28 | The AI Playbook: A Proven Framework for Implementing AI to Boost Productivity and Profits with Paul Bratcher, a technology implementation expert with 20 years of experience.

0:00Hello, and welcome to Leveraging AI. This is Isar Metis, your host, and I've got an exciting show for you today. Maybe the question I get asked the most, especially from business leaders, CEOs, and other C-suite and executives is how to get started. How does a business, not an individual, go into the process of implementing AI in an effective way that really transforms the business? And this is exactly going to be a question we're going to answer in today's show with the assistance of Paul Bratcher, who has been helping companies implement technology for the past 20 years. As always, at the end of the episode, I will review some exciting and big news that happened in the AI world this week.

0:39And now let's dive into the framework that can help businesses implement AI successfully. In the next few years, AI technology will change our world dramatically. Whether you are a business executive trying to catapult your business forward, or just somebody who refuses to be left behind and want to advance your career, this is the show for you. I'm your host, Isar Matis, a serial entrepreneur and an AI enthusiast. You'll hear invaluable practical tips from innovative business leaders, AI practitioners, and some of the brightest AI minds in our world today on how you can leverage AI in ethical ways to advance your career and grow your business.

1:27Hello and welcome to Leveraging AI, the podcast that shares practical, ethical ways to leverage AI to improve efficiency, grow your business, and advance your career. This is Isar Maitis, your host, and I've got a special guest for you today. Paul Bratcher has been in the forefront of technology his entire life. He has been implementing digital transformation and emerging technologies in anything from huge international large corporations through his own entrepreneurship journey, as well as holding different fractional leadership positions in various size and types of companies. So he has over 20 years of experience in implementing new technologies in businesses as a transformative process.

2:12And I hope he's not mad for me dating him by saying it's 20 years, but he has a lot of experience in doing that. So it's not surprising that in this past year, he has been helping companies implement artificial intelligence in businesses because that's the new technology that's around. But for him, it's just another one of those technologies that he's helping businesses implement, which makes it very interesting because he has a unique approach on how to do this right. How can and should a business look at AI from a more holistic approach instead of jumping into the tactics, instead of that, looking at what is the best use of AI for our business, how to figure that out.

2:54And this is going to be the core of our conversation. I think this is incredibly important to any business today because more or less every business today is considering implementing AI and most businesses don't know exactly where to start. And Paul is the perfect person to have that conversation with. So I'm very excited to have Paul as a guest. Paul, welcome to Leveraging AI. Hey, thanks, Matt. It's great to be here. And yeah, I don't mind you dating me at all. I think myself and the other two founders of our company, between as we worked out, we have 750 ,000 hours of experience of implementing transformation in UK retail.

3:31That's incredible. That's a really, really big number. Yeah, we've done some things and we've learned some things along the way. So hopefully some of those will come out in today's conversation. Awesome. So I really think let's just dive right in. If I'm a business owner and there's all this AI craze going around and there's a huge FOMO going on of I got to implement AI because everybody else is doing it. And if not, I'm going to lose the competitive edge and the competition is going to eat my lunch. what are the first things as a business owner or somebody's in leadership i need to consider and look at before i start diving in into any implementation process so i think the first thing i see ceo that's asking this all the time is why and where should i start that's the first question and the first kind of little bit of advice is if you're interested in ai and you've been listening to AI, then you'll build this mindset that everybody's doing AI.

4:33And actually, when I speak to a wide broad of companies, a few are doing AI, a few are doing it really well. A lot of people are trying to answer the, how do I start question. And my first answer to that is, you need to start. Because that is step one of the process. You need to start. It doesn't really matter where you start, but you need to start somewhere. And this is just change practice. and the first thing I remind people is we've done change for 20 years from digital transformation, from cloud models, from software as a service. It's the same thing. It just happens that the tooling this time is badged AI.

5:13And the things we learned about change were it's entirely about the people and it's entirely about having a clear vision and it's entirely about being able to communicate that vision. And the tools will take care of themselves. Yeah. So once we all take a breath from that, and then I like to explain about the overlap of changing technology. And the next thing I will talk about is, if you think about digital transformation for the last five years, what's really new? Like nothing. It's got to the end of its curve of activity. If you think about AI, everything's new. Every day there's thousands of new tools, there's thousands of things.

