Agile Innovation Leaders

About Agile Innovation Leaders

The Agile Innovation Leaders podcast with Ula Ojiaku is an insightful series of conversations with world-class leaders, experts and doers about themselves and topics spanning leadership, digital transformation, lean-agile principles and practices, innovation, entrepreneurship, and much more. Listeners will gain insights and actionable tips for building thriving organisations, teams and careers in an ever-changing business world.

Publisher

Ula Ojiaku

Category

business · technology

Language

en

Explicit

No

First episode

1 Feb 2021

Latest episode

17 Apr 2025

Latest episodes

70 episodes in the feed.

1. 

   17 Apr 2025

   From the Archives: Dave Snowden on Cynefin and Building Capability for Managing Complexity

   Guest Bio:  Dave Snowden divides his time between two roles: founder & Chief Scientific Officer of Cognitive Edge and the founder and Director of the Centre for Applied Complexity at the University of Wales.  Known for creating the sense-making framework, Cynefin, Dave’s work is international in nature and covers government and industry looking at complex issues relating to strategy, organisational decision making and decision making.  He has pioneered a science-based approach to organisations drawing on anthropology, neuroscience and complex adaptive systems theory.  He is a popular and passionate keynote speaker on a range of subjects, and is well known for his pragmatic cynicism and iconoclastic style. He holds positions as extra-ordinary Professor at the Universities of Pretoria and Stellenbosch and visiting Professor at Bangor University in Wales respectively.  He has held similar positions at Hong Kong Polytechnic University, Canberra University, the University of Warwick and The University of Surrey.  He held the position of senior fellow at the Institute of Defence and Strategic Studies at Nanyang University and the Civil Service College in Singapore during a sabbatical period in Nanyang. His paper with Boone on Leadership was the cover article for the Harvard Business Review in November 2007 and also won the Academy of Management aware for the best practitioner paper in the same year.  He has previously won a special award from the Academy for originality in his work on knowledge management. He is a editorial board member of several academic and practitioner journals in the field of knowledge management and is an Editor in Chief of E:CO.  In 2006 he was Director of the EPSRC (UK) research programme on emergence and in 2007 was appointed to an NSF (US) review panel on complexity science research. He previously worked for IBM where he was a Director of the Institution for Knowledge Management and founded the Cynefin Centre for Organisational Complexity; during that period he was selected by IBM as one of six on-demand thinkers for a world-wide advertising campaign. Prior to that he worked in a range of strategic and management roles in the service sector. His company Cognitive Edge exists to integrate academic thinking with practice in organisations throughout the world and operates on a network model working with Academics, Government, Commercial Organisations, NGOs and Independent Consultants.  He is also the main designer of the SenseMaker® software suite, originally developed in the field of counter terrorism and now being actively deployed in both Government and Industry to handle issues of impact measurement, customer/employee insight, narrative based knowledge management, strategic foresight and risk management. The Centre for Applied Complexity was established to look at whole of citizen engagement in government and is running active programmes in Wales and elsewhere in areas such as social inclusion, self-organising communities and nudge economics together with a broad range of programmes in health.  The Centre will establish Wales as a centre of excellence for the integration of academic and practitioner work in creating a science-based approach to understanding society. Social Media and Website LinkedIn: https://uk.linkedin.com/in/dave-snowden-2a93b Twitter: @snowded Website: Cognitive Edge https://www.cognitive-edge.com/ Books/ Resources: Book: Cynefin - Weaving Sense-Making into the Fabric of Our World by Dave Snowden and Friends https://www.amazon.co.uk/Cynefin-Weaving-Sense-Making-Fabric-World/dp/1735379905 Book: Hope Without Optimism by Terry Eagleton https://www.amazon.co.uk/Hope-Without-Optimism-Terry-Eagleton/dp/0300248679/ Book: Theology of Hope by Jurgen Moltmann https://www.amazon.co.uk/Theology-Hope-Classics-Jurgen-Moltmann/dp/0334028787 Poem: ‘Mending Wall’ by Robert Frost https://www.poetryfoundation.org/poems/44266/mending-wall Video: Dave Snowden on ‘Rewilding Agile’ https://www.youtube.com/watch?v=QrgaPDqet4c Article reference to ‘Rewilding Agile’ by Dave Snowden https://cynefin.io/index.php/User:Snowded (https://cynefin.io/index.php/User:Snowded) Field Guide to Managing Complexity (and Chaos) In Times of Crisis https://cynefin.io/index.php/Field_guide_to_managing_complexity_(and_chaos)_in_times_of_crisis Field Guide to Managing Complexity (and Chaos) In Times of Crisis (2) https://ec.europa.eu/jrc/en/publication/managing-complexity-and-chaos-times-crisis-field-guide-decision-makers-inspired-cynefin-framework Cynefin Wiki https://cynefin.io/wiki/Main_Page (https://cynefin.io/wiki/Main_Page) Interview Transcript Ula Ojiaku: Dave, thank you for making the time for this conversation. I read in your, your latest book - the book, Cynefin: Weaving Sense Making into the Fabric of Our World, which was released, I believe, in celebration of the twenty first year of the framework. And you mentioned that in your childhood, you had multidisciplinary upbringing which involved lots of reading. Could you tell us a bit more about that? Dave Snowden: I think it wasn't uncommon in those days. I mean, if you did… I mean, I did science A levels and mathematical A levels. But the assumption was you would read every novel that the academic English class were reading. In fact, it was just unimaginable (that) you wouldn't know the basics of history. So, if you couldn't survive that in the sixth form common room, and the basics of science were known by most of the arts people as well. So that that was common, right. And we had to debate every week anyway. So, every week, you went up to the front of the class and you were given a card, and you'd have the subject and which side you are on, and you had to speak for seven minutes without preparation. And we did that every week from the age of 11 to 18. And that was a wonderful discipline because it meant you read everything. But also, my mother was… both my parents were the first from working class communities to go to university. And they got there by scholarship or sheer hard work against the opposition of their families. My mother went to university in Germany just after the war, which was extremely brave of her -  you know, as a South Wales working class girl. So, you weren't allowed not to be educated, it was considered the unforgivable sin. Ula Ojiaku: Wow. Did it mean that she had to learn German, because (she was) studying in Germany…? Dave Snowden: She well, she got A levels in languages. So, she went to university to study German and she actually ended up as a German teacher, German and French. So, she had that sort of background. Yeah. Ula Ojiaku: And was that what influenced you? Because you also mentioned in the book that you won a £60 prize? Dave Snowden: Oh, no, that was just fun. So, my mum was very politically active. We're a South Wales labor. Well, I know if I can read but we were labor. And so, she was a local Councilor. She was always politically active. There's a picture of me on Bertrand Russell's knee and her as a baby on a CND march. So it was that sort of background. And she was campaigning for comprehensive education, and had a ferocious fight with Aiden Williams, I think, who was the Director of Education, it was really nasty. I mean, I got threatened on my 11 Plus, he got really nasty. And then so when (I was) in the sixth form, I won the prize in his memory, which caused endless amusement in the whole county. All right. I think I probably won it for that. But that was for contributions beyond academic. So, I was leading lots of stuff in the community and stuff like that. But I had £60. And the assumption was, you go and buy one massive book. And I didn't, I got Dad to drive me to Liverpool - went into the big bookshop there and just came out with I mean, books for two and six pence. So, you can imagine how many books I could get for £60. And I just took everything I could find on philosophy and history and introductory science and stuff like that and just consumed it. Ula Ojiaku: Wow, it seemed like you already knew what you wanted even before winning the prize money, you seem to have had a wish list... Dave Snowden: I mean, actually interesting, and the big things in the EU field guide on (managing) complexity which was just issued. You need to build…, You need to stop saying, ‘this is the problem, we will find the solution’ to saying, ‘how do I build capability, that can solve problems we haven't yet anticipated?’ And I think that's part of the problem in education. Because my children didn't have that benefit. They had a modular education. Yeah, we did a set of exams at 16 and a set of exams that 18 and between those periods, we could explore it (i.e. options) and we had to hold everything in our minds for those two periods, right? For my children, it was do a module, pass a test, get a mark, move on, forget it move on. So, it's very compartmentalized, yeah? And it's also quite instrumentalist. We, I think we were given an education as much in how to learn and have had to find things out. And the debating tradition was that; you didn't know what you're going to get hit with. So, you read everything, and you thought about it, and you learn to think on your feet. And I think that that sort of a broad switch, it started to happen in the 80s, along with a lot of other bad things in management. And this is when systems thinking started to dominate. And we moved to an engineering metaphor. And you can see it in cybernetics and everything else, it's an attempt to define everything as a machine. And of course, machines are designed for a purpose, whereas ecosystems evolve for resilience. And I think that's kind of like where I, my generation were and it's certainly what we're trying to bring back in now in sort of in terms of practice. Ula Ojiaku: I have an engineering background and a computer science background. These days, I'm developing a newfound love for philosophy, psychology, law and, you know, intersect, how do all these concepts intersect? Because as human beings we’re complex, we're not machines where you put the program in and you expect it to come out the same, you know, it's not going to be the same for every human being. What do you think about that? Dave Snowden: Yeah. And I think, you know, we know more on this as well. So, we know the role of art in human evolution is being closely linked to innovation. So, art comes before language. So, abstraction allows you to make novel connections. So, if you focus entirely on STEM education, you're damaging the human capacity to innovate. And we're, you know, as creatures, we're curious. You know. And I mean, we got this whole concept of our aporia, which is key to connecting