In this new episode of CGI’s AI for Industry podcast, our host Shas Ruparel is joined by Cat Powell from Novuna and CGI’s Raj Lewis to discuss what it really takes to move from data foundations to AI impact.
They explore how organisations can identify the right problems to solve, prioritise effectively, measure value earlier, and build the culture and capability needed to scale innovation responsibly. The conversation also looks at the balance between ambition, governance and patience as businesses turn data strategy into real business outcomes.
Speakers:
- Shas Ruparel – Director of Consulting, CGI in the UK
- Cat Powell – Head of Data, Novuna
- Raj Lewis – Strategy & Innovation Director Expert, CGI in the UK
- Transcript
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Shas: Hello and welcome to this new episode in our CGI AI for industry podcast series. I'm Shas Ruparel Director of Consulting at Asset Finance at CGI, and I'll be your host today. In this episode, we'll be exploring what it really takes for organisations to move from data foundations to real AI outcomes, from identifying the right use cases to scaling innovation across the business. I'm delighted to be joined by Cat Powell, head of data at Novuna, a leading UK provider of asset finance and financial solutions, and Raj Lewis, AI strategy and innovation expert at CGI. Cat, Raj, welcome.
It's great to have you both here. Let's jump straight into the conversation.
So Cat, looking back at Novuna's data journey, when did data start to become a real strategic priority for the business?
Cat: So we've been on quite a journey over the last, I'd say, three years with our data really. Everything really kicks off in 2023. And at that point I'm talking about Novuna as a whole group. We started replatforming our data into the cloud. And with that came an incredible effort from our central IT data team, as well as an awful lot of work across the five business units that make up Novuna as a wider group. We started doing an awful lot of work to assess data maturity, to implement a data governance framework and the systems that go with that, but also to migrate all of our data into the cloud and implement new data solutions to allow us to manage that. So we've put Databricks in and we're moving most of our reporting from current Oracle solutions onto Power BI and Databricks.
Within business finance itself, really we started pushing that forward in 2024. So making sure that we had the right data capabilities to drive decision making and efficiencies, we formed as a small team, my department, and brought a bunch of experts, which we already had within the business, as well as some new recruits together to ensure that they had all the right skills, all the right systems to really let us push on, and we haven't really looked back since.
Shas: Great. So you've certainly been on quite a journey so far, so really excited to hear more about it. I think one of the challenges organisations often face is identity. Identifying the right problems to solve with data and AI. So Cat how does Novuna approach discovering the right use cases across the business?
Cat: We've got a number of different routes, if I'm honest. And the more I thought about it, the more different options and ways of identifying new opportunities I could think of. From our point of view, within the data, insights and automation team, and I'll talk about automation probably a little bit more than I'll talk about AI, because for us, we're trying to take a much more automation first approach rather than jumping straight to AI. And there's an awful lot of work still to be done with our data, before we get too deep into some AI solutions. But in terms of identifying use cases, we're currently managing three backlogs across data, automation and decision modelling - so automated credit decisioning. And we get contributions to those from all of our business teams, be that sales or operations, and of course credit.
But we also get a really interesting pipeline of input from a continuous improvement team that we have within our change delivery department. They work really, really closely with focus teams to really get deep into their processes and look for opportunities for improvement. And that's been a really interesting feed of use cases into the data side. We're also identifying some use cases from the work of other product teams that we've got within business finance, particularly the digital services team that focus on that digital customer journey that we're really proud of to have built within business finance.
We try and make sure that all of those avenues come together, and that we manage those backlogs and keep them aligned to business strategy, and aligned to each other and that product delivery. So there's a lot of different routes in there. Finding the right ones means we need to look at benefits but also speed of delivery and any barriers that we might have there.
Shas: Super thanks Cat. And how do you do the prioritisation across those three backlogs. Who makes that call in terms of what gets developed next?
Cat: I think prioritisation is one of the biggest challenges that we've seen, particularly in the last 12 to 18 months, as we've kind of hit a level of maturity and a kind of speed of delivery that means we really need to be hot on that prioritisation. And I think it's one key point I'd be keen to make is, you need to keep iterating over how you prioritise.