5:55So you've got these two overlapping technologies and the digital transformation age is basically ending. And we're now entering what we describe as the augmentation age, which is about how do you join AI tooling to your workforce and generate a better solution for everyone else? I love what you're saying. I think I want, but okay. So I want to summarize some of what you said that it's, it happened before. Like nothing is new. It's just another new set of technologies that is coming in that the world will have to adopt to and work with. So that's not new. I think the thing that is new, and I'm roughly the same date as you, so like the same production date as you.

6:39So I've been through a lot of it, mostly as CEO of tech startups or in leadership positions in tech startups. And I think the biggest difference between all those other ones that happened before is speed. So I'll give a simple example from my side, and then I would love your opinion on this as somebody who's been helping companies through this. I first logged into the internet in 93 or 94. Like my roommate at the time bought a modem. He said, there's this new thing. It's called the internet. And I'm like, okay, what does he do? He said, I don't know, but we're going to test it out. So he bought a modem.

7:18We connected. He went one of those for a while. And then we got up like a prompt line. Those of you who remember DOS, that's what we had on the screen. And I'm like, okay, so what do we do now? I don't know. And that was the most underwhelming technological experience of my life because we connected to the internet, but we didn't really know what to do with it. So that was 94. The first time I got an email address is in 99 when I went backpacking in South America. So that's five years later. So from the moment the internet thing started to the moment I get an email address, forget about all the stuff we do today, five years went along and I'm an early adopter tech guy who loves this kind of stuff and geeks around it.

7:59What's happening now is within November of last year, ChatGPT launched. And it seems like you're saying that every new day there's new tech to learn. So the speed in which things are happening and the amount of aspects of a business I'm putting aside our social life and personal life. The amount of aspects in a business he touches is almost everything. And I think that's the biggest difference between this cycle and every other cycle, at least in my eyes. What are your thoughts on that? I think that's very true. I think I hope to define people's thinking in there was and there wasn't. So you remember when there wasn't internet and then there was.

8:42My son's outside behind me. there indeed there was iPhones and then there wasn't and they're distinct marks in technological time. And I think right now we're on the there wasn't AI and now there is. It's a step, almost a binary step from the past to the next. And the thing that's really fascinating that from a human change point of view is the last 20 years have really been targeted at automating away what you would broadly describe as non-knowledge worker tasks. But the new AI technology strikes straight into things like UX design, business process design, content creation, human relationship processing, CRF.

9:29It's deep into, ironically, the people over the last 10 years have been changing everyone else's job. Now it's there to have their job changed. and it's quite interesting watching all these like project managers and business analysts and developers grasping and struggling with the change impact so I think it's a time to as with all change to be sensitive so I agree so to that end I guess the question that leads on to is what is AI good for yeah yeah because you said you said your first recommendations get started but that's okay how do I get started what is this thing I get started with So we ask companies to think about AI in a couple of ways.

10:12The first is, and I'll come on to business cases. We have a playbook we use for business cases, and it leans into our kind of ethical social responsibility ethos as what our company is all about. But the first thing we start about is saying, if you start to read the news and you ignore the AI cult of doom madness and clickbait and get to the principle, there's basically you need to think about ai with three and three in a sort of triangle of three thoughts and at the moment everyone's talking about thinking ai as a brain as a thought engine either large language models or more excitedly protein folding if you've been reading that kind of scientific research or a conversation with a phd in ai in about 92 and i think the first AI computer science papers like 1957 or something like that.

11:07So it's not new. It's just accelerating. So there's this talk about this one corner of the triangle which is all about thinking. And we like to say that there's two others. To make a holistic thought about AI, you need to think about sensing or input. And that can either be things like computer vision. And a really good application for computer vision at the moment is things like mounted above assembly lines, checking assembly quality. Okay. There's firms I know of that are using AI to record movements at self-checkout to try and spot people who accidentally fail to scan things. And then maybe encourage them to scan them, for example.

