models and agentic AI and various other things, and maybe we'll touch on what some of those are. But for those of us lucky enough to work in that world, it's brought that
analysis of data, that synthesis of data to our fingertips. We can write new code, we can analyse large data sets, we can produce images and videos and front ends of software at the touch of a button. And so we've started to outsource for the first time a lot of that cognitive load, a lot of that heavy lifting to machines.
But that leads to all sorts of questions. And I'll get some of the questions that I tried to touch on in the book. If we are outsourcing to AI, when should we do that? Should we actually do that at all? What might be the protections in place? What might be some of the conditions that we need to make the most out of AI? And I think that's a particular interest to me is like what happens to the people in that world?
let alone what happens to our fossil fuel dependency, which I touched on earlier. the ability to outsource really quickly, like heavy cognitive burden, has all sorts of implications for how we work these days and how we need to set ourselves up to work these days.
Tom Loosemore (04:09)
mean I think the for me the the best definition of what AI lets you do today is that you've got essentially infinite interns at your at your fingertips, for good or for ill. I mean I you touched on what's the role of people and what the conditions need to be to make the most of it. Could do you give us give us a flavour of what those conditions need to be like to really make the most of the opportunities that are there from AI? Give us some
Adam Maddison (04:19)
Yep.
Tom Loosemore (04:30)
examples.
Adam Maddison (04:31)
building on that infinite number of interns, so as well as doing all sorts of transformation stuff, I coach people and one of my coaching clients was talking the other day around, he works in, in local authorities, in social care, but using AI in a startup world. And he said, well, you wouldn't get an intern into a social care organisation and just give them the most complicated caseloads that you could find and hope that they solve them.
And I think some of those things are true for AI. We wouldn't do that with AI. We wouldn't give it the most complicated problems and without any guardrails and assume it could just solve the problems for me, for us, come up with brilliant designs and brilliant solutions.
AI should be looking at having the ability to learn quickly. AI is brilliant at producing output really quickly, but if you don't have a feedback culture, if you don't have a learning culture, then you're just producing outputs and you've no idea what the impact is of those outputs. So those sorts of cultural things need to be in place as well.
Tom Loosemore (05:25)
Fascinating.
Fantastic. Thank you. That's a great analogy with the industrial era as well in your first answer. So thank you for that. Nora, over to you now. Does it do any of these ideas, these concepts resonate with you in terms of your experience?
Nora Bereczkei (05:40)
Very much so. I have to say I loved Adam's book. I really, really enjoyed it. I thought it's a really good kind of starter for ten. What do you need to think about when you embark on a transformation like this? I think for any leader who's starting a journey like this, I would recommend it's a really good baseline of what you need to be mindful of. I find it very relevant. How I think about transformation is...
It's really hard to be very clear on what success looks like, what you need to deliver throughout your transformation program. But also sometimes it's quite hard to articulate whether you're making progress on achieving it. And I think, Adam, you touched on your book that sometimes we set ourselves milestones and output based milestones just to kind of give us a...
kind of sense of control, which I think I recognise from different stories in the past. How I tend to think about it is it's really helpful to simplify as much as you can on what needs to be done and why. And I think about it in three facets. One, these three facets are kind of like process, technology and people, if you like, just to allow us to have simple conversations. And when you talk about
Processes, I mean, was in my intro as well. I'm very passionate about thinking about our operating system, like the collection of processes, rituals, the languages, the feedback loops, roles and responsibilities and the accountability models as a product on its own, as a product itself. And arguably, I could very easily make the argument by, I would say that, that it's one of your most important products as a company. It can be a differentiator.