that, which is creating a state of deliberate confusion, or a state of paradox. And the essence of a paradox is you can't resolve it. So, you're forced to think differently. So, the famous case on this is the liar’s paradox, alright? I mean, “I always lie”. That just means I lied. So, if that means I was telling the truth. So, you've got to think differently about the problem. I mean, you've seen those paradoxes do the same thing. So that, that deliberate act of creating confusion so people can see novelty is key. Yeah. Umm and if you don't find… finding ways to do that, so when we looked at it, we looked at linguistic aporia, aesthetic aporia and physical aporia. So, I got some of the… one of the defining moments of insight on Cynefin was looking at Caravaggio`s paintings in Naples. When I realized I've been looking for the idea of the liminality. And that was, and then it all came together, right? So those are the trigger points requiring a more composite way of learning. I think it’s also multiculturalism, to be honest. I mean, I, when I left university, I worked on the World Council of Churches come, you know program to combat racism. Ula Ojiaku: Yes, I'd like to know more about that. That's one of my questions… Dave Snowden: My mother was a good atheist, but she made me read the Bible on the basis, I wouldn't understand European literature otherwise, and the penetration guys, I became a Catholic so… Now, I mean, that that was fascinating, because I mean, I worked on Aboriginal land rights in Northern Australia, for example. And that was when I saw an activist who was literally murdered in front of me by a security guard. And we went to the police. And they said, it's only an Abo. And I still remember having fights in Geneva, because South Africa was a tribal conflict with a racial overlay. I mean, Africa, and its Matabele Zulu, arrived in South Africa together and wiped out the native population. And if you don't understand that, you don't understand the Matabele betrayal. You don't understand what happened. It doesn't justify apartheid. And one of the reasons there was a partial reconciliation, is it actually was a tribal conflict. And the ritual actually managed that. Whereas in Australia, in comparison was actually genocide. Yeah, it wasn't prejudice, it was genocide. I mean, until 1970s, there, were still taking half -breed children forcibly away from their parents, inter-marrying them in homes, to breed them back to white. And those are, I think, yeah, a big market. I argued this in the UK, I said, one of the things we should actually have is bring back national service. I couldn't get the Labor Party to adopt it. I said, ‘A: Because it would undermine the Conservatives, because they're the ones who talk about that sort of stuff. But we should allow it to be overseas.’ So, if you put two years into working in communities, which are poorer than yours, round about that 18 to 21-year-old bracket, then we'll pay for your education. If you don't, you'll pay fees. Because you proved you want to give to society. And that would have been… I think, it would have meant we'd have had a generation of graduates who understood the world because that was part of the objective. I mean, I did that I worked on worked in South Africa, on the banks of Zimbabwe on the audits of the refugee camps around that fight. And in Sao Paulo, in the slums, some of the work of priests. You can't come back from that and not be changed. And I think it's that key formative period, we need to give people. Ula Ojiaku: True and like you said, at that age, you know, when you're young and impressionable, it helps with what broadening your worldview to know that the world is bigger than your father's … compound (backyard)… Dave Snowden: That’s the worst problem in Agile, because what, you've got a whole class of, mainly white males and misogynism in Agile is really bad. It's one of the worst areas for misogyny still left, right, in terms of where it works. Ula Ojiaku: I'm happy you are the one saying it not me… Dave Snowden: Well, no, I mean, it is it’s quite appalling. And so, what you've actually got is, is largely a bunch of white male game players who spent their entire time on computers. Yeah, when you take and run seriously after puberty, and that's kind of like a dominant culture. And that's actually quite dangerous, because it lacks, it lacks cultural diversity, it lacks ethnic diversity, it lacks educational diversity. And I wrote an article for ITIL, recently, which has been published, which said, no engineers should be allowed out, without training in ethics. Because the implications of what software engineers do now are huge. And the problem we’ve got, and this is a really significant, it's a big data problem as well. And you see it with a behavioral economic economist and the nudge theory guys - all of whom grab these large-scale data manipulations is that they're amoral, they're not immoral, they're amoral. And that's actually always more scary. It's this sort of deep level instrumentalism about the numbers; the numbers tell me what I need to say. Ula Ojiaku: And also, I mean, just building on what you've said, there are instances, for example, in artificial intelligence is really based on a sample set from a select group, and it doesn't necessarily recognize things that are called ‘outliers’. You know, other races… Dave Snowden: I mean, I've worked in that in all my life now back 20, 25 years ago. John Poindexter and I were on a stage in a conference in Washington. This was sort of early days of our work on counter terrorism. And somebody asked about black box AI and I said, nobody's talking about the training data sets. And I've worked in AI from the early days, all right, and the training data sets matter and nobody bothered. They just assumed… and you get people publishing books which say correlation is causation, which is deeply worrying, right? And I think Google is starting to acknowledge that, but it's actually very late. And the biases which… we were looking at a software tool the other day, it said it can, it can predict 85% of future events around culture. Well, it can only do that by constraining how executive see culture, so it becomes a self-fulfilling prophecy. And then the recruitment algorithms will only recruit people who match that cultural expectation and outliers will be eliminated. There's an HBO film coming up shortly on Myers Briggs. Now, Myers Briggs is known to be a pseudo-science. It has no basis whatsoever in any clinical work, and even Jung denied it, even though it's meant to be based on his work. But it's beautiful for HR departments because it allows them to put people into little categories. And critically it abrogates, judgment, and that's what happened with systems thinking in the 80s 90s is everything became spreadsheets and algorithms. So, HR departments would produce… instead of managers making decisions based on judgment, HR departments would force them into profile curves, to allocate resources. Actually, if you had a high performing team who were punished, because the assumption was teams would not have more than… Ula Ojiaku: Bell curve... Dave Snowden: …10 percent high performance in it. All right. Ula Ojiaku: Yeah. Dave Snowden: And this sort of nonsense has been running in the 80s, 90s and it coincided with… three things came together. One was the popularization of systems thinking. And unfortunately, it got popularized around things like process reengineering and learning organization. So that was a hard end. And Sanghi's pious can the sort of the, the soft end of it, right? But both of them were highly directional. It was kind of like leaders decide everything follows. Yeah. And that coincided with the huge growth of computing - the ability to handle large volumes of information. And all of those sorts of things came together in this sort of perfect storm, and we lost a lot of humanity in the process. Ula Ojiaku: Do you think there's hope for us to regain the humanity in the process? Because it seems like the tide is turning from, I mean, there is still an emphasis, in my view, on systems thinking, however, there is the growing realization that we have, you know, knowledge workers and people… Dave Snowden: Coming to the end of its park cycle, I see that all right. I can see it with the amount of cybernetics fanboys, and they are all boys who jump on me every time I say something about complexity, right? So, I think they're feeling threatened. And the field guide is significant, because it's a government, you know, government can like publication around effectively taken an ecosystems approach, not a cybernetic approach. And there's a book published by a good friend of mine called Terry Eagleton, who's… I don't think he's written a bad book. And he's written about 30, or 40. I mean, the guy just produces his stuff. It's called “Hope without Optimism”. And I think, hope is… I mean, Moltman just also published an update of his Theology of Hope, which is worth reading, even if you're not religious. But hope is one of those key concepts, right, you should… to lose hope is a sin. But hope is not the same thing as optimism. In fact, pessimistic people who hope actually are probably the ones who make a difference, because they're not naive, right? And this is my objection to the likes of Sharma Ga Sengi, and the like, is they just gather people together to talk about how things should be. And of course, everything should be what, you know, white MIT, educated males think the world should be like. I mean, it's very culturally imperialist in that sort of sense. And then nobody changes because anybody can come together in the workshop and agree how things should be. It's when you make a difference in the field that it counts, you've got to create a micro difference. This is hyper localization, you got to create lots and lots of micro differences, which will stimulate the systems, the system will change. I think, three things that come together, one is COVID. The other is global warming. And the other is, and I prefer to call it the epistemic justice movement, though, that kind of like fits in with Black Lives Matter. But epistemic justice doesn't just affect people who are female or black. I mean, if you come to the UK and see the language about the Welsh and the Irish, or the jokes made about the Welsh in BBC, right? The way we use language can designate people in different ways and I think that's a big movement, though. And it's certainly something we develop software for. So, I think those three come together, and I think the old models aren't going to be sustainable. I mean, the cost is going to be terrible. I mean, the cost to COVID is already bad. And we're not getting this thing as long COVID, it’s permanent COVID. And people need to start getting used to that. And I think that's, that's going to change things. So, for example, in the village I live in Wiltshire. Somebody’s now opened an artisan bakery in their garage and it's brilliant. And everybody's popping around there twice a week and just buying the bread and having a chat on the way; socially-distanced with masks, of course. And talking of people, that sort of thing is happening a lot. COVID has forced people into local areas and forced people to realise the vulnerability of supply chains. So, you can see changes happening there. The whole Trump phenomenon, right, and the Boris murmuring in the UK is ongoing. It's just as