A better way to make 35,000 decisions a day

Transcript

Hey, everyone. Good to see you. Like Holly said, we’re gonna have a fun day, I think. Possibly the best venue yet for AltitudeX.

So thank you for coming and making the day what it is. Alright. So today’s talk my talk today is on, as it says there or there, how to make a better way to make your thirty five thousand decisions a day.

Apparently, according to research, we do make thirty five thousand decisions a day. The vast majority of which we act we do sub quite subconsciously. Right? We don’t actually actively make them.

So ninety five percent of those decisions just happen, the rest, the the five percent, the seventeen hundred and fifty, we have to kind of think about a little bit. Okay? And, and my talk today is really, exploring how we do that, but then trying to relate it to business because today’s, sessions are all about business performance and our use of technology in business, especially artificial intelligence. So how can we, do that every single day as business leaders going about our day to day jobs?

Alright. So first, we probably need a framework for how do we make those decisions in the first place. Okay. So, this is a little logic model of how we maybe or maybe don’t make our decisions, but there’s lots of different things that go into decisions.

Sometimes we make them based on instincts. Sometimes we make them based on emotion. Sometimes it’s data, and probably in Peaks case more often than other places, it’s about data. And then there’s logic too, of course, and then sometimes we bypass all of that and we just use our intuition.

And actually, the vast majority of our decisions that we do make are based on instinct, emotion, intuition. That’s what most business leaders will say they use to make decisions, as opposed to data and logic, often because we don’t have the data, or, because we don’t have time to really get into a lot of decisions, so we just have to let them happen based on our experience.

Okay?

But what does that then mean in our professional lives? And this is a topic that’s kinda interesting to me.

I I believe that the performance of a business leader, whether you’re running a team, running a department, running a division, or running a company, equates to your company output or your team output. So if you’re a CEO, ultimately, your performance is your business’s output. If you’re running a team, that output is your performance. The two things are directly correlated.

And the performance of a of a company, of a team, of a business is actually the sum total of all of the decisions that you make. Okay? So they could be like the decisions you make in, like, every day, operationally, but they could also be decisions that you made years ago, strategic ones that are playing out today. Okay? But the sum total of all of those things together is the company performance or the output of the performance, of the business, ultimately, which means, if you follow that logic, that as business leaders, we’re responsible for the efficacy of all of those decisions, actually. That’s our responsibility, because if we’re responsible for performance, and that is our job, and the output really is the sum total of all of those decisions, we’re therefore responsible for the efficacy of all of them, which is a problem.

It’s a problem because we’re often a long way from the decisions that are being made, and we’re not really in control. Or should we even be in control? That’s a different topic.

But, as a business leader, we can be a long way from those decisions, and that’s why we rely on intuition a lot, and it’s also why we value experience a lot. So if you look at any job advert for a business leadership position, we’ll say at least X number of years experience in this kind of role. OK? We place we place value on experience because experience helps us make better decisions based on intuition. We can allow things to run, we can allow our teams to operate without having to get into every single decision, and we instinctively know where the problems are, and we can address them before they become major issues.

However, a question that’s worth asking, I think, in this topic is, does experience equal decision accuracy?

My view on this would be no. I would say they’re correlated. They should be correlated, right? Like, if you had a toddler trying to run a business, they’d struggle.

So the more experience we have, the better we’re going to be at making decisions in the context of what we’re doing. So we do get better with experience. Some people probably get better quicker, and some people might get better slower. So there’s a correlation, but they’re probably not directly, related.

Another interesting one, and I guess the sort of cornerstone of this, of this talk is, does decision accuracy equal the output of our businesses? Because if the performance of our business is the sum total of all of our decisions, logically, you would expect me to say, well, therefore, decision accuracy equals business performance.

Does it? Well, I don’t think it does, actually, weirdly.

I think there’s something missing. There’s an equation here. It’s part of an equation.

Decision accuracy does equal output, but there’s something in between. What is that something in between?

And my view is that that is resource resource performance, I call it resource performance, which is basically the resources we have at play in our business and how they’re performing. Because if you think about it, decision accuracy multiplied by zero resources, as a, I don’t know, as a tech startup when we started peak writing a business plan with with on my own or me and Dave chatting about it, there was no resources at play. So, you know, they they could have been great decisions, but there’s no output. Nothing’s happening.

Okay? So there’s no performance there as a company. There has to be something in between. That’s your resources.

But it’s not just the resources. You could have loads of resources, and they could be doing nothing. So there has to be a performance element too.

So this is how I look at it. And this framework, I think, is useful for thinking about how we get the most out of, our businesses, and, and how we perform as best we can as leaders. Resource performance to me is two things. So it’s all of our resources added up.

So it’s a there’s a sum of resources here multiplied by an activity rate, like what are we actually doing. So let’s explore that. There’s two types of resources in companies. There’s fixed resources, a bit like a balance sheet, fixed assets, and variable assets.