Building the right thing is super important, but building it right can be kind of that operating leverage that we'll make or break a market or your competitive kind of leeway or headway. So I like to think about processes and the collection of processes, your operating systems as a product that has many uses, many use cases, and you have to go through quite intricate balancing and prioritising and
conscious conversations of what do you optimise for? And I think that is a really important first conversation to have when you embark on transformation, because once you can start talking about what you're optimising for, your trade-offs will be very, very clear as well. And then you talk about high difficulties to kind of keep the current set of bureaucracies and funding models and...
reporting lines and governance while trying to build something new. I think if you start with, okay, what do we optimise this system for? And what are we by default, then, prioritising can sharpen those conversations and help leaders identify what they need to leave behind. But also can help you identify iterations and your roadmap on how you might get there.
The second piece is technology, which Adam, you also really nicely talk about, you know, these are your internal platforms and productivity products, your CICD pipelines, your work management system, your architecture that will enable those small nimble teams that we believe is an underlying enabler for a transformation, but also having that really robust and easy and accessible data layer.
which is super, super important before, but now with AI and agentic capabilities, without that, you won't be able to prompt yourself out from your data mass that you built yourself into. And I think that is quite often has been overlooked up until now because we could stand prioritise it, we could kind of patch over it and we could...
make shortcuts or hire more analysts, for example. And that is very, very, very much in the focus of every organisation who's trying to embark on an AI-driven transformation or rethinking how they might work in this new era. And the last one is people which I think I really liked, Adam, how you talk about the roles of leaders and middle management.
It's very easy to kind of talk down and blame for self-preservation and being that kind of, what do we call it, the iron ceiling that you can't look down and you can't look up and stopping the grassroots excitement and the leadership sponsorship on the top. But in reality, they also have a lot of different things that they balance and maneuver. And I think when it comes to people,
biggest thing that we often don't talk about when we set out a transformation is what are the behavioural incentives that we build out, which are what are the rewards of time, praise, positive feedback, a seat at the table, which I think is super, super important. But also one of the key incentives is
What are the metrics that we measure? What are the metrics that we leave behind? Which is a beautiful moment of burning the bridges and letting go of what we used to be. The set of metrics that, you know, Adam, we talk about funding models and I don't know, the return on investment of project X, Y, Z in our plans or annual budget cycles or whatever.
and what are the new metrics that we are going to start cherishing and talking about. And I think that again, if you can have those conversations with your leaders and your sponsors and in all the different parts of the users of your operating system as a product can be a really big enabler. So we don't want to walk down on the transformation path that will be hard to walk back on by the time we find that actually some of the resistance that
we are working against this part of our own making because we haven't had those upfront conversations, you know, this also should have a discovery phase and we discuss, okay, where do we want to go? And how do we know? My God, that those are the metrics that we should be.
Tom Loosemore (11:36)
really like the framing around people process technology and there's so much emphasis on the technology and so little emphasis on the people and the behaviours and incentives. And to really get the benefit from AI, you are not going to be applying AI to your current process,
Or your current organisational model, you're going to need to reinvent those fairly fundamentally. And that's going to challenge people's empires, people's incentives, people's metrics. And so the behaviors, the leadership behaviors, senior leadership behaviours, which why Adam's book is so brilliant on, need to recognise the the fact that those things senior leaders need to allow fluidity and adaptability in organisations that have been rigidly focused on often on just efficiency.
I love the idea of saying goodbye to old metrics. that's a you know, you only really transform things when you've turned the old things off. And you've only really done an AI transformation when you've turned the things, the processes, the org model that predated AI off, which takes a while. Anyway, it's not meant to be about me, it's meant to be about you. So Adam, just quickly, where did the idea of the book come from? Where's this what's the genesis of this?
Adam Maddison (12:33)
Yeah.
You say just quickly, it could be a really long answer because I was thinking about this as I was writing it and thinking about it over the last few weeks and months. I think the genesis of this started a while ago when Tom, you and I both met at GDS. I was working at Government Digital Service. I started there right at the tail end of 2013.
And that was a place where it was very much a principle led organisation. you had the things that I would like to see in an organisation, the things that I talk to people these days where they say the culture is amazing. We had small empowered teams. We were obsessed with users or customers, whatever you want to call them.
there was a pressure to have these fast feedback loops and access to high quality data that you touched on Nora and this culture of learning. And it was an extraordinary place to be. So I was looking out across other parts of government and looking at other organisations. And there was this obsession with large scale agile frameworks. The scaled agile framework SAFe was the most notorious one at the time.
