What’s the BUZZ? — AI in Business

Winning with AI (Guests: Somil Gupta, Kieran Gilmurray)

June 28, 2022 Andreas Welsch Season 1 Episode 6
What’s the BUZZ? — AI in Business
Winning with AI (Guests: Somil Gupta, Kieran Gilmurray)
What’s the BUZZ? — AI in Business
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Show Notes Transcript

In this episode, Somil Gupta (Founder Algorithmic Scale), Kieran Gilmurray (Author & Digital Transformation Expert), and Andreas Welsch discuss how business leaders can win with AI. Somil and Kieran share their insights on what it means to digitally transform an organization and provide valuable tips for listeners looking to lead AI programs to success. 

Key topics: 
- Lead digital transformation programs
- Monetize AI products & insights
- Incorporate ethics from the start

Listen to the full episode to hear how you can:
- Take a holistic view across people, place, and platform 
- Increase revenue from AI when approached as a product
- Make AI ethics a part of transformation and monetization projects 

Watch this episode on YouTube: https://youtu.be/VculKvDros8

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Disclaimer: Views are the participants’ own and do not represent those of any participant’s past, present, or future employers. Participation in this event is independent of any potential business relationship (past, present, or future) between the participants or between their employers.


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Andreas Welsch:

Today we have a special episode for you. Now, I've been playing one of my favorite retro game:"What's the BUZZ?" But I'm stuck. And that's why I've asked a few experts to help me"win with AI", Somil Gupta and Kieran Gilmurray. Thanks for joining.

Kieran Gilmurray:

Great to be here. I've been in business technology for about the last 27 years. I've described both because I love technology, but love what it can do for a business. I've sat in intelligent automation roles. I've ran AI teams for 13, 14 years. I've ran businesses, large, small, international, you name it, I've been there, done that. Hopefully today I can add some value. Recently I've written a book on the A to Z to digital transformation. I'm going to use some of the answers in that book, hopefully in today's session as well. Delighted to be here.

Somil Gupta:

Thank you. My name is Somil Gupta. I'm the founder of Algorithmic Scale. It's a company based in Sweden, and we focus on building the monetization and commercial stack for AI based business models. What it means is we help our clients build production services. We help them build digital operating models for delivering the value that they create from from AI. But also help them with value care, which is helping them with pricing strategies, helping them with different modernization strategies. And this is something which kind of very exciting. So I am very crazy about algorithmic operating models and algorithmic business models. And now this company is something that will intended to make those visions into reality.

Andreas Welsch:

Awesome. Thank you so much. If you're just joining this stream, drop a comment in the chat, what tricks you are looking for. No cheating allowed by the way. So what do you say? Should we start playing?

Kieran Gilmurray:

Oh, yes,

Andreas Welsch:

Okay, so this one is a warmup. When I hit the buzzer, you'll see a sentence. I need your help. So can you answer with the first thing that comes to mind in why, in your own words? So you only have 60 seconds for your answer. And for those of you watching us live, please drop yours in the chat as well. Team, are you ready for What's the BUZZ? If AI were city, which would it be? 60 seconds. Go.

Kieran Gilmurray:

Oh wow. Let me say Boston for a couple of reasons. What an amazing practical city. There is 1,001 amazing things. There are all wrapped up in a reasonably small package, and when everybody goes to Boston, they all experience something different. Everybody thinks of it as a different place. So I think it was a us Irish city. I'm sure a lot of other people remembered from all sorts of movies and different things, but a really practical, thriving city where a lot of tech exists, where a lot of people are making an impact and there's still lots of potential for things crazy great things to happen.

Somil Gupta:

All right. I would say Stockholm. And reason being, I think one of the things which I like about Stockholm is it has three equal qualities that you need in AI stuff. Well there is a very much centralized decision making. Very decentralized accountability. That means people are, individually very innovative and they are thinking about the problems and solutions. At the same time, there is a very collaborative culture. So collaboration is very important here in Stockholm. And we see people collaborating very openly in a very trusted environment. And that kind of builds upon each other's works, each other stores, and help us come up with very holistic solutions.

Andreas Welsch:

So great job answering on the fly. Thank you so much. And I already heard you say something about transformation, right? And that it's a significant effort. So let's take a look at the first question here. How do you define digital transformation? Maybe Kieran, can you help me with this?