bad as the Trump phenomenon. It's the institutionalization of corruption as a high level. Right? Those sorts of things trigger change, right? Not without cost, change never comes without cost, but it just needs enough… It needs local action, not international action. I think that's the key principle. To get a lot of people to accept things like the Paris Accord on climate change, and you’ve got to be prepared to make sacrifices. And it's too distant a time at the moment, it has to become a local issue for the international initiatives to actually work and we're seeing that now. I mean… Ula Ojiaku: It sounds like, sorry to interrupt - it sounds like what you're saying is, for the local action, for change to happen, it has to start with us as individuals… Dave Snowden: The disposition… No, not with individuals. That's actually very North American, the North European way of thinking right. The fundamental kind of basic identity structure of humans is actually clans, not individuals. Ula Ojiaku: Clans... Dave Snowden: Yeah. Extended families, clans; it's an ambiguous word. We actually evolved for those. And you need it at that level, because that's a high level of social interaction and social dependency. And it's like, for example, right? I'm dyslexic. Right? Yeah. If I don't see if, if the spelling checker doesn't pick up a spelling mistake, I won’t see it. And I read a whole page at a time. I do not read it sentence by sentence. All right. And I can't understand why people haven't seen the connections I make, because they're obvious, right? Equally, there's a high degree of partial autism in the Agile community, because that goes with mathematical ability and thing, and that this so-called education deficiencies, and the attempt to define an ideal individual is a mistake, because we evolved to have these differences. Ula Ojiaku: Yes. Dave Snowden: Yeah. And the differences understood that the right level of interaction can change things. So, I think the unit is clan, right for extended family, or extended, extended interdependence. Ula Ojiaku: Extended interdependence… Dave Snowden: We're seeing that in the village. I mean, yeah, this is classic British atomistic knit, and none of our relatives live anywhere near us. But the independence in the village is increasing with COVID. And therefore, people are finding relationships and things they can do together. Now, once that builds to a critical mass, and it does actually happen exponentially, then bigger initiatives are possible. And this is some of the stuff we were hoping to do in the US shortly on post-election reconciliation. And the work we've been doing in Malmo, in refugees and elsewhere in the world, right, is you change the nature of localized interaction with national visibility, so that you can measure the dispositional state of the system. And then you can nudge the system when it's ready to change, because then the energy cost of change is low. But that requires real time feedback loops in distributed human sensor networks, which is a key issue in the field guide. And the key thing that comes back to your original question on AI, is, the internet at the moment is an unbuffered feedback loop. Yeah, where you don't know the source of the data, and you can't control the source of the data. And any network like that, and this is just apriori science factor, right will always become perverted. Ula Ojiaku: And what do you mean by term apriori? Dave Snowden: Oh, before the facts, you don't need to, we don't need to wait for evidence. It's like in an agile, you can look at something like SAFe® which case claims to scale agile and just look at it you say it's apriori wrong (to) a scale a complex system. So, it's wrong. All right. End of argument right. Now let's talk about the details, right. So yeah, so that's, you know, that's coming back. The hyper localization thing is absolutely key on that, right? And the same is true to be honest in software development. A lot of our work now is to understand the unarticulated needs of users. And then shift technology in to actually meet those unarticulated needs. And that requires a complex approach to architecture, in which people and technology are objects with defined interactions around scaffolding structures, so that applications can emerge in resilience, right? And that's actually how local communities evolve as well. So, we've now got the theoretical constructs and a lot of the practical methods to actually… And I've got a series of blog posts - which I've got to get back to writing - called Rewilding Agile. And rewilding isn't returning to the original state, it’s restoring balance. So, if you increase the number of human actors as your primary sources, and I mean human actors, not as people sitting on (in front of) computer screens who can be faked or mimicked, yeah? … and entirely working on text, which is about 10%, of what we know, dangerous, it might become 80% of what we know and then you need to panic. Right? So, you know, by changing those interactions, increasing the human agency in the system, that's how you come to, that's how you deal with fake news. It's not by writing better algorithms, because then it becomes a war with the guys faking the news, and you're always gonna lose. Ula Ojiaku: So, what do you consider yourself, a person of faith? Dave Snowden: Yeah. Ula Ojiaku: Why? Dave Snowden: Oh, faith is like hope and charity. I mean, they're the great virtues… I didn’t tell you I got into a lot in trouble in the 70s. Dave Snowden: I wrote an essay that said Catholicism, Marxism and Hinduism were ontologically identical and should be combined and we’re different from Protestantism and capitalism, which are also ontologically identical (and) it can be combined. Ula Ojiaku: Is this available in the public domain? Dave Snowden: I doubt it. I think it actually got me onto a heresy trial at one point, but that but I would still say that. Ula Ojiaku: That's amazing. Can we then move to the framework that Cynefin framework, how did it evolve into what we know it as today? Dave Snowden: I’ll do a high-level summary, but I wrote it up at length in the book and I didn't know I was writing for the book. The book was a surprise that they put together for me. I thought that was just writing an extended blog post. It started when I was working in IBM is it originates from the work of Max Borrasso was my mentor for years who tragically died early. But he was looking at abstraction, codification and diffusion. We did a fair amount of work together, I took two of those aspects and started to look at informal and formal communities in IBM, and its innovation. And some of the early articles on Cynefin, certainly the early ones with the five domains come from that period. And at that time, we had access labels. Yeah. And then then complexity theory came into it. So, it shifted into being a complexity framework. And it stayed … The five domains were fairly constant for a fairly long period of time, they changed their names a bit. The central domain I knew was important, but didn't have as much prominence as it does now. And then I introduced liminality, partly driven by agile people, actually, because they could they couldn't get the concept there were dynamics and domains. So, they used to say things like, ‘look, Scrum is a dynamic. It's a way of shifting complex to complicated’ and people say ‘no, the scrum guide said it's about complex.’ And you think, ‘oh, God, Stacey has a lot to answer for’ but… Ula Ojiaku: Who`s Stacey? Dave Snowden: Ralph Stacey. So, he was the guy originally picked up by Ken when he wrote the Scrum Guide… Ula Ojiaku: Right. Okay. Dave Snowden: Stacey believes everything's complex, which is just wrong, right? So, either way, Cynefin evolved with the liminal aspects. And then the last resolution last year, which is… kind of completes Cynefin to be honest, there's some refinements… was when we realized that the central domain was confused, or operatic. And that was the point where you started. So, you didn't start by putting things into the domain, you started in the operatic. And then you moved aspects of things into the different domains. So that was really important. And it got picked up in Agile, ironically, by the XP community. So, I mean, I was in IT most of my life, I was one of the founders of the DSDM Consortium, and then moved sideways from that, and was working in counterterrorism and other areas, always you're working with technology, but not in the Agile movement. Cynefin is actually about the same age as Agile, it started at the same time. And the XP community in London invited me in, and I still think Agile would have been better if it had been built on XP, not Scrum. But it wouldn't have scaled with XP, I mean, without Scrum it would never have scaled it. And then it got picked up. And I think one of the reasons it got picked up over Stacey is, it said order is possible. It didn't say everything is complex. And virtually every Agile method I know of value actually focuses on making complex, complicated. Ula Ojiaku: Yes. Dave Snowden: And that’s its power. What they're… what is insufficient of, and this is where we've been working is what I call pre-Scrum techniques. Techniques, which define what should go into that process. Right, because all of the Agile methods still tend to be a very strong manufacturing metaphor - manufacturing ideas. So, they assume somebody will tell them what they have to produce. And that actually is a bad way of thinking about IT. Technology needs to co-evolve. And users can't articulate what they want, because they don't know what technology can do. Ula Ojiaku: True. But are you saying… because in Agile fundamentally, it’s really about making sure there's alignment as well that people are working on the right thing per time, but you're not telling them how to do it? Dave Snowden: Well, yes and no - all right. I mean, it depends what you're doing. I mean, some Agile processes, yes. But if you go through the sort of safe brain remain processes, very little variety within it, right? And self-organization happens within the context of a user executive and retrospectives. Right, so that's its power. And, but if you look at it, it took a really good technique called time-boxing, and it reduced it to a two-week sprint. Now, that's one aspect of time boxing. I mean, I've got a whole series of blog posts next week on this, because time boxing is a hugely valuable technique. It says there's minimal deliverable project, and maximum deliverable product and a minimal level of resource and a maximum level of resource. And the team commits to deliver on the date. Ula Ojiaku: To accurate quality… to a quality standard. Dave Snowden: Yeah, so basically, you know that the worst case, you'll get the minimum product at the maximum cost, but you know, you'll get it on that date. So, you can deal with it, alright. And that's another technique we've neglected. We're doing things which force high levels of mutation and requirements over 24 hours, before they get put into a Scrum process. Because if you just take what users want, you know, there's been insufficient co-evolution with the technology capability. And so, by the time you deliver it, the users will probably realize they should have asked for something different anyway. Ula Ojiaku: So, does this tie in with the pre-Scrum techniques you mentioned earlier? If so, can you articulate that? Dave Snowden: So, is