What you do today might not work for your business in six months’ time in terms of prioritising that backlog. And that's definitely been a lesson we've learned, and we keep flexing it to make sure it works for us. At the moment, we're managing the prioritisation through regular roadmap reviews with each of the senior stakeholders in the business - that's SLT and senior managers. We have a monthly Change and Innovation board to allow us to ensure that everything prioritised aligns to the wider strategy, and there's a bit of cohesive thinking across the group. And we also align those backlogs between the delivery teams themselves to manage any dependencies and ensure that that's rising things up as needed.
All of that is fed into by that benefits assessment, and that assessment of delivery effort I mentioned just before. And then of course, we get to execute the delivery and make sure that we're feeding back into that loop, getting that MVP out and iterative delivery to ensure that that keeps things moving. That's been one key bit around our prioritisation, is not to maybe assess things as an absolute whole and a full delivery, but break them down into chunks and that often means we can justify moving something up the priority stack sooner, get some benefit from it, and then keep coming back.
Shas: Yeah, that sounds great. And it feels like you've got business agility in the heart of everything you're doing at the moment, so you're almost being able to respond to those market changes, those trends, and reprioritise and pivot as you need to. Raj, you work with a lot of organisations on their data and AI strategies. Does Cat’s experience reflect what you're seeing more broadly?
Raj: Absolutely. And I think the journey Cat’s described is probably one of the mature journeys that we see across organisations. And organisations exist in many different shapes and sizes and different points of their transformation journey. A few key things to call out from what Cat mentioned is I really like the way that she mentioned the continuous improvement team is hunting for those kind of internal process improvement opportunities within the teams, and secondly, backed by the digital services team, really obsessing over what's the right digital customer journey, the digital customer experience, so we're kind of making sure that any journeys or experiences we're putting out into the market are really getting customers what they need, and we're kind of tying it back to value.
What that means is that they're finding the right use cases, and we're really tying it back to colleague value to customer value. And that's kind of the first thing that we look for is: how are organisations really filling the hopper of ideas of use cases to tackle?
Because one of the pitfalls that we do see is organisations get a little bit too obsessed and distracted by the technical back end and the core platforms and the governance and the processes and the architecture, etc., which is fundamental, and is really, really important. But what we believe in is taking a use case driven approach and an incremental approach to delivering transformation and improving maturity at the same time.
So let's not wait for the perfect back end before we can start innovating at the front end. It's finding the right use cases that are right for tackling that really link to user value, to colleague value that allow us to do this as typically and it's almost verbatim from a client that we spoke to recently: they're asking for a data strategy that's not about spending 16 weeks mapping everything, and all you're going to get is Visio maps and architecture diagrams and PowerPoint slides, is when within this time we need to see value released. And that talks to another important point around leadership getting quite impatient for results. So data and AI is not something that just been born in 2026, it’s heightened ever more so. But people have been tackling this for a while, but we're still yet to get that real value release. So how do we make sure that we're really kind of getting those tangible use cases that link back to colleague value, to customer values, who are able to demonstrate meaningful progress along the way. So a lot of great things in the Novuna story there that Cat mentioned.
Cat: Raj, it'd be great to pick up on that value piece. I think it's something that we've definitely found can be challenging around managing the prioritisation and making sure that people focus on unlocking that value earlier. I've mentioned it for delivery, but we often find there's a general impatience to get to that end goal, and that's where we started to break things down into those smaller pieces.
We've also really worked to embed data analytics into those deliveries, and that's allowed us to evidence the value sooner. And that's something that we would honestly say we weren't focusing on 12 months ago. But by focusing on that now and making sure that whatever we build, we're able to provide immediately from the off, evidence of what it's doing and how it's performing and what benefit it’s unlocking, it allows us then to kind of come back, revisit that with stakeholders, move on to that next phase, but with confidence that we are beginning to unlock that value and starting to chip away at the end goals that we were hoping to achieve, or counter to that, not doing so. And then we can revisit and look at it. And that's been an aspect where within business finance, we've really found the value in data in our general change delivery that we’re moving forward with.