11:49And that's all computer vision orientated. So this, you think about, you've got input sensing. How do I gather understanding of what's going around me? I saw a very interesting use case, not in vision, but they actually use vision as well. but most of it is sound, they record machines. Or they don't record, they listen to machines and compare it to the original recording. And they can detect the beginning of a potential fault in specific parts of the machine based on a very detailed understanding of the sounds that it makes. And it helps you fix that component of the machine before the machine breaks and stops your assembly line or your production line or whatever it is that you're doing.

12:33So there's really endless number of use cases for the sensing part that you're talking about because it allows you to connect things in the physical real world into this machine learning world where you can then make assumptions and understand things and define actions. So then you've got sensing, thinking, and then the last one is to take action. Now in chat GPT, action is done by the human being and it's done using the most powerful part of chat gpt which is copy paste it's the number one interface for chat gpt copying paste out vice versa yeah but in the rest of the world there's quite a rich world of process automation physical automation in fact microsoft's inspire launches last week they've released a whole bunch of stuff around deep process mining using ai to develop and define new business processes so there's a whole bunch of interesting ai stuff in that space and then of course you get into robotics and drones and all that kind of stuff.

13:30So when people start to talk to us about AI, our first point is let's pick a place that would allow you to get innovation, to generate value that's going to be additive to your business and is low risk to your existing business. Because if you do an AI project and it fails, as with every change project, every naysayer in the world will say, I told you it was rubbish. And that's the lesson we've learned from 20 years of doing digital change, right? you want to get some wins under your belt. And just to put all that into concept as to how interesting alternative uses of AI could be, IKEA, the furniture manufacturer, have released and designed a set of drones, which they release into their warehouses overnight.

14:16They map the warehouse, they take off, and they do the stock count. Interesting. Now, interestingly enough, people don't like counting stock, and we're really bad at it. We just get confused and it's a job no one wants to do. So that for me is a really good example of a use case for a business, which is AI driven, but actually it's not leapt into chat GPT. It's trying to solve a problem that needed solving. Okay. So let me do a quick summary and then maybe we can dive into either additional use cases or how do I know which of the three to start with? Because you mentioned a few very important things.

14:54So first of all, you said you want the first thing you pick, let's call it your low-hanging fruit, something that will be additive that will actually add value to the business. And there's always two sides to that coin, right? It can solve a problem or it can increase a potential in both cases. It's additive to the business. It needs to be low risk because you don't want to bet the farm on something that is a new technology and you're not sure how to implement it. And you want to get quick wins. So you don't want to create a three-year project. You want to create a two-week month, two-month project that can show quick benefits and show a win because then A, you get the buy-in for anybody who need to get the buy-in, whether it's the board, the investors, employees, whoever needs to be in.

15:37And B, it can buy itself, then finance the next step because the company is now making more money and you can do more stuff. So I love all these points. I think they're incredibly valuable. How do I identify? So in my eyes, what you just said, me as a business owner, you just confused me a little more because so far I was thinking ChatGPT and I didn't know where to start. Now I got ChatGPT and I've got sensing and I've got actions that I can take across different aspects. How do I know to pick that stuff that is additive, low risk, quick win that I need to start with? The first part of that is just start with get some good advice.

16:20Okay. Because if you do them, if you do the maths in the UK, in the 2021 centers, there were 109 ,000 people recorded their job as systems architect, solution designer, or business analyst. Okay. There's 5.5 million businesses in the UK, 1.8 million of which have more than a thousand employees. even if there were 100 ,000 AI people, the chance of you recruiting or finding one is about 35 minutes is your share of the resource pool. They're just rare people. So the first thing you need to do is a strategy to either get a good person to help you or a strategy to grow some people in your organization that learn these tools, which is another reason why you need safe places for them to learn because you don't want them to become unsuccessful.