And that sort of industrialisation of, how do you transform an organisation? And so things like SAFe and Large Scale Scrum and various other things were around. And people were selling this. Consultancies were selling this. But mostly because I think organisations were looking to buy it. They were looking for the quick fix. And we'll come on to AI as a quick fix in a bit.
So I used to do conference talks on how we were principles-led, how we tried to set ourselves up, how we learned from various different ways of working and we would try things out and they would work or they wouldn't work and how we shared those through communities of practice. And a lot of that has appeared in the book like 13 years later. But I guess there was an additional frustration more recently.
And I mean, particularly of interest, because my view of the product operator model, it stems from a sort of readings, perhaps a misreading of the Spotify model. Nora, you worked at Spotify. You would have seen what this looked like in practice sometime after it first appeared. But we've seen and in fact, know, Nora, when you and I were both at BT, we saw this attempt at
this wholesale adoption of a product operator model, which actually looked like, let's rename our teams. Let's just move some people around in spreadsheets and let's not actually change any of the underlying conditions about how teams work and how information flows through teams and how they get assigned work. And let's hope that we transform the way that the organisation works. So a wholesale adoption of somebody else's model.
still seems to be a very popular thing. And I touched on, you know, the conditions that we think are really useful for teams to do brilliant work.
It's like organisations can outsource their transformation. They're thinking, we don't have the ability to deliver really good products and services very quickly. Our developers aren't coding quickly enough. Our designers aren't designing quickly enough. It's nothing to do with the structural issues of the organisation, with the funding models or the empowerment or the control or the culture or anything like that. It's to do with people not being able to type fast enough. AI can type really quickly. Let's just outsource everything to AI. AI is going to be the solution to our organisational problems.
AI can do all the cognitive heavy lifting that I touched on earlier. But the problem is the same underlying conditions aren't changing. last year, towards the end of last year, both Google and IBM did a few studies and were looking at
where AI was successful in transforming organisations and where it wasn't, where the return on investment was coming from, that you touched on, Nora which companies were gaining significant return on investment from AI and which companies weren't. And the results of their studies...
It turns out that it's pretty much the same conditions that I was talking about 12, 13 years ago when I was talking at conferences about how GDS operates. It's the same conditions that are necessary for companies to make the best use of AI. It's around small, empowered teams and access to data and fast feedback loops and a culture of learning and experimentation. And so I wanted to write something about A, about my frustration, but B, about
I know this works and I know it's amazing and the opportunity is amazing. I remember the day I first walked into GDS in 2013, like the culture was extraordinary. You were there Tom, it was just this vibrant place. It was an exciting place. And so if we can help organisations actually get to that position, then it's better for them as a business and it's better for their people. It's more exciting place to work. You deliver brilliant products and services for your users.
It's just really good place. So try to distill all my knowledge, experience of what works, what needs to be done in order to learn what works. I guess that's probably the biggest thing is that culture of learning, that culture of feedback. And actually produce something which... go on Tom.
Tom Loosemore (17:00)
Well I I I think that
I mean I just want to congratulate you on having done a really good job, not just of writing a book, but writing a short book. And those messages around small empowered teams that have clarity of outcome that they're seeking
That have access to the data, that really have a cultural learning and expectation of fast feedback. that shines through. And particularly I think the challenges around what the incentives and behaviours you need on leaders. And I mean, Nora I'd love to hear your experiences around senior leadership behaviours that really give teams the top cover that's needed to make the most of things like AI, to allow them to to thrive.
and learn quickly, safely. I'd love to hear some examples from you on what leaders need to be doing and not doing in order to enable the ideas that are in Adam's book to really shine.