Raj: Yeah, I love that. And I like how you're almost distinguishing between leading and lagging indicators of what do we need to measure to kind of prove that our experiments and our MVPs are going in the right direction? Have you got the hypothesis right, and what data is going to tell us that we're right or wrong. And there might be early signals and there might be really delayed signals in terms of everyone think about kind of cost reduction and revenue recognition etc., but that's also that's often quite downstream too, earlier indicators that we can measure around customer behaviour or adoption or nudging or, or otherwise. So we like to think about what are those kind of early signals that we might catch earlier in terms of things being released and things that we can measure versus do they then accumulate into those kind of strategic outcomes: revenue, cost saving, optimisation, optimisation, productivity, etc.
Shas: So let's say I've got a well-groomed backlog of well described, structured, user centric and problem focused use cases. How do you prioritise and make sure that there is alignment across the business in making them happen? So Cat I think you've already kind of described some of the prioritisation approaches that you're already taking. Raj, from your perspective, what are you seeing at the moment in terms of some of the clients you're speaking with?
Raj: Yes, I think I like to think about prioritisation in kind of two forms. One is: are we focusing on the right problem? And so if we're first choosing what problem is the most pressing to solve for the business and what's going to deliver most value. And we can think about value in many different forms then, then we are about kind of choosing what's the right solution.
The solution might change, and it could likely change over time as we try various different ways to solve the problem. But if we're focusing on solving the right problem first, we know that we've got teams that are iterating, experimenting, moving very quickly in terms of testing solutions, but we're anchoring on the right things that the organisation wants to solve, whether it's kind of moving big barriers out of the way, streamlining processes, improving customer experiences.
So from my perspective, it's kind of really getting very clear on what are the big barriers to progress today. And let's anchor on those. The solution will come and it could be many fold, but we trust the team to come up with the right solution if we're focusing on the right problem.
Shas: Now that makes sense. So if you were to restart your organisation's data journey today and knowing what you know now, what, if anything, would you do differently?
Cat: The million-dollar question, isn't it? It's always hard to know what you would do differently looking back. Hindsight is always 2020. I'm not sure I can pick one. I think there are probably three main aspects that I would have maybe started with earlier, I guess. And the first is one we've touched on already and that's embedding the performance analytics into change delivery but also data discovery into that use case area.
We've really found that to be game changing, particularly in the automation space. And working with that continuous improvement team in our change department, it's really meant that we focus on the right areas and that we can prove out the benefit of what we deliver, but also feed into the prioritisation beforehand. It's also made a massive difference in helping teams and the business to understand the pros and cons of whether that's a system or a process change, that needs either strategic or tactical. And that's been quite game changing for us. I think there's often, particularly with the pressures to deliver and unlock value that we've talked about, there can be a tendency to move towards more tactical decisions and strategic solutions are seen so long term. But by putting in the time and it's no longer with the tooling that we've got a long period of time.
And that's the other bit that's been game changing. But by putting in the time to look at the data ahead and allow that to inform the design decisions that you're making, we're able to actually get a balance of strategic and tactical solutions in place, or make informed decisions about which way to go, which I think, as I say, that's something I wouldn’t look to have done earlier, because I think we've seen the benefits of that.
The other piece is that I would have expanded the data team earlier in our journey because I think we all underestimated how fundamental having that insight and availability of it was going to be. It's always difficult to justify resource focusing on data, and I think many organisations that I've spoken to already struggle to build a large enough data team, really, to meet their business needs. But it's holding them back. And we're now finding ourselves in a great position now that we've added a few people into that area. And so I'd love to have done that earlier in hindsight.
And the other bit is that cultural shift and working on moving everybody along the journey with you. We talk about and it's not my phrase, but within the group IT & data team, they talk about a ‘steady drumbeat of data’ and actually keeping people moving towards that.
But that cultural shift is hard, to bring data into the central aspects of your organisation and allow it to support your other strategies, is a difficult shift for people. But it's so critical if we want to take on some of these newer technologies and really get the benefits out of them. So I think dedicating more time to that earlier in the journey is definitely something in hindsight I would have done.
Shas: Thank you. Some really interesting reflections there. But perhaps, Raj, you might have some good insight. So what are the hard truths about becoming a data driven organisation that people don't talk enough about?
Raj: So hard boiling it down to just one? One that comes to mind and is really, really important, is towards more the back end of the process people feel. But it's definitely around change management and how we bring people on the journey. And so I think what can often get overlooked is how are we supporting the adoption and the uptake of new tools or data products or solutions that we're creating rolling out to the business, because that's where the value is really mediated.