to say different methods in different places. And that's again, my opposition to things like SAFe, to a lesser extent LeSS, and so on, right, is they try and put everything into one bloody big flow diagram. Yeah. And that's messy. All right? Well, it's a recipe, not a chef. What the chef does is they put different ingredients together in different combinations. So, there's modularity of knowledge, but it's not forced into a linear process. So, our work… and we just got an open space and open source and our methods deliberately, right, in terms of the way it works, is I can take Scrum, and I can reduce it to its lowest coherent components, like a sprint or retrospective. I can combine those components with components for another method. So, I can create Scrum as an assembly of components, I can take those components compared with other components. And that way, you get novelty. So, we're then developing components which sit before traditional stuff. Like for example, triple eight, right? This was an old DSDM method. So, you ran a JAD sessions and Scrum has forgotten about JAD. JAD is a really…  joint application design… is a really good set of techniques - they’re all outstanding. You throw users together with coders for two days, and you force out some prototypes. Yeah, that latching on its own would, would transform agile, bringing that back in spades, right? We did is we do an eight-hour JAD session say, in London, and we pass it on to a team in Mumbai. But we don't tell them what the users ask for. They just get the prototype. And they can do whatever they want with it for eight hours. And then they hand it over to a team in San Francisco, who can do whatever they want with it in eight hours. And it comes back. And every time I've run this, the user said, ‘God, I wouldn't have thought of that, can I please, have it?’ So, what you're doing is a limited life cycle -  you get the thing roughly defined, then you allow it to mutate without control, and then you look at the results and decide what you want to do. And that's an example of pre-scrum technique, that is a lot more economical than systems and analysts and user executives and storyboards. And all those sorts of things. Yeah. Ula Ojiaku: Well, I see what you mean, because it seems like the, you know, the JAD - the joint application design technique allows for emergent design, and you shift the decision making closer to the people who are at the forefront. And to an extent my understanding of, you know, Scrum … I mean, some agile frameworks - that's also what they promote… Dave Snowden: Oh, they don’t really don’t. alright. They picked up Design Thinking which is quite interesting at the moment. If you if you look at Agile and Design Thinking. They're both at the end of their life cycles. Ula Ojiaku: Why do you say that? Dave Snowden: Because they're being commodified. The way you know, something is coming to the end of its life cycle is when it becomes highly commodified. So, if you look at it, look at what they are doing the moment, the Double Diamond is now a series of courses with certificates. And I mean, Agile started with bloody certificates, which is why it's always been slightly diverse in the way it works. I mean, this idea that you go on a three-day course and get a certificate, you read some slides every year and pay some money and get another certificate is fundamentally corrupt. But most of the Agile business is built on it, right? I mean, I've got three sets of methods after my name. But they all came from yearlong or longer courses certified by university not from tearing apart a course. Yeah, or satisfying a peer group within a very narrow cultural or technical definition of competence. So, I think yeah, and you can see that with Design Thinking. So, it's expert ideation, expert ethnography. And it still falls into that way of doing things. Yeah. And you can see it, people that are obsessed with running workshops that they facilitate. And that's the problem. I mean, the work we're doing on citizen engagement is actually… has no bloody facilitators in it. As all the evidence is that the people who turn up are culturally biased about their representative based opinions. And the same is true if you want to look at unarticulated needs, you can't afford to have the systems analysts finding them because they see them from their perspective. And this is one of one science, right? You did not see what you do not expect to see. We know that, alright? So, you're not going to see outliers. And so, the minute you have an expert doing something, it's really good - where you know, the bounds of the expertise, cover all the possibilities, and it's really dangerous. Well, that's not the case. Ula Ojiaku: So, could you tell me a bit more about the unfacilitated sessions you mentioned earlier? Dave Snowden: They’re definitely not sessions, so we didn't like what were triggers at moments. Ula Ojiaku: Okay. Dave Snowden: So, defining roles. So, for example, one of the things I would do and have done in IT, is put together, young, naive, recently graduated programmer with older experienced tester or software architect. So, somebody without any… Ula Ojiaku: Prejudice or pre-conceived idea... Dave Snowden: … preferably with a sort of grandparent age group between them as well. I call it, the grandparents syndrome - grandparents say things to their grandchildren they won’t tell their children and vice versa. If you maximize the age gap, there's actually freer information flow because there's no threat in the process. And then we put together with users trained to talk to IT people. So, in a month's time, I'll publish that as a training course. So, training users to talk to IT people is more economical than trying to train IT people to understand users. Ula Ojiaku: To wrap up then, based on what you said, you know, about Cynefin, and you know, the wonderful ideas behind Cynefin. How can leaders in organizations in any organization apply these and in how they make sense of the world and, you know, take decisions? Dave Snowden: Well, if there's actually a sensible way forward now, so we've just published the field guide on managing complexity. Ula Ojiaku: Okay. Dave Snowden: And that is actually, it's a sort of ‘Chef's guide’. It has four stages: assess, adapt, exert, transcend, and within that it has things you could do. So, it's not a list of qualities, it's a list of practical things you should go and do tomorrow, and those things we're building at the moment with a lot of partners, because we won't try and control this; this needs to be open. Here's an assessment process that people will go through to decide where they are. So that's going to be available next week on our website. Ula Ojiaku: Oh, fantastic! Dave Snowden: For the initial registration.  Other than that, and there's a whole body of stuff on how to use Cynefin. And as I said, we just open source on the methods. So, the Wiki is open source. These… from my point of view, we're now at the stage where the market is going to expand very quickly. And to be honest, I, you know, I've always said traditionally use cash waiver as an example of this. The reason that Agile scaled around Scrum is he didn't make it an elite activity, which XP was. I love the XP guys, but they can't communicate with ordinary mortals. Yeah. It takes you about 10 minutes to tune into the main point, and even you know the field, right. And he (Jeff Sutherland) made the Scrum Guide open source. And that way it’s great, right. And I think that that's something which people just don't get strategic with. They, in early stages, you should keep things behind firewalls. When the market is ready to expand, you take the firewalls away fast. Because I mean, getting behind firewalls initially to maintain coherence so they don't get diluted too quickly, or what I call “hawks being made into pigeons”. Yeah. But the minute the market is starting to expand, that probably means you've defined it so you release the firewall so the ideas spread very quickly, and you accept the degree of diversity on it. So that's the reason we put the Wiki. Ula Ojiaku: Right. So, are there any books that you would recommend, for anyone who wants to learn more about what you've talked about so far. Dave Snowden: You would normally produce the theory book, then the field book, but we did it the other way around. So, Mary and I are working on three to five books, which will back up the Field Guide. Ula Ojiaku: Is it Mary Boone? Dave Snowden: Mary Boone. She knows how to write to the American managers, which I don't, right… without losing integrity. So that's coming, right. If you go onto the website, I've listed all the books I read. I don't think… there are some very, very good books around complexity, but they're deeply specialized, they’re academic. Gerard’s book is just absolutely brilliant but it's difficult to understand if you don’t have a philosophy degree. And there are some awfully tripe books around complexity - nearly all of the popular books I've seen, I wouldn't recommend. Yeah. Small Groups of Complex Adaptive Systems is probably quite a good one that was published about 20 years ago. Yeah, but that we got a book list on the website. So, I would look at that. Ula Ojiaku: Okay. Thank you so much for that. Do you have any ask of the audience and how can they get to you? Dave Snowden: We've open-sourced the Wiki, you know, to create a critical mass, I was really pleased we have 200 people volunteered to help populate it. So, we get the all the methods in the field guide them. And they're actively working at that at the moment, right, and on a call with them later. And to be honest, I've done 18-hour days, the last two weeks, but 8 hours of each of those days has been talking to the methods with a group of people Academy 5, that's actually given me a lot of energy, because it's huge. So, get involved, I think it’s the best way… you best understand complexity by getting the principles and then practicing it. And the key thing I'll leave us with is the metaphor. I mentioned it a few times - a recipe book user has a recipe, and they follow it. And if they don't have the right ingredients, and if they don't have the right equipment, they can't operate. Or they say it's not ‘true Agile’. A chef understands the theory of cooking and has got served in apprenticeship. So, their fingers know how to do things. And that's… we need… a downside.. more chefs, which is the combination of theory and practice. And the word empirical is hugely corrupted in the Agile movement. You know, basically saying, ‘this worked for me’ or ‘it worked for me the last three times’ is the most dangerous way of moving forward. Ula Ojiaku: Because things change and what worked yesterday might not work Dave Snowden: And you won't be aware of what worked or didn't work and so on. Ula Ojiaku: And there's some bias in that. Wouldn't you say? Dave Snowden: We’ve got an attentional blindness if you've got Ula Ojiaku: Great. And Dave, where can people find you? Are you on social media? Dave Snowden: Cognitive. Yeah, social media is @snowded. Yeah. LinkedIn, Facebook and Twitter. Two websites – the Cognitive Edge website, which is where I blog, and there's a new Cynefin Center website now, which is a not-for-profit arm. Ula Ojiaku: Okay. All these would be in the show notes. Thank you so much for your time, Dave. It's been a pleasure speaking with you. Dave Snowden: Okay. Thanks a lot.