Nora Bereczkei (17:46)
I think probably the cheapest and most important thing that you can do is to go down to the factory floor and see how things work in real life. feel like, you know, as I've described, buying a cookie cutter solution. I think you would be more mindful about how applicable or non-applicable there are if leaders could go down and spend a week with
different types of teams. I think that would really help empathising with the challenges that they have and the opportunities that we could easily capitalise on. I loved kind of Adam talking about the picking your pilot teams. Find a team that has some of the opportunities already there. In the past, what I've worked really well for me in one situation is find the pilot team that feels the most pain.
at the time. So they will be the most enthusiastic to hop on any journey and be the guinea pigs because the cost of the current system is higher than experimenting with something else. So that's also quite a good way of picking a pilot team, but you can only do that if you spend some time with the teams. And I think that's quite often overlooked. We spend more time talking to different companies who are offering their services to solve our pain than
going and understanding the pain firsthand. I think that is very important. And then we can also identify what the blockers are as we go through. If once we have that interface, I think it makes it much easier. I also would recommend thinking about your user groups, because it's not just companies are different and therefore what worked over there won't necessarily work over here without the right adaptation.
but also our teams are very different within a company. So if you are a platform team, your needs and must are very different than if you're nimble, a front end experimentation team. And I think that's also quite often overlooked because we want we embark on a large transformation program, which we only see the hill.
right, and we want to be on the top of the hill as quickly as possible. So we can kind of celebrate success, but quite often, instead of climbing, kind of this wave like approach is much better. What are the first step, second step, third step that kind of build on each other, and also prioritising, which is a difficult, it's a difficult thing to do. Like we having this conversation and just so we are embarking on a very exciting transformation.
to experiment with how we can become AI native. It's a super exciting time to be and we're also having this conversation of what do we want to do? Do we want to look at our team portfolio and the different team profiles and uplift everyone to step one? Or do we want to kind of give people and teams a bit more space and freedom to identify their own journeys?
And if a team is further ahead, give them much more leeway to experiment while different teams who might have other risk profiles, depending on the work and the context that they have, take a slightly different approach, perhaps a more cautious approach. And all of these take a lot of time and consideration. I think, you know, Adam and I found that when we were together at BT, what makes it quite difficult is
It's a significant undertaking and quite often, while sponsorship from senior leadership is a must, you can't do without it. It can also put pressure on people when they try to find the quickest way in that they can show results, which is a really important communication tool in change management. But we must carve out the time for discovery to identify our user segments.
the different types of users and teams that we've got, the different functions, different areas in the business and create that kind of road, met and pathway depending on the risk profile and the context of where I want to be, which is not an easy job. And I think Adam's book is a really good first kind of quick pathfinder, if you like.
Tom Loosemore (21:48)
There are a couple of things there that really resonate with me and what you just said, Nora, that the importance of sort of building on different phases, that you take what you learned in the early days, build it into the next wave, build it into the next wave. So you've got a feedback loop at the heart of your transformation journey and you don't just have a singular plan. I mean I remember Mike Bracken, the chief executive of GDS, when I would introduce him to sort of new people, he would always start by saying, Well, my job is actually to say no to lots of brilliant ideas that come through our door every
Because we have to focus on gov.uk, we have to focus on the platforms, you know, the really important things. So the ability to prioritise what you're doing when in your transformation journey and have a sort of theory of change around that that you learn from thinks really, really important.
I'm also really struck by
the sort of leadership behaviours around cookie cuttering, what worked elsewhere, and I love the idea that you've really got to look at the teams and where they are.
of individual team level and think what freedom should I give these people, what boundaries should I be setting around them? What stage are they at? a team is an organism. And I mean Adam, I really like the way in your book you're really empathetic towards senior leaders around what gets in the way. and you know, and one of the things that gets in the way of product operating model is it's kind of seen as a one size fits all magic cure. And it's only really a few others that sort of dig under and say, how do you actually make this work in practice? And
Adam Maddison (22:54)
Hmm.