And I'm a firm believer of more data, more tooling, more dashboards, and more insights doesn't necessarily drive the value itself. It's the people that are doing something differently because of the information that we're then able to give them. And so people for me, people are really at the heart of making this data transformation really take hold in organisations.
And so how do we bring them at the front of the process? So really understanding them, the world, what they're trying to get done, what's getting in the way. What opportunities are we missing? How do we really empower them to do their job as effectively as possible? And jobs to be done could either be colleague side or it could be customer side equally, so, making sure we're taking that kind of human centred design approach, that change approach, that we're putting the person at the centre of the data or the AI solution, and we're really working with them at the beginning to understand the problem and the solution, but also working with them at the end and throughout, to making sure that we're really getting adoption right. So we're getting not just initial adoption and traction, because what we can often see is there are spikes and interest of oh, something new, but has it sustained. And if we don't have that kind of repeatable, sustainable, sticky behaviour, the value isn't sustained over time.
And so how do we make sure that we're really getting that behaviour change that accompanies the new product, service, solution that we're really rolling out to the business? So if my one: would be that change management and human centred design mindset for sure.
Shas: Yeah. And just bringing those people on a journey is absolutely key, and identifying those change audiences from the outset, certainly will hold dividends in the future, right? It's certainly key. I totally agree with you. We talk a lot about experimentation in data and AI. Do either of you think organisations are better at trying these things, learning quickly and when something doesn't deliver the expected value and moving on? Or is it still seen as failure and difficult culturally to address this? Raj, What are your thoughts on that?
Raj: I love this question because it feels like we're in a resurgence of experimentation. And so if I think back to eight, ten years ago, when we think about lean startup and approaches to product development, software development, it was all about early experimentation of hypothesis driven design and product testing, etc. prototyping. And now with the focus shifting to data and AI, the experimentation kind of notion has come around again, and everyone's got to really obsess about it.
Which is rightly so, because it's 100% the right approach of it's essentially about learning and kind of how do we tackle our unknowns or how do we really gather the data and the evidence and the proof points to make sure that what we're building is the right thing? Is it technically feasible from a data perspective, from a technology perspective?
And is it hitting the desirability aspects from a user perspective? So I've definitely seen it come around again, I think it's just changing shape or it's going to be being applied to a new domain as data and AI comes to the fore in terms of the business psyche. Where we are at right now, is really interesting, GenAI tools have exploded over the last couple of years, and it's really given the power to create to everybody. It's really democratised it and reduced the barriers to entry in terms of creating prototypes or creating mock-ups. I'm always going to say lo fi, but mid fi and high fi versions of what the end result could be. So it's a really great leveller in terms of communicating the product or the concept or the notion, but it doesn't remove the hard engineering or the architecture or the things that need to accompany it to make sure that it really works, live in the world at scale, when you're supporting many, many users or customers.
And I think that could be somewhat of a distraction. I think a lot of people are distracted by the ability to create really quickly. And I think what's missing is the discipline of around what makes an effective experiment. Do we have a very clear problem statement? Do we have a very clear hypothesis that's backed by evidence? Do we know to Cat’s point earlier around, what are we going to measure to make sure that we know that this thing is a success or not? And then we go through in terms of the iterative design of the solution, design and testing with users to make sure we're on the right tracks.
So for me, it's really around the discipline of what makes experimentation work that companies need to invest in, in terms of making sure they're getting the most return on effort that they're putting into prototypes and mock ups and POCs, POVs, etc. so we can actually get to products that scale effectively.
Shas: Cat, I'd be interested to understand from your perspective what approaches Novuna is taking at the moment, and have you adopted that experimentation approach and culture as of now?
Cat: I think there's definitely a change. I wouldn’t disagree with anything that Raj just said. I think 2 or 3 years ago, maybe, maybe longer, there was a period where we perhaps persisted with some initiatives for longer, rather than trying to take that more of a fail fast approach almost through fear of them not working out. And I think I saw that elsewhere in the industry as well. And I think that has definitely seen a change. And I think you're right, Raj, there's an environment of innovation feels like it's in place, and there's a bit more buzz around that.
