Transcript
Transcript: Trends in Technology-Driven Change: Transforming Operations Through AI
[00:00:00 Text appears onscreen that reads "Trends In Technology-Driven Change".]
[00:00:06 The screen fades to Chris Howard.]
Chris Howard: Hi, I'm Chris Howard. I'm the Global Chief of Research at Gartner. Thanks for taking some time to listen to the advice that we have around A.I. and related subjects. I hope you find it interesting.
[00:00:17 Text appears onscreen that reads "Transforming Operations through A.I.".]
It's a multi-executive strategy. How many of you here consider yourselves to be more sort of technically-oriented, technology-oriented? A handful, right? But the rest of you are also very interested and you would be involved in sort of understanding the answer to the last question I asked, where is it important enough for us to actually build our own insights because of what we have? That's going to require a pull from you.
[00:00:42 A slide is shown with a graph titled "Generative A.I. Utilization".]
And so, one of the other surveys that we did at the end of last year was to say, where is the investment coming from? And so, what we did is we interviewed all types of leaders in enterprises except for technology leaders to see, is it different? And what you see here is that 80% of them plan to implement or continue to implement over the next year, and we're already four months into that, right?
So, a lot of the money and pull is coming from the non-technical side of the house, which for those of us that have been technologists is kind of an odd place to be, right? Because you have demand for a technical solution coming from non-technical people because they see the value, that usually it's the other way around, right? As technologists, we say, there's this thing we could do and it could be really valuable, how do you want to use it? And you have no idea. You have no idea. It's like walking into an auto parts store and saying, okay, go build a car, all the parts are there, why can't you see what the value of that is? It's not so easy. But in this room, you're responsible for putting data to work for the citizens of Canada, that's what you do, and what is it about their data and where would insights into that data create a better life for the citizen? I mean, that's the types of questions. That's really what this page reflects, is to say we think, in our function, we have data locked down to a point where if we could open that up using some of these tools, it would give us a different kind of benefit. That's really the story of this page.
[00:02:02 A slide is shown text that reads:
- 6.5% is the average percentage of functional building being dedicated to generative A.I. for the 79% of functional leaders implementing generative A.I.
- 3.8% of headcount in 2024, 6.1% by 2025, and 8.2% by 2026 are the projected headcount reductions by functional executives implementing generative A.I.
- 51% of functional executives report that in the coming year, they will also add generative A.I. staff and experts to their budgets.
- 76% of employees would prefer to use A.I. tools at work, whether those tools are already present (for 46% of employees) or not yet readily available for use (for 30% of employees).]
Types of investments here, so we're seeing money moving towards it. So, part of that $3 trillion is spent within an enterprise and within the government as well. And so, you see money moving but there's some expectations here, right? So, that 3.8 number, 3.8% number, is expected reduction of headcount, and this is very attractive but seldom achieved. I'll just put it that way. There's a lot of promise around productivity for A.I. that we're not sure is true yet. Some places, it is. So, any place where you have a call centre agent that's dealing with a human and has a screen full of information as they're doing it, it turns out that productivity gains in that environment are pretty high, but you think about, there used to be a call centre agent who's used to that environment where there's a screen and they're getting information and they're helping in the midst of that information.
But the other thing that's true about most customer service lines is that they're highly measured. So, you know if you add something new into that, whether it improved it or not, it's much harder in other parts of the organization to know whether those productivity gains were achieved or not, but the hope is there. There's some concern about staff here. Do I have the people that understand how to do this? And this question will continue. What you don't need are sort of deep A.I. engineers to implement this stuff. The most important people you can hire or train within your existing staff are people that know how to ask good questions, because the prompt, the thing that goes into the data to bring a response back, is really a great question, and the better that question is, the better the result that comes back. The better the context you wrap around it, the better that comes back. And so, it's really training people to understand how to ask great questions and then tune them and tweak them and then learn from that.
The 76% number, I think, is really important for you as you think about sort of the wave of baby boomer retirements happening and attraction to new workers. They want these types of tools in the work environment or they will leave. I mean, the connection between really good digital design and employee value proposition and lack of attrition or reduction of attrition was very, very obvious. And so, you've lived this in different places as well. We have these great tools that live outside of work that you use every day that you can't use at work, and it creates a frustration and a tension, and it's like, well, why can't we do this? Interestingly, that extends to the pre-hire. So, if a recruiting experience in digital tooling is bad, they will abandon those applications. The rate is extremely high, like 76% of people applying will actually not go through with the application if it's a crappy experience, so even before they hit the environment. So, there's pressure, pressure from outside.
Okay, the other thing that's happening is the appearance of senior A.I. leaders. So, these are very senior people that are responsible for the implementation of A.I. in a particular organization. It's happening across multiple sectors. The really obvious example of that was the executive order in the United States where every agency has to have a head of A.I., so suddenly, 300 or more new positions who have heads of A.I. that are responsible for figuring out and figuring out how to apply it to create value. I don't know whether that's the same here. What I know is the 2.8 billion number that came out of the federal budget proposal, but that's not just for spending on government positions. That's out into investment in other places. It'd be great if it was, right? Wouldn't it be great? And so, there it is, right? Yeah, the securing Canada's A.I. advantage and then the advancing governance, innovation, and risk, that's the executive order from the U.S., so the need for senior leaders. Be thinking about people that you would put into a position like that. They have sort of the characteristics that we're talking about.
[00:05:51 A slide is shown with a diagram titled "The GenAI Opportunity Radar".]
Okay, one of the ways that we talk about this here, and we do a workshop on this with clients is the thing about what your ambition is. So, the bottom of this radar is internal operations. The top is external citizen-facing. Left is every day, so the stuff you're already doing, you simply make it better. Game-changing is on the right-hand side. It's easy to come up with stuff that fits onto the left-hand side of this, especially the lower left. It's very hard to think about the stuff that fits up in the upper right. In your case, it in some cases means parliamentary change, to actually implement something completely different, but let me give you a few examples. So, post-call summary, you have a visit with somebody, you want a summary of that, really easy use case. The customer service chat bot I was just talking about that sits alongside the agent, common case but it's facing the customer. Topology optimization, I'll talk about here in a minute. This is hardware design, so the design of aircraft parts, those types of things, so optimizing that. And then, the computable policy that I was mentioning around AXA. That's really game-changing because it actually creates a new product for them, a new set of… a new revenue series stream as well.
Here's a view of government. This is specifically around human services. And so, you see there's nothing in the top right, or the bottom right actually. It's all left-hand side which is improving operations of what you do today, which is fine, right? The advantage of that is that there's a lot of stuff to fix or to apply it to, but the other thing is that it gains people's trust. So, if you're able to implement something that shows success, for example in case management, like multi-lingual case management, the Canadian government is not completely unique but the scale of the language challenge is real for you. If you could assist with multi-lingual case management using A.I., people would say, well, do more of that, that's great. I mean, it gives you trust in the fact that this is a technology that can be used to improve stuff, and you see a handful of other things in here, and we don't have time to go through this in detail but it is something I want you to think about. So, what crosses that line? I want more dots on that right-hand side of this radar.
Thanks for watching. And again, I hope you found this useful and interesting for the work that you're doing in Canada.
[00:08:08 The CSPS logo appears onscreen.]
[00:08:15 The Government of Canada logo appears onscreen.]