Tom Loosemore (22:59)
I'd love to hear from you a bit about what does get in the way of organisations. I mean the theory of like you need an adaptable, multidisciplinary, fast feedback culture is quite widely embraced, but what what actually gets in the way and what do you do about it as a senior leader?
Adam Maddison (23:06)
Yeah, yeah, yeah.
Yeah, that's, there's so much, mean, read the book, Tom. No, don't do read the book. You have read the book. building on some of the stuff that Nora said, I really liked the idea. And you talked in your first response around, you know, treating an organisational design as a product and going to see the team and treating those people as users. The sort of things that we would naturally do when we're building a product that we're putting out to customers or external users.
Tom Loosemore (23:17)
god the book.
Adam Maddison (23:35)
having the same respect for our people and the same respect for our processes internally is a really powerful thing. But to answer the question, so the sort of things that get in the way. It's like the legacy culture of an organisation is such a big thing. You know, I mentioned that we worked in Government Digital Service. The civil service is hundreds of years old.
And so the legacy culture is such a big thing. The culture of fear amongst leaders of looking like they've done the wrong thing, that they've invested money in the wrong area. The culture of blame that goes with that and finger-pointiness and the impact that has on the ability of teams to actually deliver stuff, knowing that getting something wrong might actually be okay because you're learning. That culture around fear and blame.
And the good news culture that is so pervasive in so many organisations, you cannot say something is not going well, you have to say positive things. And that's such a detriment to the organisation. If you promote and encourage and reward that sort of culture, only come to me with good news, only say yes. The antithesis of Mike Bracken saying no, or this isn't working or something else.
So you've got that legacy culture and legacy structures associated with that very hierarchical organisations.
Particularly we've seen organisations that have grown by acquisitions and mergers, you get this dissonance of different hierarchies and different cultures and different reward systems, sometimes explicit reward systems, sometimes implicit reward systems. Who is actually rewarded for what behaviour? What sort of behaviours do I need to demonstrate in order to get to the top of this shop?
Nora Bereczkei (25:00)
and
Adam Maddison (25:06)
Those sorts of things are really difficult. then, you know, project funding and annual funding could probably talk for hours about funding and how you fund.
Tom Loosemore (25:15)
five minutes on funding, 'cause I thought that was a particularly strong bit in the book. And it sounds boring, but I'm totally with you that it's right at the core of what gets in the way of really embracing the principles in your book.
Adam Maddison (25:25)
It is, yeah. So the idea that you have absolute certainty of what the outcome is going to be, that you have absolute certainty over how long this piece of work is going to last, that it is project-based and therefore, you know, the traditional definition of a project is it's a time-bound organisation that will disperse at some point.
just goes in counter to the idea that when you're first starting out, you talked about phases of delivery, when you're first starting out and you're literally learning what the problem is that your users are facing, whether they're internal within the organisation, if you're building something internal, or they're external customers, you don't know what you're going to be building. You don't know what you're going to try and do to solve those problems. So you don't know what you will need some point down the line.
And then you start building stuff and you start releasing stuff and you start learning whether it's solving the problems that your users have or it's helping them achieve the outcomes that they want to achieve. That is a continuous process where you're continually learning and there may be a point where you get to point where you go, okay, we've built enough and we can move on to something else and that could be time bounded.
But most of the time, you should be devoting a team to continually improving the way they work and the products and the services they deliver. And that requires just long-term funding of the team. The team will change and your focuses and your priorities may change, in which case you can move funding around. But you have to respect a team that is long-lasting, that is learning about its users and its customers.