Kieran Gilmurray:

This is got a big one. Let me talk about three Ps: people, process, and product. So if we're talking about digital transformation, we're talking about a mindset change. It's not a case of throwing something to the IT department saying transform it because it's technology. Everyone in the organization needs to think digital, behave digitally, and act digitally. We say act digitally. What do we mean? No longer analog. All the things that they deal with are digital. The tooling, the platform, and again, the product as well. So we're, once, we were dealing with a lot of physical products, now we're dealing with a lot of digital products. So those three things are primarily then you're trying to get all those things together. So when we're talking about digital transformation, we're talking about Change culture changing people as well as the technology. A lot of people focus on the tech side, Andreas, but people transform businesses, not technology. They used to just use really great technology to augment what they do. So once everybody's got the right mindset, they're gonna do it digitally and that matters from everyone from the post room right the way up to the chairman of the company. And I'm a tremendous fan of saying an executive team needs to understand technology because most business, strateg. These days are technology enabled. It's a requirement just like understanding finances these days. Once you've built an organization to act digitally and think digitally, your products tend to end up being digital products or there's a pile of AI or data surrounding those products allowing you to deliver new value very quickly. An example, Insurance brokers years ago used to pick up a telephone, ring them. You would get a piece of insurance sent out in the post numbers of days later. Now you're able to log into online websites that work out who you are. Work out your preferences. Work out next. Next best action. Next best offer. Product upsell, cross-sell credit ratings, debt ratings, renewal retention rates. Chances are when that goes through the system, you're ending up with a digital certificate. because the computing systems inside of the company are all based on a digital platform with a digital front, and there is very little manual paperwork. Everything's really instant. And when it comes to making a claim, take a picture with your phone, use AI to describe the accident scene, work out the value order, the breakdown vehicle, order, the garage that's going to pick up the car, give you a cost estimate, give you another vehicle or a hotel, depending on a far you are from home. All done automated without the necessity to have someone there. But at the same time, all of this tech and AI and everything else around it. You've digitally transformed your firm should be sensitive to you as an individual, and therefore it should connect you with an amazing human who understands intimately your needs because they've profiled you, done used whatever big data sets that exist on the market and are able to answer the question where humans add tremendous value and to be able to do, again, very digitally. Camera, face, phone, tech, WhatsApp, you name it, whatever channel door or whatever way you want to. They're there to do it, and all that data is connected together. So there's one customer view, everybody's aware of it, new as a customer, feel like you've been treated as an individual, but chances are you're one of many customers. So people, product place or platform, sorry, mindset change. Method change, and the actual. Digital product that you've got at the end of the day, that's changed too. And then there'll be a whole set of digital metrics around that to measure your performance. And all the time you're attempting to remove attrition from the process using technology and constantly driving forward. Otherwise you run the risk of being outta business. That's a process. Someone once called Digital Darwinism and I love that phraseology.

Andreas Welsch:

So great points all around. Thanks, Kieran. The part that really resonates with me is that about people. Because when I typically think of transformation, I think of changing the employee experience, changing something for them. But I think you made an excellent point that it's not only just about the employee, but actually also the customer. And spinning that further I think that's where Somil, you and the algorithmic decision making and your focus on that come in. So maybe let's take a look at question number two. And maybe Somil, that is something that, that you can help me with. The question is, what do companies need to do to monetize AI? So over to you, Somil.

Somil Gupta:

So one of the things, when you look at monetization is I see it's a lot about mindset. And one of the things which I'm a bit concerned is more, a lot of companies, they're working in a very project centric mindset. That means they're focusing a lot on activities and not on outcomes. And I like to think of, when I think about modernization and look at solutions, I like to think of them as like a four concentric circles. So the innermost circles your data model, and that's. Everything, the value that you have that is called your data, and then you go one circle outside and then you have the decision intelligence and you have your decision. And that's where you use your ai, you use your, and you use different techniques, make sense of that data and kind of convert that into insights and people tend to stop there. But there are two additional circles. The moment you go outside this decision intelligence, you get into the commercial model. And commercial model is really about relationships. It's about the processes. Why are we a business? What kind of relationship are we enforcing? Or what kind of relationship are we enabling? And then there's finally the external model, which is the business model inside which all of this happens, right? And that is more about how do we make money? How do we lead? If you want to go from looking at AI as project that you implement, or AI as a model, if you wanna go towards modernization, we have to follow this three step process. And it's very simple. I call it PCM: productize, commercialize, and monetize. So productization is really about packaging data and AI models in such a way where it's not only valuable, but it's also easily consumable. That means you have to go that extra mile towards your users, helping them derive value. And I actually believe that helping the end user derive value from your product is really the responsibility of the product manager. It's not the consumer's responsibility. Many people say they have to come meet halfway, but I think we have to go all the way. Commercialization that is really one part is scaling and initializing it. Other part is also to build in our ecosystem. How are you going to deliver this to your customer? How you're going to operate it? How you reorchestrate it? How you deliver services? That whole operating model principle comes into commercialization. And finally, modernization is where you get paid for all the hard work that you've done. That goes to your contracts, to your pricing. And how do you really create value for the end user, but also for yourself for being this entrepreneurial venture. So I think that's what monetizing should look like. And we need to really move ahead, move away from this project mindset. A lot of companies are trying to get us work management around data and AI. It's not really working out and get towards the product mindset, which is more focused on outcomes and more focused on kind of incremental value creation.

Andreas Welsch:

Thanks, Somil. I really like that part that you mentioned, to think more broadly and not to approach it just like any other IT project. I think that's really key. So maybe Kieran for you. So maybe you can build on that. Are you seeing a lot of customers already doing that? And a lot of companies like in the insurance example, really doing that, monetizing that?

Kieran Gilmurray:

Yeah, you do. Yeah, done right? You do you know what I mean? Because ultimately, why are we in business? Of course, this meal says it's not about just doing activities, it's about actually creating something that's of value that someone is willing to pay money for. So you can use AI in lots of ways. The example we gave a moment ago, insurance. Yes, of course. Next best action, cross sale, up, sale, renewal, retention, figures, whatever else. But I've seen some. Great companies having been set up literally to collect data, and then they're using the data to sell the data on for different purposes. So Facebook's an amazing example of that. At TikTok, you name it, all these companies are set up to collect vast amounts of data, and then they use that data to allow other companies to target, or they sell services like LinkedIn, to do targeting marketing, you name it. You can use the data that you've got for a tremendous amount, in a tremendous amount of different ways to actually derive value. The other bit as well is we're talking about, that's external value for me. We're looking at the internal value as well. So if I start to get IoT devices and you're start seeing a lot of shipping in companies to be a lot more. Careful around what they're doing, and this can be anything from literally logistics, shipping from one country to another, and then use the analytics to work out when should I travel? How long should I travel, how quick should I travel? You'll see the same thing with airlines or cargo ships or whatever else, right? The way to building digital twins, for example, if I've got an oil field sitting somewhere in the North Sea or in wherever it is, you start to see companies using AI to build digital twins of those, to operate them in the most efficient manner possible. You also see them using AI to do predictive maintenance on equipment because every hour that those particular facilities are down, it's not, hundreds of dollars. It's potentially hundreds of millions of dollars with these things are not actually operating. You see the real value of that now in the world where energy and production costs and oil costs and whatever else are going through the roof. They're really smart. Companies are looking at every single part of their value chain. Both the customer end, their own performance end and everything in between. And then they're using other analytics in tremendous ways around the people analytics. Looking at how likely people are to stay looking at analytics as to who they should hire. Looking at analytics to work out how do you put teams together to work in the best possible, most efficient way. And everything else in between. So AI analytics are amazingly powerful when you use them across the entire value chain. But you need to be clear, as someone says in the very first instance, what are the outcomes you're actually trying to get? And some of those might be third party outcomes, deliberately like Facebook. Some of those might be deliberate. Building a digital twin and working off that to drive efficiency. And some of them could actually be what I describe as making money off the exhaust fumes of companies activities. So I've seen some companies that are set up to do quoting, they can't actually quote for a particular piece of business. And what they've done is use the AI or the data that they've collected to sell on to other companies, and they're making money outta that AI as well. A clever example is a, again, a large company in the consumer space, they get thousands of people and companies giving them their very latest details every day to get. Quoted on a particular type of business I can't name as private company. And then what they actually do is sell that company on to other insurance companies, other targeting companies, other companies that validate that these companies exist with the latest contact details and the directors and everything else to do credit checks and so there's really clever things happening across the company's value chain. And then they're taking that data, selling it on to allow other companies to do interesting and different things. But always with a business icon in mind.