So there’s fixed resources that they would be like your product, your IP, your your factories, your plants, your infrastructure, and so on, the technology that underpins the business. Even your brand is a fixed resource.

And then your variable resources would be people, cash, inventories, and processes. Okay? Things like that. Things that move more frequently. And then there’s an activity rate. Your activity rate is how fast you’re doing things and how often you’re doing things. Right?

So decision accuracy multiplied by performance, and the resource performance equals output in my view. Okay. But if, if you think back to how I started the talk, that would imply that the only decisions that matter are the people in charge, like the bosses, or the bosses of a team, or the CEO. And that and that isn’t true.

It’s gotta be everybody’s decisions, because we all make decisions that add up to the company performance.

So, so that would be strategic decisions. So they can be like farsighted investment. We open a new factory. We start operating in a new geography.

Things like that. They’re big, like, strategic decisions. There’s operational decisions, and there’s process decisions, things that are happening, like, regularly or even autonomously, they’re being set up to run. Okay?

The sum total of all of the the performance of those decisions multiplied by the resource performance, so all of my fixed and variable resources effectively, and the activity rate equals output. Okay. So it starts to get a bit complicated if we think about about it like that. So there’s a more simple way to describe it, I think, which is basically the performance of our businesses, if we want to maximize the performance of our business, we need to make the best decisions possible with the optimal resource mix, okay, as much as we can, as often as possible.

So those are the kind of different levers we have to pull in order to maximize the performance of our businesses.

So with that in mind, like, I guess one of the key themes of today is artificial intelligence. How, like, what how does that how does that relate to AI? And how should we think about AI in that context? And I think there’s a really, really simple way of thinking about AI in the context of our business performance.

And you can just ask this question, will this thing, this AI project, this idea, this technology improve output? It’s a it’s a really it’s just a simple question to ask yourselves. As business leaders, you’re gonna get ideas thrown at you all the time now, hopefully, by your teams. We could do this, we could do this, we could do this, and you’ll have some ideas.

And the and the way to think about it, your own framework can just be, is it gonna improve the output of my company?

That means, if you think back to the equation, you could be doing one of three things. You could be improving decision accuracy, and AI is great for that, actually.

You could be improving the resource allocation or mix that we have at play. So what resources do we have operating for us in our business? Or we could be at improving the activity rate, because through AI and automation, we could be doing more things faster. Any of those three things can be impacted by AI, and picking the right ones to improve, improve the business is really just about improving the output.

Okay? So you guys know, obviously, that Peak is an AI company, but there’s lots of different flavors of AI AI technologies. We certainly don’t do it all. We do a little bit.

But relating it to what Peak does, here’s some practical examples. Okay? So this is just a screen grab from one of Peak’s applications. This particular application helps us optimize our pricing decisions as retailers and merchandisers.

So this application will set pricing when you’re going into a markdown period or a sale period or something like that. Okay. And the reason I put this slide up is just is that this this this goes after the first of those three things. This is improving decision accuracy.

Okay. Because what we’re able to do with this application is say, right, I’m going into this sale period. I have some goals I want to achieve. I want to achieve a certain sell through rate.

I want to achieve a certain margin. I want to achieve a certain, a a certain revenue target, perhaps. Okay? And the AI will simulate based on forecasted demand, anticipated price elasticities, all of those kinds of things.

It will simulate the different ways you can achieve different, different goals as part of that sale period. And then you can that little tiny blue dot there is you picking a point and saying, okay, as a business leader, I wanna do that thing. I’m gonna do that thing. And then you can let the AI go and price the products and carry out, the activity around that sale period.

And what that’s allowing you to do, if you think about it as a business leader, is be much more in control of the output of the company, because we’re improving decision accuracy. And we feel it’s not just that we feel more in control, it’s that we are, because we’re going from a world where we’re using our intuition, how our products are gonna perform, how the sell through rate will be affected by a markdown or a particular product, and what that’s gonna mean for our margins, and so on and so forth. You’re able to simulate it all and then precisely say that thing. I wanna do that thing.

And that’s just improving the accuracy and therefore helping you be more in control and boosting the output.

Alright?

The second is automation.

So one of them was one of those points was, well, how could we boost the activity rate? Okay.

This little screenshot here, is a little bit complicated, but it’s a visual representation of the decision flow that sits behind the application I just showed you. So what it’s doing is it’s it’s it’s aggregating data. It’s bringing in data. It’s it’s aggregating data. It’s pushing that data through different machine learning models. It’s combining those outputs of those machine learning models with business rules, logic, different, guardrails for how a company will and won’t make decisions, and then it’s spitting out the optimal optimal pricing for a particular goal. Okay?

Now if we were trying to do that manually, that would be quite difficult, and it would take a long time. Even if we tried to do it manually supported by technology other than calculators or spreadsheets, it would take a long time. But with, AI systems like this, we can automate those flows, and we can get from the start to the end of that decision flow in a minute, for example. So that is an example of automation in decision making speeding things up.