Nora Bereczkei (26:47)
But I think sometimes we forget to give people time to grieve because these practices, these legacy cultures exist because once this is what was needed to make the company successful. And I find that we need to give people some time to acknowledge that this was brilliant and you have become a successful leader.
in this structure and this was very much necessary for the company to be what it is today. However, environments and circumstances have changed and now we need to evolve. I feel sometimes very tempted and sometimes guilty to arrive to the conversation of like all of this is wrong and you are wrong and what you're doing is wrong and I'm going to tell you what right is. And I think that's why I think the empathy that you showed in the book is really important because
sometimes and that's why kind of you talk about the fight for relevance. If someone is like telling you that from tomorrow, this will be an organisation that it's transformed what it was today is no longer applicable. And your practices and behaviours that made you successful up until now, will have to go and we don't know what the new one is, because it will be an iterative process. I mean, that is quite difficult for any human to like be excited about and not feel like
can I reserve like what I understand? Like, I have learned to pull these five levers in the past 20 years, what do I pull now? And we quite often don't have a very good answer. It's like, well, you know,
Tom Loosemore (28:14)
mean I think that's a really great pushback on the idea that, you know, in with out with the old and in with the new. I like the idea of grieving, but I sometimes use a couple of different analogies to say, you know, that many organisations thrived during a period of relative stability of user expectation and behaviour and
they succeeded because there was relative certainty. And you optimise for efficiency when you've got relative certainty which is why you get projects and why you get some of those behaviors. And the know-ability in advance of an investment is greater when you haven't got things like LLMs emerging like every day that kind of change the weather. What AI has done for me, has changed the balance between needing efficiency.
as your primary and only real goal as an organizing principle to needing adaptability and fast feedback and learning what works as the primary goal. That's often the narrative I use. But I've never grieved for an efficiency based project culture. And maybe maybe we should, I think that's a really interesting idea. Have a wake.
Nora Bereczkei (29:12)
think
I've seen meetings when
people kind of on their faces, they have this kind of fear and hesitation of, I want to be excited about, but I don't know if I can or should be. And I think quite often, because of the pressure of times and showing results, I feel like I have to be very mindful of carving that time out. And also these are the cultures when having these conversations is harder by default, you know, like
What are the behaviours that we need to have? What do we reward as leaders, sometimes subconsciously? And if this is not the culture that your company has today, that will be quite a while until we can have these honest conversations. And without those conversations, we can't set up the environment for us to succeed.
Tom Loosemore (29:55)
Complete.
Adam Maddison (29:56)
there's a lot of fear and that fear leads to paralysis and then perverse behaviors like we know we need to change. People can see the existential threat out there. I start the book with the anecdote about octopus, which is only 10 years old, probably now 11 years old, and is now the largest utility provider in the UK overtaking British Gas, which is 150, 160 years old.
And so people can see that there's this exponential threat, but yet, what's the immediate solution? The immediate solution is, I can just outsource this problem, because I don't know and I don't understand and I'm scared of. And so hopefully, book and the steps and the guide.
helps people realise that there is a sequence that one can go through, some things that people can try, some of which may work and some of which may not work. That's okay. As long as you're doing that with the idea that we're embedding a learning culture, I think that's a really important part of it. We can try some stuff and we can learn. And in doing something that may fail,
actually if we're talking openly and honestly about that and embracing that then you are embracing a learning culture and that's part of the transformation that's part of the first step to get away from this control this certainty is this learning culture I think you need to get to.
Tom Loosemore (31:06)
know, the perils of false certainty are very appealing for people. They're a dangerous reef that you're drawn towards on your ship of change because you think it's going to be solid ground and actually it's gonna rip the bottom off your ship. That's a terrible metaphor, but let me ask you an unfair question, Adam. And actually Nora, I'll leave I'll leave you a bit longer to think about this one. you know I like things to be like really short.
Adam Maddison (31:08)
Yeah.
Nora Bereczkei (31:09)
Yeah.
Adam Maddison (31:19)
Okay.
Thanks.
Yeah.
Tom Loosemore (31:31)
gonna ask you to make things impossibly short now, which is if I was a senior leader and senior executive in a commercial company and I'm I haven't even got time to read your book, but I'd like you to tell me one thing to change in what how I lead tomorrow, what would that one thing be?
Adam Maddison (31:33)
Okay.