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   10 Mar 2025

   Ula Ojiaku: From Ditch to Triumph: Harness Your Inner Strength

   Bio Uloaku (Ula) Ojiaku is the Founder/ CEO of Mezahab Group Ltd (a UK-based consultancy focused on helping leaders in large organisations improve how they work to deliver value to their customers). With over 20 years of professional experience, Ula has board-level experience and has worked in multiple countries, in a variety of technical, business and leadership roles across industries including Retail, Oil & Gas, Telecommunications, Financial Services, Government, Higher Education and Consulting. Ula hosts the Agile Innovation Leaders podcast, which features conversations with thought-leaders and world-class practitioners on topics covering leadership, innovation, business, agility, and much more. She also currently serves a multinational retail organisation as an Enterprise Agility Strategist/ Coach. Connect Uloaku (Ula) Ojiaku | LinkedIn Happy International Women's Day to all the wonderful, brilliant, strong and multi -talented women out here! There's this story about a donkey that fell into a ditch. The owners decided that it was more convenient to bury it than to try and rescue it. So out with their shovels they came. Each would dig up a shovel full of dirt and pour this into the ditch where the donkey lay. Unbeknownst to them, the donkey at the bottom of the ditch was shaking off the dirt being piled on it and stepping on this. On went this activity until the donkey got to the surface and simply walked away, free from what was once a ditch that trapped it. What's my point? Sometimes you may feel like you're in a ditch and the people you expected to help get you out are instead pouring on dirt to bury you. You're not alone (I feel that way sometimes too). You always have a choice, even in the gravest situations. It's not about what happens to you, it's what afterwards you choose to do. So, just shake off the dirt, the limitations, the ...(fill in the blank gaps), step on it and rise higher. You've got this!

3. 

   9 Feb 2025

   E048 Bala Madhusoodhanan on Critical Considerations for Leaders when Adopting AI Solutions