Andreas Welsch:

So here, and I think you're making a key point here, right? That it's really about the business outcome and the business value and making all of that measurable. Because a lot of times I also hear about, we're doing automation and it'll lead to a better employee experience. And I think that's important and good and fair. Of course we want our employees to feel. Good about their work and be happy and enjoy coming to work. But that's certainly not enough, right? It needs to be more, and it needs to be measurable. So I really like that component that you are highlighting that here. And so maybe question for you, Somil. Kieran already talked about insurance is one example, but what types of data are you seeing companies monetize with?;

Somil Gupta:

So as the field itself is still evolving for reason because a lot of companies are still trying to get their head around the data quality, and for me, data quality is always fit for purpose and then know depends a lot on what you want to do. But lot of companies have come upon themselves to define the pristine. Got like data, whatever they wanna have. It's more world of fantasy. IoT data is one thing that we see most of the companies they want to now monetize because you get like real information about what's happening on the ground. Logistics data, definitely very much in use. I think financial data that companies are producing, transactional data. It's not so much for me about what data, but it's like what purpose they're driving. The data sources can come from both internally, externally, from devices, but it's more about what companies need to figure out is how are we differentiating ourself? How are we positioning ourself? How are we really creating value in this thing? So IoT is, for example, one of the things that people have interest in. But I think there's a long way to go to really integrate that data in other source of data to create something tangible.

Andreas Welsch:

I see. When we talk about data, access to data, different types of data, monetizing it for improving the customer experience, improving the employee experience. I think that's a key point here. So maybe let's take a look at question number three. And that is, what is the role of ethics in this? Maybe that's something you can help me with. Kieran, can you take a stab at that?

Kieran Gilmurray:

Yeah it's a massive topic and has probably come to the fore in the press around that company mentioned earlier on, Facebook and we had Cambridge Analytica who took the data and used that to what you might describe, profile people and then try and persuade people to do particular things. An example of. Was it the best use of data possibly for the company? Was it the best use of data for political parties and people themselves? Debatable, probably not. It's all new technologies. This is an interesting one because if you're talking about IT, programming in general, now we're worried about security and people, company security and financial loss ethics has been one of those things where everybody got really excited about the power of and they probably got so excited that they concentrated on doing the actual work. But when they worked all these things through, they discovered that there was a genuine people impact. Let me go back to insurance and give you an example. A lot of companies run retention analytics. So what they do is they build a model. They work out which customers are likely to stay with them and which customers are not likely to. Very often what they do is they actually price the insurance according to that. So if you're highly likely to stay, you will not get a discount from a broker or insurance company. Chances are you'll actually get a price rise if you're highly likely to leave. They will probably offer you a discount to stay. Statistically speaking, because I've been in the industry. The older generation, and again, whatever age you wanna put that, 50, 67, they tend not to look around, tend to trust at times, tended not to be as digitally literate. So what you're actually doing is targeting people who had. Very little propensity to move and you are constantly raising the prices, whereas someone in a different cohort would've left and they were getting a different price for exactly the same product, not exactly ethically sound, and probably against numbers of insurance regulations at the same time. You do see that as well in. Referencing and credit agencies. If you are someone on a lower income and have a per credit history, then when it comes to accessing credit and buying something like a car or a financial product or something else, a house of mortgage to cover that risk company's actually gonna charge you more money. Now, again, you know at. May be correct from a company point of view, but how ethical is it that you or I who are coming as two consumers who are probably the same but Mightly have slightly different credit history, are getting two different prices. So anywhere from potential manipulation of people, manipulation of pricing, treating people differently, who are getting the same product. All these are is are technically and have risen. Poor ethical practices and poor outcomes of using analytics that resulted in some harm or other two consumers. From a company point of view though, just remember this, if we're talking purely financial, using analytics to make those decisions to derive the most profit that you possibly can. Is something that most companies want to strive for. Therefore, we end up with an ethical debate. How ethical is it for a company that's set up to make profit, to use every tool at its advantage? And again, if you have superior technology, you're gonna do better than a company that doesn't. But data analytics is one of those things that's really risen to the fore run, this particular topic, it's really powerful where machines can crunch large volumes of data that companies have into Samir's Point. They can also now buy other sets of data to allow them to make more micro decisions. They can be more persuasive using data. If you combine that data with behavioral psychology NLP neurolinguistic programming, you can start to tangibly manipulate, and I use that word, both positively and negatively, how people will respond to your product and your price and everything else in between. I personally think. Like data companies need to think through all of the operations that they're doing. Companies who use data need to think all the way through to the end of their operations. The need to focus on consumers to make sure they're providing great value and not manipulating them. Can we trust companies to do that? Question mark. I don't think every company in the world is a horrible, unethical group. You might assume it the way the press treats companies who deal with ai. I do think there's companies out there that do forget their responsibility, their bigger responsibility to society and individuals to treat them fairly and equitably. Do we need a regulator? Question mark. The medical industry, for example, as a regulator to make sure that people build medical or medicines that don't harm people. Is it arguable that because data's become so prevalent and so important in society, that we need a regulator for a lot to allow companies to behave honorably and equitably possibly, and probably.