Okay, I think you've both touched on this earlier. The single biggest barrier to transformation isn't, it's not technology. Technology is a solved problem in many regards and technology will keep accelerating. And actually it's not funding. I think funding can be solved. I think the single biggest barrier to transformation is culture and that culture is driven by what leaders do, not what they say.
people model the behavior of their leaders, they model what is necessary to get to the top of this shock, as I said earlier, which is often in historic bureaucratic hierarchical organisations, it's fear and various other things we touched on. And so it's what can one do to change the culture? And as a leader, it's about demonstrating different behaviors. It's about demonstrating trust. There's a whole section on trust.
which is a sort of, some people might think it's a soft and fluffy thing. It's vitally important. I need to read this because the figures are extraordinary. In organisations with high trust, employees are 260% more motivated. Turnover is 50% lower.
And recovery from setbacks, like when things go wrong, high trust teams recover 26 times faster than low trust teams. And if think, a team hits a setback with a problem with some data and some AI and it takes a week for that team to recover, in a low trust team, that's six months of not being able to recover from that setback. So trust is like the fundamental thing. Is that a short enough answer for you, Tom?
Tom Loosemore (33:04)
so TLDR is like find ways to exhibit that you're going to trust a team to to learn. Cool. Nora, you got extra two minutes to answer that question. Over to you.
Adam Maddison (33:09)
Yeah, thank you.
Yeah
Nora Bereczkei (33:14)
Well, I can't decide between two. think it's system thinking? So I think all of the different bits and bobs that transformation will touch connect to each other and reinforce each other. So if you have trust, but you don't have the right...
Tom Loosemore (33:20)
what does that mean? What does this think mean?
Nora Bereczkei (33:33)
funding model, you will still struggle, right? If you have the right funding model, but you don't have trust, you still struggle. If you don't have the right technology, you might have the right behaviors, but you still can't realise value as and when. So kind of understanding what are the different moving parts, I think is really important and kind of putting your bets, distributing your bets to the highest priority areas. And then the second was I was going to say is focus, which is kind of intention with each other.
Because one thing that I find that is difficult is that we quite often still want to keep the operational excellence that we have today while on top of that doing transformation. And I think that is also making it harder to leave things behind, because we don't have to leeway, the space and leeway built into the system. And we know that when change, when going through a change, there will be a learning dip, there will be a performance dip.
And I think quite often as leaders, we don't want to acknowledge that. So we keep pulling the pressure lever that we know well and has been working in the past for us. That's how we got here. While also trying to pull the transformation lever and then kind of like the brake and the gas are both being pushed at the same time. So, and I think, you know, that is really difficult because leaders are also under pressure.
So it's not an easy job for anyone.
Tom Loosemore (34:55)
I no, I mean, trust me. there's a reason I became a consultant. it's a really hard job and much respect to leaders doing it. The trade offs are really difficult and the legacy culture you have to still live within. I've got so much respect for the bravery of leaders who are actually trying to change large legacy organisations. It's one of the hardest jobs out there. cool. So getting towards the end now,
just as a couple of things that I think Adam you touched upon at the start, which is, if you've been following me on LinkedIn, you know I've got very excited about AI agents and agentic stuff. can you touch upon that? It'd be really good to get your perspective on agentic AI and where you think it might be going and what what should organisations be doing?
Adam Maddison (35:31)
Yeah, kind of deliberately stayed away from talking about what AI can do. As you said, it touches on it at end of the book, because even though started a PhD, didn't quite finish a PhD, started a PhD in AI many years ago, it moves on so quickly. But I work with teams, I'm working with the team at the moment that uses AI internally, not to write code, but to help manage the team.
The ability to generate prototypes, to prompt ideas, to write code is extraordinary and it's just accelerating so rapidly. I've loved watching Tom create finders for petrol prices based on dodgy data coming from various services.
Tom Loosemore (36:10)
Official government service
API that was. Official government API. Yes.