   Bio Bala has rich experience in retail technology and process transformation. Most recently, he worked as a Principal Architect for Intelligent Automation, Innovation & Supply Chain in a global Fortune 100 retail corporation. Currently he works for a luxury brand as Principal Architect for Intelligent Automation providing technology advice for the responsible use of technology (Low Code, RPA, Chatbots, and AI). He is passionate about technology and spends his free time reading, writing technical blogs and co-chairing a special interest group with The OR Society. Interview Highlights 02:00 Mentors and peers 04:00 Community bus 07:10 Defining AI 08:20 Contextual awareness 11:45 GenAI 14:30 The human loop 17:30 Natural Language Processing 20:45 Sentiment analysis 24:00 Implementing AI solutions 26:30 Ethics and AI 27:30 Biased algorithms 32:00 EU AI Act 33:00 Responsible use of technology Connect Bala Madhusoodhanan on LinkedIn Books and references ·       https://nymag.com/intelligencer/article/ai-artificial-intelligence-chatbots-emily-m-bender.html  - NLP ·       https://www.theregister.com/2021/05/27/clearview_europe/  - Facial Technology Issue ·       https://www.designnews.com/electronics-test/apple-card-most-high-profile-case-ai-bias-yet  - Apple Card story ·       https://www.ft.com/content/2d6fc319-2165-42fb-8de1-0edf1d765be3  - Data Centre growth ·       https://www.technologyreview.com/2024/02/06/1087793/what-babies-can-teach-ai/ ·       Independent Audit of AI Systems - ·       Home | The Alan Turing Institute (https://www.turing.ac.uk/) ·       Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World, Marco Iansiti & Karim R. Lakhani ·       AI Superpowers: China, Silicon Valley, and the New World, Kai-Fu Lee ·       The Algorithmic Leader: How to Be Smart When Machines Are Smarter Than You, Mike Walsh ·       Human+Machine: Reimagining Work in the Age of AI, Paul R Daugherty, H. James Wilson ·       Superintelligence: Paths, Dangers, Strategies, Nick Bostrom ·       The Alignment Problem: How Can Artificial Intelligence Learn Human Values, Brian Christian ·       Ethical Machines: Your Concise Guide to Totally Unbiased, Transparent, and Respectful AI, Reid Blackman ·       Wanted: Human-AI Translators: Artificial Intelligence Demystified, Geertrui Mieke De Ketelaere ·       The Future of Humanity: Terraforming Mars, Interstellar Travel, Immortality, and Our Destiny Beyond, Michio Kaku, Feodor Chin et al Episode Transcript Intro: Hello and welcome to the Agile Innovation Leaders podcast. I’m Ula Ojiaku. On this podcast I speak with world-class leaders and doers about themselves and a variety of topics spanning Agile, Lean Innovation, Business, Leadership and much more – with actionable takeaways for you the listener. Ula Ojiaku So I have with me here, Bala Madhusoodhanan, who is a principal architect with a global luxury brand, and he looks after their RPA and AI transformation. So it's a pleasure to have you on the Agile Innovation Leaders podcast, Bala, thank you for making the time. Bala Madhusoodhanan It's a pleasure to have a conversation with the podcast and the podcast audience, Ula. I follow the podcast and there have been fantastic speakers in the past. So I feel privileged to join you on this conversation. Ula Ojiaku Well, the privilege is mine. So could you start off with telling us about yourself Bala, what have been the key points or the highlights of your life that have led to you being the Bala we know now? Bala Madhusoodhanan It's putting self into uncharted territory. So my background is mechanical engineering, and when I got the job, it was either you go into the mechanical engineering manufacturing side or the software side, which was slightly booming at that point of time, and obviously it was paying more then decided to take the software route, but eventually somewhere the path kind of overlapped. So from a mainframe background, started working on supply chain, and then came back to optimisation, tied back to manufacturing industry. Somewhere there is an overlap, but yeah, that was the first decision that probably got me here. The second decision was to work in a UK geography, rather than a US geography, which is again very strange in a lot of my peers. They generally go to Silicon Valley or East Coast, but I just took a choice to stay here for personal reasons. And then the third was like the mindset. I mean, I had over the last 15, 20 years, I had really good mentors, really good peers, so I always had their help to soundboard my crazy ideas, and I always try to keep a relationship ongoing. Ula Ojiaku What I'm hearing is, based on what you said, lots of relationships have been key to getting you to where you are today, both from mentors, peers. Could you expand on that? In what way? Bala Madhusoodhanan The technology is changing quite a lot, at least in the last 10 years. So if you look into pre-2010, there was no machine learning or it was statistics. People were just saying everything is statistics and accessibility to information was not that much, but post 2010, 2011, people started getting accessibility. Then there was a data buzz, big data came in, so there were a lot of opportunities where I could have taken a different career path, but every time I was in a dilemma which route to take, I had someone with whom either I have worked or who was my team lead or manager to guide me to tell me, like, take emotion out of the decision making and think in a calm mind, because you might jump into something and you might like it, you might not like it, you should not regret it. So again, over the course of so many such decisions, my cognitive mind has also started thinking about it. So those conversations really help. And again, collective experience. If you look into the decision making, it's not just my decision, I'm going through conversations that I had with people where they have applied their experience, so it's not just me or just not one situation, and to understand the why behind that, and that actually helps. In short, it's like a collection of conversations that I had with peers. A few of them are visionary leaders, they are good readers. So they always had a good insight on where I should focus, where I shouldn't focus, and of late recently, there has been a community bus. So a lot of things are moving to open source, there is a lot of community exchange of conversation, the blogging has picked up a lot. So, connecting to those parts also gives you a different dimension to think about. Ula Ojiaku So you said community bus, some of the listeners or people who are watching the video might not understand what you mean by the community bus. Are you talking about like meetups or communities that come around to discuss shared interests? Bala Madhusoodhanan If you are very much specifically interested in AI, or you are specifically interested in, power platform or a low code platform, there are a lot of content creators on those topics. You can go to YouTube, LinkedIn, and you get a lot of information about what's happening. They do a lot of hackathons, again, you need to invest time in all these things. If you don't, then you are basically missing the boat, but there are various channels like hackathon or meetup groups, or, I mean, it could be us like a virtual conversation like you and me, we both have some passionate topics, that's why we resonate and we are talking about it. So it's all about you taking an initiative, you finding time for it, and then you have tons and tons of information available through community or through conferences or through meetup groups. Ula Ojiaku Thanks for clarifying. So, you said as well, you had a collection of conversations that helped you whenever you were at a crossroad, some new technology or something emerges or there's a decision you had to make and checking in with your mentors, your peers and your personal Board of Directors almost, that they give you guidance. Now, looking back, would you say there were some turns you took that knowing what you know now, you would have done differently? Bala Madhusoodhanan I would have liked to study more. That is the only thing, because sometimes the educational degree, even though without a practical knowledge has a bigger advantage in certain conversation, otherwise your experience and your content should speak for you and it takes a little bit of effort and time to get that trust among leaders or peers just to, even them to trust saying like, okay, this person knows what he's talking about. I should probably trust rather than, someone has done a PhD and it's just finding the right balance of when I should have invested time in continuing my education, if I had time, I would have gone back two years and did everything that I had done, like minus two years off-set it by two years earlier. It would have given me different pathways. That is what I would think, but again, it's all constraints. I did the best at that point in time with whatever constraints I had. So I don't have any regret per se, but yeah, if there is a magic wand, I would do that. Ula Ojiaku So you are a LinkedIn top voice from AI. How would you define AI, artificial intelligence? Bala Madhusoodhanan I am a bit reluctant to give a term Artificial Intelligence. It's in my mind, it is Artificial Narrow Intelligence, it's slightly different. So let me start with a building block, which is machine learning. So machine learning is like a data labeller. You go to a Tesco store, you read the label, you know it is a can of soup because you have read the label, your brain is not only processing that image, it understands the surrounding. It does a lot of things when you pick that can of soup. You can't expect that by just feeding one model to a robot. So that's why I'm saying like it's AI is a bit over glorified in my mind. It is artificial narrow intelligence. What you do to automate certain specific tasks using a data set which is legal, ethical, and drives business value is what I would call machine learning, but yeah, it's just overhyped and heavily utilised term AI. Ula Ojiaku You said, there's a hype around artificial intelligence. So what do you mean by that? And where do you see it going? Bala Madhusoodhanan Going back to the machine learning definition that I said, it's basically predicting an output based on some input. That's as simple as what we would say machine learning. The word algorithm is basically something like a pattern finder. What you're doing is you are giving a lot of data, which is properly labelled, which has proper diversity of information, and there are multiple algorithms that can find patterns. The cleverness or engineering mind that you bring in is to select which pattern or which algorithm you would like to do for your use case. Now you're channelling the whole machine learning into one use case. That's why I'm going with the term narrow intelligence. Computers can do brilliant jobs. So you ask computers to do like a Rubik's cubes solving. It will do it very quickly because the task is very simple and it is just doing a lot of calculation. You give a Rubik's cube to a kid. It has to apply it. The brain is not trained enough, so it has to cognitively learn. Maybe it will be faster. So anything which is just pure calculation, pure computing, if the data is labelled properly, you want to predict an outcome, yes, you can use computers. One of the interesting videos that I showed in one of my previous talks was a robot trying to walk across the street. This is in 2018 or 19. The first video was basically talking about a robot crossing a street and there were vehicles coming across and the robot just had a headbutt and it just fell off. Now a four year old kid was asked to walk and it knew that I have to press a red signal. So it went to the signal stop. It knew, or the baby knew that I can only walk when it is green. And then it looks around and then walks so you can see the difference – a four year old kid has a contextual awareness of what is happening, whereas the robot, which is supposed to be called as artificial intelligence couldn't see that. So again, if you look, our human brains have been evolved over millions of years. There are like 