17:10So let's just drift into trying to find candidates of what AI is good at and what people are good at. Awesome. AI is good at a bunch of things. But what it's really good at, if you think about large language models as a use case, is dull things. Extraction and summary. Transformation. Translation. Interpretation. Sentiment setting. Prediction. Prediction. Stuff is just really basically what you would call the utility rule of computing. It's the dishwasher of your house. It's not exciting stuff. No one cares if the computer does it. The thing that ChartGTPT brings in, a lot of times it wasn't big in, they radically lower the cost of doing those tasks in an automated way.

18:02If you wanted to automate your email workflow before, you had to do data transformation. You had to do this, you had to do that. You had to do a process. I was all really quite hard. The language tools are really good at that general purpose language management, which historically IS systems have been poor at, which is why those problems have been left alone. So we like to say AI is good at joining dots. As a simple metaphor, it's good at joining dots. So the opposite of that is what are people good at? People are good at relationships. They're good at dealing with you and expected. They're good at dealing with the unknown.

18:41They're good at imagining a new way of doing things. So they're good at making new dots. So you've got this almost beautiful symbiosis where something that likes, likes is a wrong word, something that cares not how many times he does a mundane job, dots to dots is a thing you could have. People who like doing new, interesting work but not doing the mundane, now have a way to be creative and be expressive. So it's about bringing these two things together to make what we describe an augmented AI experience, where you're using these AI tools to essentially give your everyday work superpowers to your people.

19:24So you're going from trying to think about how can I get a productivity saving or how can I get a reduction in effort? So actually how can I get to a significant value gain for everybody? and this is where we now stray into the how do you do a business case to the cfo we have a fairly simple pitch process for that so before we dive into this i want to add one thing i just came back from a great conference it's called macon it's the marketing ai conference and it was in cleveland ohio and one of the speakers was the chief decision officer at google she's incredibly smart and yet has this amazing skill to take really complex stuff and turn it into words that normal people like you and me can understand.

20:09And she was talking about the whole idea behind AI and where is it going, is it differentiates between thinking and thunking, which thunking is a word she made up. But thinking is what humans are very good at, is taking something that did not exist before and thinking about it and making this new idea out of it. Thunking is the stuff that you do once you finish the thinking in order to make the thing actually happen, which a machine does a lot better. And it does it a lot better because it has access to a lot more data. It has a lot more memory than you and I have. And hence, it's very good at these repetitive data analytics kind of things.

21:01And so if you cut the line between thinking and thunking, which is basically what you're saying, and I think the biggest difference, that's not a new concept. I think the new concept is that we've pushed the line of thunking way further ahead than it was before because there were tasks, not that there were thinking tasks, It's just tasks that computers did not know how to do easily until a year ago. And even that's not totally true. It was available to Netflix and Google and Facebook. And these companies just wasn't available to us, the common people, in an amount of money that we could afford.

21:41And now it costs you either zero or 20 bucks a month, and you get access to an API that can do things that just were not doable earlier. So I love the way you framed it. And now let's dive into your framework on how to pick the business cases. So we always start, quite often that conversation starts with a CFO. Always someone who says, how do I persuade my CFO? Okay. I've been in business 20 years and I've said in board meeting after board meeting, where we've done the strategy and the finance review for the next year. And it always starts with something like this. The CEO says to the chief commercial officer, I need you to save half a percent margin.

22:18And then I'll say to the HR director, I need you to save a quarter percent margin. And I go around and you'll think if we get another 1.5 or 2 % margin in this company next year, that would be great for us. That's that straight bottom line profit. So it's all about margin. Okay. So I say, what if we think about your human resource spend, which is typically 70 % of your cost, excluding stock, excluding property. So of your manageable cost is 70%. What if you could make those all 10 % better? So that gives you a net 7 % gain in margin. Would you like that? I haven't yet found a CFO who doesn't want 7 % margin gain.

22:59Okay. Yeah. So I said, okay, let's use AI to get 7 % then. They're like, okay, that's a good idea. Okay. That's quite nice. The problem with that is there's no one to do AI in your organization. You can't do your plan. What if there were some projects that were on the to-do list, but had always been put off because they were a bit not a good return on investment. All those things like social responsibility, well-being, all of those soft process-y type things that just never happened. But actually, your shareholders are saying, what is your ESG agenda? What is your social responsibility? What is your...