The out what the result of that is, we can make more decisions. And often, one of the biggest, like, constraints on performance we have as businesses is time and our ability to make decisions. So we’ll often focus on maybe as a retailer, we might focus on our top and bottom selling products. Let’s deal with the stars and let’s deal with the problem children and everything else we just let run because we don’t have enough hours in the week to get through everything, and that’s the same for any kind of business.

We focus on the edges, and we don’t focus on the middle because we run out of time. But if we can automate getting from the, you know, the input to the output of those decisions, then we can make more decisions. And if we can automate some of those decisions, we can make even more decisions. And we’re not just making them periodically.

We can make them all the time. So if you think back to the equation, again, that’s boosting the activity rate. So therefore, the output increases.

The decision actually doesn’t have to be better than that we would have made manually. It’s just the fact that we’re making it quicker and more of them is the is is one of the main benefits there. And then finally, this is a little video playing of one of, one of our new products. We launched Co:Driver last year at this conference, which is our generative AI, assistant, effectively, thinking about that, our AI agent that works on the Peak platform.

What it’s doing in this video is it’s being asked by somebody, maybe me, which products which products in my range do I need to reorder, and it’s going away and it’s understanding, it’s understanding all the predictive data and all the other data that’s under the hood of the peak platform, and it’s returning a result saying you need to reorder these things. Okay? And what that’s actually doing, the reason I’m showing you that, is that’s combining some of the accuracy of the, of those of, say, the first example with the automation of the second to speed the whole thing up. Okay?

So then, again, that is putting both accuracy and speed and automation in the hands of business users, so that we can make more decisions quicker. And then what Co:Driver, will then be able to do is go away and carry out those tasks for you as well, so you have this kind of, co driver working alongside you in your business. So all of those three examples really apply to that framework, which is, boosting output by increasing the activity rate or improving the resource allocation or mix because it might be adjusting inventories, it might be adjusting prices, things like that, or improving decision accuracy, and all of those things input to improve the output.

Okay?

So I thought that was a a sort of a useful framework as you approach the rest of today is to think about, like, okay, my job as a business leader is that, How do I maximize my own performance? How do I maximize the performance of my company or my team? And then how can technology help?

Just, I think, keeping that in your mind, even if you’re not thinking about AI, by the way, is a really useful framework for how can we boost the output of our businesses, because this doesn’t have to be this doesn’t have to be AI or tech. It can just be we’re doing our jobs better. Okay? So it’s quite a useful framework for that anyway, but it’s really useful for assessing the impacts potentially.

So, some other thoughts for today. That that is the end, of this talk, hopefully, interesting to you guys. But some extra thoughts for today, which are going around our minds at peak at the moment, and I think it’d be really useful, to to think about as you talk to others here today and, and and, like, relate relate them to the to the other talks, and even maybe some of the demonstrations of the technology at the back. One thing that I find really interesting is, with AI has been seen as a productivity booster, but it seems that not that widely adopted in productivity, by teams yet.

So you could think have a think about that. What could I do to improve my own personal output? If you’re thinking of that equation just relating to you, can you improve your own output using AI? What kind of tools could you use?

Could be scheduling, emails, writing assistance, things like that. I I find them really useful.

I’m sure many could, and there’s some good, there’s some good examples at the back there, particularly on the Gen AI stand.

Another topic that I would love to chat to anyone about, I I don’t have a strong opinion on this, but I have a hunch. My intuition tells me that businesses will have to adapt in structure and form in the AI era. Okay? Because we organize ourselves around we organize ourselves around processes in businesses today, and we, divide up the processes pretty much by the breadth of our own, cognitive function and the size of our teams, and the size of the tasks.

And there’s loads of different handoffs and pro and like different ways of organizing ourselves in companies today, but it tends to be top down process driven. It might actually be able to be reoriented if we can automate end to end decisions. Will we form our teams differently? Will there be different roles, and so on and so forth?

I think that’s a really interesting one. I don’t think it will happen overnight, but I do think in five or ten years’ time, company structures will be completely different. And I and I also think that you will have different, like, executive roles, in the c suite because of the importance of data and AI in running those businesses, which is a really interesting topic. So I’d love to chat to anyone about that.

So hold that one in your thoughts. And then finally, do established businesses have an advantage or disadvantage, in the AI era? And my view on that is I think they have some advantages and some disadvantages, but I’d love to hear other people’s opinions. The main advantage is data.

The AI doesn’t work without data, so established businesses have way more data, have been around for way longer. They might not have the data well organized and looked after. That’s another point. But, like, there is an inherent advantage as being established today, because it’s harder to disrupt you if you can use that data as a a moat, around your business.

But there are other disadvantages, which is old systems, legacy ways of doing things, old ways of thinking.

So some pros and some cons, but I would love to hear what everyone else is thinking about that as well, through the day. So, yeah, that is it. Thank you for your time. Thanks again for coming. I’m gonna hand back to Holly, and I look forward to chatting a bit with all of you, during the rest of the day.