10 billion neurons or something, and it is highly optimised. So when I sleep, there are different set of neurons which are running. When I speak to you, my eyes and ears are running, my motion sensor neurons are running, but these are all highly optimised. So the mother control knows how much energy should be sent on which neuron, right, whereas all these large language models, there is only one task. You ask it, it's just going to do that. It doesn't have that intelligence to optimise. When I sleep, maybe 90 percent of my neurons are sleeping. It's getting recharged. Only the dream neurons are working. Whereas once you put a model live, it doesn't matter, all the hundred thousand neurons would run. So, yeah, it's in very infancy state, maybe with quantum computing, maybe with more power and better chips things might change, but I don't see that happening in the next five to 10 years. Ula Ojiaku Now, what do you say about Gen AI? Would you also classify generative AI as purely artificial neural intelligence? Bala Madhusoodhanan The thing with generative AI is you're trying to generalise a lot of use cases, say ChatGPT, you can throw in a PDF, you can ask something, or you can say, hey, can you create a content for my blog or things like that, right? Again, all it is trying to do is it has some historical content with which it is trying to come up with a response. So the thing that I would say is humans are really good with creativity. If a problem is thrown at a person, he will find creative ways to solve it. The tool with which we are going to solve might be a GenAI tool, I don't know, because I don't know the problem, but because GenAI is in a hype cycle, every problem doesn't need GenAI, that's my view. So there was an interesting research which was done by someone in Montreal University. It talks about 10 of the basic tasks like converting text to text or text to speech and with a generative AI model or multiple models, because you have a lot of vendors providing different GenAI models, and then they went with task specific models and the thing that they found was the task specific models were cheap to run, very, very scalable and robust and highly accurate, right. Whereas GenAI, if, when you try to use it and when it goes into a production ready or enterprise ready and if it is used by customers or third party, which are not part of your ecosystem, you are putting yourself in some kind of risk category. There could be a risk of copyright issues. There could be a risk of IP issues. There could be risk of not getting the right consent from someone. I can say, can you create an image of a podcaster named Ula? You never know because you don't remember that one of your photos on Google or Twitter or somewhere is not set as private. No one has come and asked you saying, I'm using this image. And yeah, it's finding the right balance. So even before taking the technology, I think people should think about what problem are they trying to solve? In my mind, AI or artificial intelligence, or narrow intelligence can have two buckets, right. The first bucket is to do with how can I optimise the existing process? Like there are a lot of things that I'm doing, is there a better way to do it? Is there an efficient way to do it? Can I save time? Can I save money? Stuff like that. So that is an optimisation or driving efficiency lever. Other one could be, I know what to do. I have a lot of data, but I don't have infrastructure or people to do it, like workforce augmentation. Say, I have 10 data entry persons who are graduate level. Their only job is to review the receipts or invoices. I work in FCA. I have to manually look at it, approve it, and file it, right? Now it is a very tedious job. So all you are doing is you are augmenting the whole process with an OCR engine. So OCR is Optical Character Recognition. So there are models, which again, it's a beautiful term for what our eyes do. When we travel somewhere, we get an invoice, we exactly know where to look, right? What is the total amount? What is the currency I have paid? Have they taken the correct credit card? Is my address right? All those things, unconsciously, your brain does it. Whereas our models given by different software vendors, which have trained to capture these specific entities which are universal language, to just pass, on data set, you just pass the image on it. It just picks and maps that information. Someone else will do that job. But as part of your process design, what you would do is I will do the heavy lifting of identifying the points. And I'll give it to someone because I want someone to validate it. It's human at the end. Someone is approving it. So they basically put a human in loop and, human centric design to a problem solving situation. That's your efficiency lever, right? Then you have something called innovation level - I need to do something radical, I have not done this product or service. Yeah, that's a space where you can use AI, again, to do small proof of concepts. One example could be, I'm opening a new store, it's in a new country, I don't know how the store layout should look like. These are my products. This is the store square footage. Can you recommend me the best way so that I can sell through a lot? Now, a visual merchandising team will have some ideas on where the things should be, they might give that prompt. Those texts can be converted into image. Once you get the base image, then it's human. It's us. So it will be a starting point rather than someone implementing everything. It could be a starting point. But can you trust it? I don't know. Ula Ojiaku And that's why you said the importance of having a human in the loop. Bala Madhusoodhanan Yeah. So the human loop again, it's because we humans bring contextual awareness to the situation, which machine doesn't know. So I'll tie back this to the NLP. So Natural Language Processing, it has two components, so you have natural language understanding and then you have natural language generation. When you create a machine learning model, all it is doing is, it is understanding the structure of language. It's called form. I'm giving you 10,000 PDFs, or you're reading a Harry Potter book. There is a difference between you reading a Harry Potter book and the machine interpreting that Harry Potter book. You would have imagination. You will have context of, oh, in the last chapter, we were in the hilly region or in a valley, I think it will be like this, the words like mist, cold, wood. You started already forming images and visualising stuff. The machine doesn't do that. Machine works on this is the word, this is a pronoun, this is the noun, this is the structure of language, so the next one should be this, right? So, coming back to the natural language understanding, that is where the context and the form comes into play. Just think of some alphabets put in front of you. You have no idea, but these are the alphabet. You recognise A, you recognise B, you recognise the word, but you don't understand the context. One example is I'm swimming against the current. Now, current here is the motion of water, right? My current code base is version 01. I'm using the same current, right? The context is different. So interpreting the structure of language is one thing. So, in natural language understanding, what we try to do is we try to understand the context. NLG, Natural Language Generation, is basically how can I respond in a way where I'm giving you an answer to your query. And this combined is NLP. It's a big field, there was a research done, the professor is Emily Bender, and she one of the leading professors in the NLP space. So the experiment was very funny. It was about a parrot in an island talking to someone, and there was a shark in between, or some sea creature, which basically broke the connection and was listening to what this person was saying and mimicking. Again, this is the problem with NLP, right? You don't have understanding of the context. You don't put empathy to it. You don't understand the voice modulation. Like when I'm talking to you, you can judge what my emotion cues are, you can put empathy, you can tailor the conversation. If I'm feeling sad, you can put a different spin, whereas if I'm chatting to a robot, it's just going to give a standard response. So again, you have to be very careful in which situation you're going to use it, whether it is for a small team, whether it is going to be in public, stuff like that. Ula Ojiaku So that's interesting because sometimes I join the Masters of Scale strategy sessions and at the last one there was someone whose organisational startup was featured and apparently what their startup is doing is to build AI solutions that are able to do sentiment analysis. And I think some of these, again, in their early stages, but some of these things are already available to try to understand the tone of voice, the words they say, and match it with maybe the expression and actually can transcribe virtual meetings and say, okay, this person said this, they looked perplexed or they looked slightly happy. So what do you think about that? I understand you're saying that machines can't do that, but it seems like there are already organisations trying to push the envelope towards that direction. Bala Madhusoodhanan So the example that you gave, sentiment of the conversation, again, it is going by the structure or the words that I'm using. I am feeling good. So good, here is positive sentiment. Again, for me the capability is slightly overhyped, the reason being is it might do 20 percent or 30 percent of what a human might do, but the human is any day better than that particular use case, right? So the sentiment analysis typically works on the sentiment data set, which would say, these are the certain proverbs, these are the certain types of words, this generally referred to positive sentiment or a good sentiment or feel good factor, but the model is only good as good as the data is, right? So no one is going and constantly updating that dictionary. No one is thinking about it, like Gen Z have a different lingo, millennials had a different lingo. So, again, you have to treat it use case by use case, Ula. Ula Ojiaku At the end of the day, the way things currently are is that machines aren't at the place where they are as good as humans. Humans are still good at doing what humans do, and that's the key thing. Bala Madhusoodhanan Interesting use case that I recently read probably after COVID was immersive reading. So people with dyslexia. So again, AI is used for good as well, I'm not saying it is completely bad. So AI is used for good, like, teaching kids who are dyslexic, right? Speech to text can talk, or can translate a paragraph, the kid can hear it, and on the screen, I think one note has an immersive reader, it actually highlights which word it is, uttering into the ears and research study showed that kids who were part of the study group with this immersive reading audio textbook, they had a better grasp of the context and they performed well and they were able to manage dyslexia better. Now, again, we are using the technology, but again, kudos to the research team, they identified a real problem, they formulated how the problem could be solved, they were successful. So, again, technology is being used again. Cancer research, they invest heavily, in image clustering, brain tumours, I mean, there are a lot of use cases where it's used for good, but then again, when you're using it, you just need to think about biases. You need to understand the risk, I mean, everything is risk and reward. If your reward is out-paying the minimum risk that you're taking, then it's acceptable. Ula Ojiaku What would you advise leaders of organisations who are considering implementing AI solutions? What are the things we need to consider? Bala Madhusoodhanan Okay. So going back to the business strategy and growth. So that is something that the enterprises or big organisations would have in mind. Always have your AI goals aligned to what they want. So as I said, there are two buckets. One is your efficiency driver, operational efficiency bucket. The other one is your innovation bucket. Just have