And that's definitely the case within business finance. I think we've found ourselves with a setup across all of our tooling actually, never mind just in the data space where we can innovate, we can try things out and then we can revisit them and we can we can go back to the drawing box if needed.
And I think AI has changed that. There's a lot more ability to mock things up, as you say. And we're not even talking about something really, really basic. We're talking about something that's a lot quicker than to fully form and take forward if it gets that green light, and the availability of those tools has just changed things dramatically.
I do still see a challenge shifting things into production, and I think that that's where often I find that we still feel that things take too long. And so we may have that great innovation and some great ideas, but actually then really converting that into a truly production secure solution maybe is still a little bit sticky, but I've no doubt that that will continue to improve, I think it's got better and we'll get there.
And we touched on pilots and proofs of concept. It's perhaps goes back to that value question again. But I again, I've seen a shift within business finance and anything that we are piloting or doing concept on, there's a lot more conversation upfront around what is the success criteria and how will we measure it.
Prior, I think that we've all fallen into a trap of let's pilot something and then you realise your pilot's been running for a year and a half and maybe not quite sure if it works for you yet. Whereas by actually being able to try things out, have a really clear idea of what does success look like for that pilot doesn't have to be for production, but being able to then turn around the analytics and prove it out, it has definitely improved. I don't think it's perfect yet, and I do think it's something that people need to keep in mind for every pilot concept they're floating. Any innovative idea is, how are you going to prove this works? How does this meet and deliver value? So yeah, a really interesting one.
Raj: What about the culture of failure Cat and experimentation? Because recognising as part of experimentation, not everything that's going to succeed. And I guess what we're seeing across some organisations is they’re still getting to grips with, that celebrating failure concept and the non-certainty of experiments. So if we're going to try this thing it may work, it may not work. And there are myriad of reasons for why it could or could not work. But the point is we're making progress and we're learning by doing actually learn more faster by doing than we do behind whiteboards and all that good stuff.
What does that look like at Novuna, and has that still got somewhere to go? Or what are your thoughts there?
Cat: I think there's a there's a lot of open feedback and honesty around where perhaps projects aren't progressing as we’d like or where we're going to pivot and try something new. I think there's definitely an openness to that. I think it is still something, as humans, that we're not comfortable with and maybe taking a bit more of an industry slant, it's something that I've tried through some of my other roles within in the asset finance industry to encourage more conversation on what is that project that didn't go quite right. What lessons did you learn? And embedding and trying to encourage that conversation. Perhaps bringing it back to Novuna for a second. One of the things that we've definitely shifted towards is truly taking on that lessons learned approach and not wait until the end of a project to do that. And actually bringing that into much more of a regular review, particularly during longer initiatives, and it's something our continuous improvement initiatives have also really, really depended on. Fast feedback, understanding, getting engaged with the business. Understanding does this resonate with them? Are they comfortable with what's being worked on, even if it's not something that's delivered to them? And don't get me wrong. More to be done, but it's a work. Yeah, it's progress, more to be done. But we're making some progress.
Shas: Which is a great segue now into, I guess, the question about return on the investment. So we've done the MVP or we've shaped a prototype. Where do you actually see the most tangible value being delivered by AI today? And more importantly, do we need to get better at measuring the value and defining the KPIs? Raj, what are your thoughts on that?
Raj: I’ll answer this from two perspectives in terms of where the ROI on AI is coming in, that we're seeing. One’s from a solution perspective and one’s from a process perspective, but a process that we take in terms of the teams using AI to accelerate our process of searching for new value creation opportunities.
So I guess at CGI we're very obsessed around how AI can transform the software delivery lifecycle. And that's not just engineers and coding or testing, so further down the lifecycle. It's also up front in terms of how we are understanding users and opportunities and markets and defining the right opportunities, shaping these experiments and hypotheses, etc. everything's getting a lot faster.
And there are various stats across organisations, even within CGI of improving things, whether it's ten, 20, 30, 40% across the software delivery lifecycle, it's the way we're kind of building and testing product is fundamentally changing. We're really at that kind of tipping point of a new era of tooling taking shape, and it's changing the processes that we're following.