Andreas Welsch:

Thanks. So ethics obviously play plays a key role. I know there is a lot of debate happening between experts and also on a government level, like at the E.U. with the AI Act or even over here in, in the U.S. But maybe question for you, building on that Somil what do you see in your work? How early should companies really think about ethics when they do work around AI? And who should actually own this topic of AI ethics?

Somil Gupta:

Yeah, I focused a lot on the ethical commercialization, ethical modernization. The only kind of thing is that I think the ethics discussion should not be an afterthought. Ethics has to be built into the design of the product. Before we even start talking about a product, we start building the value framework. And in that value framework, we have to start embedding ethical constraints and and ethical policies. The reason why this is important is if you're not, put that in the initial part of framing of the problem, both technical framing and commercial framing, then what happens is that even while you get your model up and running, it's totally too late to look after ethics after the model is being built. And then we will be like to think that our data scientists are smart enough to figure out how the model is responding, and that is completely not true. Once our model is deployed, it tends to have its own life and how it's making prediction. Of course, you could figure it out later on if you do a diagnostics, but then it's already too late. Denial of service, infringing of somebody's rights manipulation social engineering, all those are real threats now. You cannot wait late enough. You can't put early enough. I can't stress upon this. The framework that we create, the value framework. In that value framework that we created the first time, that is where we have to start looking at ethics. Because you start looking at who are the people who are going to be most vulnerable? I think you give an example of the people who are elderly, who might be impacted the most, but who are the least who also the most vulnerable. We also saw some examples of gig economy, like drivers within Uber who get impacted with an algorithm, on which they have. Uber does not have any say on this. They can't figure out whether the decision was taken or was right or wrong. You have to understand that the decision intelligence works within the commercial framework. You cannot control the decision from point to point. But we can definitely control the commercial framework which we operate. And we have to realize that these companies, they have massive amounts of data, thousands of data points about us. They have a lot of power. In what they can do. So then really comes down to this ethical design of offerings, ethical pricing of offerings. Even though a risk like dynamic pricing. So when we are doing dynamic pricing, we should not infringe upon somebody's rights. We should not discriminate against people. So that's the kind of thing which is a trade off between doing what is right versus doing what is easy. And I hate to say that, but a lot of companies, they're not really inputting a thought around these topics. So all these ethics and everything, governance, they come very late in the cycle by then, it's usually too late to do anything about it.

Andreas Welsch:

Fantastic. So when it comes to ethics, start early and make sure it's pervasive throughout the project. That's awesome, team! We've made it. Thank you so much for helping me play"What's the BUZZ?" today?

Kieran Gilmurray:

Hurray!

Andreas Welsch:

So let me quickly summarize. First of all, if you think about digital transformation, keep the three Ps in mind: people, place, and platform. But really people should be the focus and the center of your initiatives, whether it's your employees or your customers, because the transformation certainly impacts them in different ways. Secondly, if you're thinking about monetization of AI, really don't treat it like any other IT project, because it's not. The different layers of it in and AI should really be core. And as you're doing that, you get to number three, make sure that ethics is a part of it from the very beginning, all the way to the end of that project. So we're getting close to the end of the show.

Somil Gupta:

Thanks, Andreas.