Adam Maddison (36:13)
It's an official government API. It's very good. Sorry,
not dodgy. It's a very good API. Access to high quality data is obviously vital. And actually, you and I had not an argument, but a disagreement over the role of data in the book. And actually, you reminded me, AI specialty is to find patterns in really horrible, messy data. So there's huge opportunities. And those are only accelerating.
And there are all sorts of things that people can do around prototyping and there's all sorts of things people can do. know Octopus are looking at hyper personalisation, so building and delivering services that
know the customer intimately and when a human at Octopus gets in touch with the customer, they have in front of them really clear understanding of the client that they're talking to. It's not just an anonymous person at other end of the call, which makes the customer feel really special, which is amazing. And that's why they are the most popular company out there, because of their ability to do that. I guess I would counter.
At the same time, the possibilities of AI are just accelerating. As you said, Tom, know, things are moving so quickly. I also think it's really important to try and remember why you're using AI and what about the humans in your organisation. I've talked to people that...
various organisations that we would consider to be internet era organisations and they feel under threat from organisations that they describe as AI first, like literally started in the last year or six months that are just building software, building services, building products entirely by AI. Like, I don't know, a two teenagers in a bedroom type company that will do amazing things. But I think it's really important
and touched on humanity a couple of times. I think it's really important that organisations are collections of people and that we still use the opportunity to find really interesting, empowering problems for those humans to solve. And I think probably AI affords even more of an opportunity. You can take away some of the drudgery from their jobs and give them really interesting problems to solve. And so actually, I think the possibilities for AI.
are really positive and they can drive really good culture in an organisation.
Tom Loosemore (38:12)
I couldn't agree more with that. I think the the the opportunities come from really being bold about reimagining the role of the human in solving the problem in a radically different way rather than bolting it on to an existing process and I think it's relatively rare to find organisations that have reached that realisation of just how radical they're going to need to change, what the how exciting the opportunities are for them to deliver value in brand new ways, if they can break out of their legacy culture and structure and behaviours. But Nora, I'd love to hear from you. Your reckons
on all things where AI is going, where it's useful, where you think the challenges remain.
Nora Bereczkei (38:43)
There are multiple fronts, right? So you can think about personal productivity, which we talk about, you know, how we can enable engineers to be faster, use agents as much as possible. You can talk about organisational productivity, where especially for kind of older and more legacy organisations, heritage organisations.
There are a lot of different systems that they have built over time that it's quite difficult to integrate while you can prompt over these differences. So I think there is a big opportunity for understanding where these organisations can overcome internal human glue type of work that
we know exists and it pulls on productivity quite a bit. And there's also kind of what are the offerings that we can do within our product to our users to make it more relevant with the new opportunities that we have. I think when it comes to organisational productivity that I'm most excited about.
I think Adam touches on it on the book that AI will amplify the good, the bad and the ugly. And we don't always own the complexity of our internal structures and systems. We quite often offload that complexity to the teams or the individuals. And I think this is the point where that's no longer an option.
When it comes to employee experience, you might be able to offset it in different ways. When it comes to agentic experience, you can't. It will be expensive and it will be very obvious. The incentives to invest into developer experience might have been lower than investing into agentic experience. I don't know what that says about us as humans, but that's where we are.
Tom Loosemore (40:26)
Yeah.
That's a rabbit hole that sadly we haven't got time to go down. Fascinating though it would be, but well, thank you. Sadly we're out of time and I just want to end by saying thank you to both Adam and Nora for a really fascinating conversation and thank you for going down a a really interesting set of avenues to understand the role of AI in large organisations and what you really need to do to make the most of it.
If you want to know more about how you can transform your organisation to be ready for the Intelligence Era, for the AI era, whatever you want to call this era, copies of the book, 'The Intelligence Era Organisation' are available for order on the Public Digital website. Get yours now if you haven't already. And if you've enjoyed this episode, and we hope you have, and you want to hear more, you can find other episodes of PD Cast.
On Public Digital's website, public.digital or your podcast platform of choice. I've always wanted to say that. and just to finish, thank you all for listening and hope to see you for the next PD cast. Thank you.