So I think that's one very specific area in terms of where we're seeing kind of real, tangible impacts on what we're doing today versus what we're doing, last year versus a few years ago, even last month. These things move pretty quickly.
I think a second point is around new product creation. And so one example comes to mind is we've been working with one of our clients in the insurance sector. And so they sit at the heart of the sector and in the ecosystem. And they mediate the exchange of data between multiple parties. And what we've been working with them on is how do we really turn the data that they're custodians for, into new products and services, into the ecosystem, that's really empowering their ecosystem partners to do their best job possible. And so they so they can transform their role from data custodian through to service delivery. And what’s part of that is making sure that we understand who our ecosystem partners are, what they're trying to do, what challenges they face, what opportunities exist, and what more could we do with either more data, better data or better insights? And how can we service that? Not through just exchange of data, but actually as a service. So how are we actually giving them insights and capabilities and service outcomes through providing a service to these people. So what I really like is really opening up new opportunities for extending relationships and value propositions that you have with your existing customers or partners in ecosystems.
I think there's a lot of focus on improving efficiency, productivity, but I love to think about the kind of value creation, the new proposition value proposition extension as part of this, and that's just one example of where we're seeing this live.
Shas: Thanks Cat your thoughts?
Cat: I think I'll pick up on that final point first. Actually, I think that's where we're seeing the biggest potential for AI and for data and true insights, is in that brand area. And being able to make sure that we are truly unlocking what we can deliver out to the businesses and individuals that we serve. Not only in the speed of delivery, which Raj also touched on, and the ability to create new solutions quicker, but also to make sure that we're not only feeding back to our customers but also able to truly understand their experiences and how they're using our tools.
And that might be internal customers, but it definitely speaks to the external customers. And some of the embedded analytics that you can you can get allows us to really understand the customer need. As I mentioned before, I think within business finance, we've made some progress in being able to understand and measure that value that we are delivering. I would love to see us in a position in 12 months time, of having true monitoring across everything. Of that performance of all of our systems and all of our key metrics as a business, at a touch of a button. Are we there yet? No, but I think we're now in a position to really unlock that. And I think that will give us more real time feedback of how our systems are performing, how our business is performing, how our users are interacting with our customers and really see the business in a different way. I think we're already very, reactive, and we've touched on that already, and we're able to pivot to the needs of the industry and to our customers. But I think the potential for AI and the insights that we're getting out of all of our tooling today, really should unlock that in another 12 months. And I think we're on that real cusp of seeing the true benefits there. I think we do need to make sure we continue with the work we've been doing to measure that value and build that in from the off into everything that we're doing. But I think we're right on the cusp of it.
Shas: And looking ahead, Cat, what do you think will be the biggest challenge organisations face in scaling data and AI?
Cat: For me, I think the biggest challenge organisations are going to face is balancing their ambition with patience and governance. I think that there is an awful lot of ambition out there, but there is still an awful lot of fear and ensuring that people take the time to deliver robust solutions is going to take patience, and that is always challenging when you're an ambitious organisation. And I'd say that for ourselves, as well as many others I see.
Shas: I agree Cat. And at times where we think we're racing against the clock, right? Because the world is moving at such a pace, it's almost like organisations need to feel that they're going to be left behind, or they need to move a lot quicker. So you're proposing is kind of do it in a steady, pace, would you say?
Cat: Yeah, it's a steady pace. But it's taking that fail fast approach that we discussed as well. It's allowing yourselves to innovate whilst continuing to invest in some of those more strategic, longer-term plans that you have. It's also remaining ambitious in the face of potentially layers and layers of governance and regulation. There is so much value to unlock in the tooling that we now have, and to ensure our best serving all of our customers. So we do need to maintain that ambition. And that might be tricky, because I do think there will be organisations out there that it just feels like too much and they start to step back from it and that will be a shame.
I'm really confident that Novuna and Novuna Business Finance has that ambition and that patience and the attitudes to move it forward. But I can really easily see how it would be difficult to keep the balance right and not swing one way or the other, and both have their risks.
Shas: Absolutely. Raj, your thoughts?