a sense check of where the business wants to invest in. Just because AI is there doesn't mean you have to use it right. Look into opportunities where you can drive more values. So that would be my first line of thought. The second would be more to do with educating leaders about AI literacy, like what each models are, what do they do? What are the pitfalls, the ethical awareness about use of AI, data privacy is big. So again, that education is just like high level, with some examples on the same business domain where it has been successful, where it has been not so successful, what are the challenges that they face? That's something that I would urge everyone to invest time in. I think I did mention about security again, over the years, the practice has been security is always kept as last. So again, I was fortunate enough to work in organisations where security first mindset was put in place, because once you have a proof of value, once you show that to people, people get excited, and it's about messaging it and making sure it is very secured, protecting the end users. So the third one would be talking about having secure first design policies or principles. Machine learning or AI is of no good if your data quality is not there. So have a data strategy is something that I would definitely recommend. Start small. I mean, just like agile, you take a value, you start small, you realise whether your hypothesis was correct or not, you monitor how you performed and then you think about scale just by hello world doesn't mean that you have mastered that. So have that mindset, start small, monitor, have constant feedback, and then you think about scaling. Ula Ojiaku What are the key things about ethics and AI, do you think leaders should be aware of at this point in time? Bala Madhusoodhanan So again, ethical is very subjective. So it's about having different stakeholders to give their honest opinion of whether your solution is the right thing to do against the value of the enterprise. And it's not your view or my view, it's a consent view and certain things where people are involved, you might need to get HR, you might need to get legal, you might need to get brand reputation team to come and assist you because you don't understand the why behind certain policies were put in place. So one is, is the solution or is the AI ethical to the core value of the enterprise? So that's the first sense check that you need to do. If you pass that sense check, then comes about a lot of other threats, I would say like, is the model that I'm using, did it have a fair representation of all data set? There's a classic case study on one of a big cloud computing giant using an AI algorithm to filter resumes and they had to stop it immediately because the data set was all Ivy League, male, white, dominant, it didn't have the right representation. Over the 10 years, if I'm just hiring certain type of people, my data is inherently biased, no matter how good my algorithm is, if I don't have that data set. The other example is clarify AI. They got into trouble on using very biased data to give an outcome on some decision making to immigration, which has a bigger ramification. Then you talk about fairness, whether the AI system is fair to give you an output. So there was a funny story about a man and a woman in California living together, and I think the woman wasn't provided a credit card, even though everything, the postcode is the same, both of them work in the same company, and it was, I think it has to do with Apple Pay. Apple Pay wanted to bring in a silver credit card, Apple card or whatever it is, but then it is so unfair that the women who was equally qualified was not given the right credit limit, and the bank clearly said the algorithm said so. Then you have privacy concern, right? So all these generic models that you have that is available, even ChatGPT for that matter. Now you can chat with ChatGPT multiple times. You can talk about someone like Trevor Noah and you can say hey, can you create a joke? Now it has been trained with the jokes that he has done, it might be available publicly. But has the creator of model got a consent saying, hey Trevor, I'm going to use your content so that I can give better, and how many such consent, even Wikipedia, if you look into Wikipedia, about 80 percent of the information is public, but it is not diversified. What I mean by that is you can search for a lot of information. If the person is from America or from UK or from Europe, maybe from India to some extent, but what is the quality of data, if you think about countries in Africa, what do you think about South America? I mean, it is not representing the total diversity of data, and we have this large language model, which has been just trained on that data, right? So there is a bias and because of that bias, your outcome might not be fair. So these two are the main things, and of course the privacy concern. So if someone goes and says, hey, you have used my data, you didn't even ask me, then you're into lawsuit. Without getting a proper consent, again, it's a bad world, it's very fast moving and people don't even, including me, I don't even read every terms and condition, I just scroll down, tick, confirm, but those things are the things where I think education should come into play. Think about it, because people don't understand what could go wrong, not to them, but someone like them. Then there is a big fear of job displacement, like if I put this AI system, what will I do with my workforce? Say I had ten people, you need to think about, you need to reimagine your workplace. These are the ten jobs my ten people are doing. If I augment six of those jobs, how can I use my ten resources effectively to do something different or that piece of puzzle is always, again, it goes back to the core values of the company, what they think about their people, how everything is back, but it's just that needs a lot of inputs from multiple stakeholders. Ula Ojiaku It ties back to the enterprise strategy, there is the values, but with technology as it has evolved over the years, things will be made obsolete, but there are new opportunities that are created, so moving from when people travelled with horses and buggies and then the automotive came up. Yes, there wasn't as much demand for horseshoes and horses and buggies, but there was a new industry, the people who would mechanics or garages and things like that. So I think it's really about that. Like, going back to what you're saying, how can you redeploy people? And that might involve, again, training, reskilling, and investing in education of the workforce so that they're able to harness AI and to do those creative things that you've emphasised over this conversation about human beings, that creative aspect, that ability to understand context and nuance and apply it to the situation. Bala Madhusoodhanan So I was fortunate to work with ForHumanity, an NGO which basically is trying to certify people to look into auditing AI systems. So EU AI Act is now in place, it will be enforced soon. So you need people to have controls on all these AI systems to protect - it's done to protect people, it's done to protect the enterprise. So I was fortunate enough to be part of that community. I'm still working closely with the Operation Research Society. Again, you should be passionate enough, you should find time to do it, and if you do it, then the universe will find a way to give you something interesting to work with. And our society, The Alan Turing Institute, the ForHumanity Society, I had a few ICO workshops, which was quite interesting because when you hear perspectives from people from different facets of life, like lawyers and solicitors, you would think, ah, this statement, I wouldn't interpret in this way. It was a good learning experience and I'm sure if I have time, I would still continue to do that and invest time in ethical AI. As technology, it's not only AI, it's ethical use of technology, so sustainability is also part of ethical bucket if you look into it. So there was an interesting paper it talks about how many data centres have been opened between 2018 to 2024, which is like six years and the power consumption has gone from X to three times X or two times X, so we have opened a lot. We have already caused damage to the environment with all these technology, and just because the technology is there, it doesn't mean you have to use it, but again, it's that educational bit, what is the right thing to do? And even the ESG awareness, people are not aware. Like now, if you go to the current TikTok trenders, they know I need to look into certified B Corp when I am buying something. The reason is because they know, and they're more passionate about saving the world. Maybe we are not, I don't know, but again, once you start educating and, telling those stories, humans are really good, so you will have a change of heart. Ula Ojiaku What I'm hearing you say is that education is key to help us to make informed choices. There is a time and place where you would need to use AI, but not everything requires it, and if we're more thoughtful in how we approach, these, because these are tools at the end of the day, then we can at least try to be more balanced in the risks and taking advantage of opportunities versus the risks around it and the impact these decisions and the tools that we choose to use make on the environment. Now, what books have you found yourself recommending most to people, and why? Bala Madhusoodhanan Because we have been talking on AI, AI Superpower is one book which was written by Kai-Fu Lee. There is this book by Brian Christian, The Alignment Problem: Machine Learning and Human Values alignment of human values and machine it was basically talking about what are the human values? Where do you want to use machine learning? How do you basically come up with a decision making, that's a really interesting read. Then there is a book called Ethical Machines by Reid Blackman. So it talks about all the ethical facets of AI, like biases, fairnesses, like data privacy, transparency, explainability, and he gives quite a detail, example and walkthrough of what that means. Another interesting book was Wanted: Human-AI Translators: Artificial Intelligence Demystified by a Dutch professor, again, really, really lovely narration of what algorithms are, what AI is, where, and all you should think about, what controls and stuff like that. So that is an interesting book. Harvard Professor Kahrim Lakhani, he wrote something called, Competing in the Age of AI, that's a good book. The Algorithmic Leader: How to Be Smart When Machines Are Smarter Than You by Mike Walsh is another good book, which I finished a couple of months back. Ula Ojiaku And if the audience wants to find you, how can they reach out to you? Bala Madhusoodhanan They can always reach out to me at LinkedIn, I would be happy to touch base through LinkedIn. Ula Ojiaku Awesome. And do you have any final words and or ask of the audience? Bala Madhusoodhanan The final word is, again, responsible use of technology. Think about not just the use case, think about the environmental impact, think about the future generation, because I think the damage is already done. So, at least not in this lifetime, maybe three or four lifetimes down the line, it might not be the beautiful earth that we have. Ula Ojiaku It's been a pleasure, as always, speaking with you, Bala, and thank you so much for sharing your insights and wisdom, and thank you for being a guest on the Agile Innovation Leaders Podcast. Bala Madhusoodhanan Thank you, lovely conversation, and yeah, looking forward to connecting with more like minded LinkedIn colleagues. Ula Ojiaku That’s all we have for now. Thanks for listening. If you liked this show, do subscribe at www.agileinnovationleaders.com or your favourite podcast provider. Also share with friends and do leave a review on iTunes. This would help others find this show. I’d also love to hear from you, so please drop me an email at ula@agileinnovationleaders.com (mailto:ula@agileinnovationleaders.com) Take care and God bless!

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