23:38What if we used some of that 7 % saving to do those products? You say, well, that would be good. Actually, I've got better news for you. Actually, it's not 7%. It's probably nearly 20%. So you could have your 7 % for free, see for your shareholders, they're happy, you're happy. You could take the next 10 % and invest that into doing these long-term shareholder projects. You could use AI to drive some of those out. You can use them as a training route. And that generates you a set of people who are skilled in being able to do even more of this. And then I'll usually look at the HR director and say, just remind me again, motivated, well-trained staff, add what productivity to company?

24:20I think you'll find the number's 60%. So you're going to do these two things to get 20 % unlocked from your business. And you're going to end up with a set of people that have just been doing projects that they like doing, that were interesting, that retrained them to unlock a further 60 % at the end of the process. You're already at 100 % target, well, 80 % target. And actually 20 % is a low bar number. If you look at the stats from the employment plans for the US for the impact of AI, same in the UK, same for McKinsey, they're talking about 45 to 47 % revenue gain. So if you want to unlock and have a change program that's going to work, you've got to address the question, what do you want to do in the business?

25:06Get rate value. How are you going to do it? We're going to do a bunch of projects that are safe to work on, that are low risk, to retrain our staff because there's always going to be a staff shortage. We're going to end up with motivated staff. So you're looking to generate, how we phrase it, wouldn't it be good if AI generated value for everybody for good? Why wouldn't you do that? And usually, by now, the CFO and the board are like, this seems like a reasonable plan. Where do we really start now? We're bought into it. We're going to start. We need to start. We're both into the idea of the exact where.

25:41Yeah. And then the exact where we try and target a combination of impactful enough, safe enough, and enough risk that there's an impediment to do it. So it's got to solve a problem. It's got to solve an itch, but not crazy risk. Like, for example, if you were clinical practitioners, would you immediately let AI do all your diagnosis? No, that's just a bad idea. However, would you let it help fill in the 60 % of administrative time that once you've seen a patient, the clinical practitioner can't do because he's got 35 forms filling? That, to me, seems like a safer place to go than the other. And the same if you're a sales-oriented business with a call center.

26:29If you've got five-star customer service in your call center, leave it the heck alone. If you've got one-star customer service in your call center, then maybe it is a good idea because it's not going to make it any worse. So we try and sit down with the board and find these itches, these challenges, and say, okay, that feels like something you feel you want to do, something you feel will get value, and something that is within your board's level of risk appetite. And every company, every board's risk appetite is a little bit different. But broadly speaking, the companies that are implementing AI now want to be leaders as opposed to laggards.

27:07So they tend to have a little bit more appetite for risk at a board level than the others. And also my experience is they tend to have a little bit more appetite for risk than the CIO or the IT department or the business themselves actually realizes if you can express it and control it in a sensible way. I love this. I want to touch on two really interesting and important points that you touched. One is at the end of the day, decisions in a company are based on the financial outcome. Meaning, yes, it's very important to get the buy-in from the employees and train them and get them motivated and whatever.

27:43But at the end of the day, the CFO slash CEO will make the decision based on, is this going to make us more money or less money to ourselves and to the shareholders? And so the framework you shared is brilliant because I've never heard anybody talk about this. People talking about how to get employees trained and how to do this, but not about, okay, we can do all of that, but nobody will ever do this. if it doesn't really make sense from a financial perspective, because that's what a company does. The second thing that I really like is the idea behind the ethical aspect of this. And to be fair, I would assume, and I've seen this, so it's not just an assumption, let's call it an educated assumption, that this will cost some jobs.

28:26Meaning if you're saying, okay, I have X employees, 30 employees that are doing this task, and now I can do this task 30 % more efficient. You have two options. One option is to say, I'm going to let go of 30 % of the employees and get immediate savings to the bottom line. That's the less ethical and long-term, I think, less profitable option. The other option is to look at other parts of the organization which are connected to that 30 % and saying, can we push them 30 % that now we can sell 30 % more or 50 % more because of these capabilities with the same resources that we have today. And now everybody wins because now you have, like you said, happier employees, they're doing stuff they care about.