Raj: Such a well-rounded answer, it's very hard to follow. That was brilliant. I think to complement that, then I think the one thing I see is culture, because Cat mentioned the tools are there and they're constantly evolving very, very quickly, and they give us new powers in our hands kind of daily, monthly, like more and more. And so what's going to hold us back is really the organisational culture and the affordances that we're giving people to use these tools to the best effect.
And this is a mix of multiple things, right? So it's what beliefs do we have of an organisation that's allowing us to experiment and innovate at pace with the new tooling? What habits are we forming around experimentation, around innovation, around failing quickly and actually seeing failure as learning, rather than failing? And for me, it's kind of mentioned it earlier, experimentation is just learning but learning quickly.
And I’m a firm believer that organisations are competing on the pace of learning, and we don't get everything right, but those who learn to get it wrong quicker and pivot and change will get to the right destination faster. But it's also about trust as well. How much trust are we putting in the hands of people? But also, do we have the right guardrails to support them, to make sure that they're using the tools effectively?
It’s discipline as well. We're not just kind of chasing after shiny things and spinning things up because we can, and we now have the power. It's making sure we're rooting back into real gnarly problems to solve. And we're clear on the problem to solve. Who's experiencing the problem? What's the benefit? Is that the right problem to solve?
And it's that discipline around experimentation. And then finally, psychological safety, if you wrap it all in again, is kind of giving people permission to try things. So you add all that up, which is probably quite a lot, which is for me, it’s the heart, that's the culture of innovation, of experimentation. And I think that's going to be the unlock for a lot of companies.
Cat: It feels like quite a long list, doesn't it, of things that are a challenge when we phrase it like that. I think I'd build on one of those points as well. You mentioned habits. And I think building that habit of utilising these tools and trusting in them, it takes time. And that's where I feel like we're at that tipping point because people are in such a rush. But we've all done it. You don't take up a new habit by just rushing at it, doing it every day for four weeks, and then it falls away. And I think that will be a really tricky point.
The other thing I would flag, and I do think we're now coming up with probably too long a list of challenges, is actually to ensure organisations are still giving time and attention to the people and process side and the impact of what they're delivering on the day to day. There are so many wins that we've already seen around just looking at somebody's process, and there's no system needed. It's just how people are doing something and how they're using the tools they've already got, and there's so much value to be unlocked there, that I feel like the other challenge is just to get swept up in the buzz of scaling data and AI, and forget about where they can also make some wins in the way they've always approached things.
Shas: Now, absolutely great. So just to leave our listeners with that one extra piece of advice, resource or information, what would be that? What would that be that helps you along your journey so far? So Raj, should I come to you first?
Raj: I'm going to get quite far back with this one. And it's and it's something I think about quite a lot and it's had a real impact in terms of how I think about business and business strategy. So Playing to Win by Roger Martin is like the foundational piece of strategy literature that I think it informs how I think unbelievably, because it so clearly lays out and once you read it, you'll see the language coming up everywhere in terms of how to where to play, how to win and all that good stuff.
And for me, in terms of distinguishing between organisational factors versus market factors, where you're choosing where you compete, on what problems you're solving, areas of the market that your customers, your areas of market that you're playing in, the customers that you're serving. That choice framework is kind of really influenced how I think about a lot of things, and it's like a macro framework that then can be applied to data and AI in terms of where are are we choosing to apply data, and AI to have that outsized impact for the business, for customers. So that's the one for me. An old one.
Shas: Great, sounds like a great one, I've noted it myself. Thanks, Raj. Cat?
Cat: I'll take a slightly different view on that or answer on that. My key takeaway for people would be to engage not only with others in your industry, but to look outside of your industry, outside of your bubble. It's something that a mentor of mine once told me is to look at other parallels and see how they are doing things, how they're approaching it, and learn from others.
I think we can very easily get caught up in that our own company and really invested there, but actually, we've got so much to learn from engaging with others, and I think that's going to be critical, particularly as the pace of things moves. We need to keep talking to each other and learn from each other.
Shas: And that's a great note to end on. Cat Raj, thank you both for joining me today and sharing your perspectives. It was a great conversation and I'm sure we only scraped the surface.
Raj: Thanks. Okay.
Shas: If you enjoyed this podcast, keep an eye out for more conversations like this where we explore how organisations are approaching data, AI, and innovation. Thank you for listening.
