- 2 years ago
Paul Daugherty, Chief Technology and Innovation Officer, Accenture Moderator: Alan Murray, Chief Executive Officer, FORTUNE
Category
🤖
TechTranscript
00:00 So an ongoing conversation that we're having at this year's event is of course what AI means for the workforce
00:07 and how we can train employees to work alongside artificial intelligence in a way that also improves their own experience of the workplace.
00:16 We're going to be discussing this in a second with our next speaker, Paul Doherty, who is the Chief Technology and Innovation Officer at Accenture.
00:25 And he'll be interviewed by Fortune CEO Alan Murray. But before we hear from those two, please watch this video from our founding partner, Accenture.
00:36 Reinvention. It's how retailers can use the power of AI for the benefit of all.
00:41 Personalizing consumer experiences without bias and giving employees new capabilities to supercharge what they do.
00:47 Where could reinvention take your business? Accenture. Let there be change.
00:52 Paul, thank you for being here and thanks for supporting this event. I think it's been, everybody here has been really engaged in the conversation.
01:01 Yeah, great event, a great buzz, a lot of great dialogue going on.
01:05 Yeah, so thanks for being part of it. Look, we've heard a lot of examples over the last two days of companies that are using AI and generative AI to make extraordinary productivity gains.
01:20 But they're anecdotal. You deal with, Accenture deals with more customers than anybody on this stuff.
01:25 You have thousands of customers that you're working with. Where are you seeing the biggest gains? Is this really revolutionary or is it more hype than reality?
01:37 No, we believe it's clearly more reality than hype. But of course, there's always a lot of hype with any new technology.
01:43 And I think the exciting thing and the thing that creates this hype perception around generative AI is the fact that it's so human-like.
01:52 It's the first technology where it's like looking in the mirror. No other technology has really been like that.
01:58 And I think the technology really is going to continue to become more human-like as you look at what becomes beyond generative AI as well.
02:05 And I think that creates a lot of misperceptions around individuals and work and how work happens.
02:10 And one thing I've been saying is we might be well served by taking the word artificial out of AI and just focusing on intelligence.
02:16 Because at the end of the day, that's what we're really trying to create is a greater intelligence by combining the human and the machine capability.
02:24 And as you said, we see a lot across a lot of industries around the world. We've had thousands of conversations with clients on generative AI.
02:32 You can't have a conversation that's not about generative AI anymore. And we've done hundreds now of projects with clients on generative AI.
02:38 And there's three things that we believe based on that work we're doing.
02:42 The first is that this will lead to the reinvention of companies. You saw a little bit about that in the video.
02:49 But it's things like a telco where we're working on a customer care example, a customer care implementation.
02:56 Generative AI in the call center in this case, 30 percent increase in productivity and 65 percent better experience as measured by the customer.
03:06 Wow.
03:07 In terms of what they get back. In drug discovery, looking at dramatically impacting timelines.
03:12 I think that there's been some other sessions here talking about that.
03:15 In places like energy, looking at how you can take tremendous cost out of the industry, overruns on capital projects by bringing in new forms of generative AI that can do things you couldn't do previously.
03:26 So that we see this reinvention of companies. We believe that will redefine leaders.
03:29 As we look at the Fortune 500 as one benchmark going forward, more change going forward than even the tremendous change looking to the past.
03:37 Because we're seeing a big gap between how companies are pursuing it. That's going to lead to disproportionate performance.
03:45 And then the third takeaway is the fact we really believe this is about human potential.
03:51 And human potential both on the productivity side and the creative side.
03:54 And those are the three key things that we're seeing in the early days.
03:59 When you talk about, you mentioned the Telco example with 30 percent improvement in productivity.
04:05 When you talk about productivity numbers like that, inevitably that raises the question of does that just mean that you're going to have massive layoffs in the customer service area or in the other areas where this is being implemented?
04:19 What are you seeing in terms of, is there reason to be worried about lack of jobs?
04:25 I think the biggest worry is the jobs for the people who won't be using generative AI.
04:32 I think that's the big transition that we're in. And for our company, the way we look at it is we don't really need less people.
04:39 We need more people who can work with generative AI in different ways.
04:42 This impacts our company as much as any company when you look at software engineering and coding, the things that we're doing.
04:47 And that's the same discussion we have even with this particular company I was talking about.
04:50 This wasn't about workforce reduction. It was better outcomes and more growth and more productivity and impact that they could get to their workforce.
04:57 Now, there will be some consolidation. Productivity means you need less people to do the same things.
05:02 The question is people want to do more things as well. And that's where we'll continue to see a lot of focus.
05:07 And the big, you know, the things I'd say you need to focus on are three things.
05:11 You need to think about work and how work changes, which is, again, thinking about intelligence rather than artificial versus human intelligence and how it changes it.
05:18 You need to think about that and that really dramatically reimagining the work and the way it happens rather than just applying technology into how work happens today.
05:28 The second is the workforce, which is what you're getting at. Where do I need more people? Where do I need less people? What skills do I need?
05:34 We have something called our R12, our required 12, which are the 12 things we need at scale to continue to drive generative AI in our business.
05:41 And I suggest every company develop the same.
05:44 What does that look like, R12? These are the skills you need.
05:48 There's specific skills in a variety of different skills across the 12.
05:52 One of it is around the understanding the change skills that you need to drive processes across human machine.
05:59 Another is prompt engineering, which is probably a basic one.
06:02 But there's this set of basic skills.
06:04 But talk a little more about that. I think this phrase prompt engineering is kind of an interesting one.
06:08 I mean, three or four years ago, what everybody was saying was, oh, you know, you've got to go to coding school.
06:12 You've got to have technical skills. If you don't have technical skills, you're not going to be employed.
06:16 Now we've had so many conversations here over the last two days about human in the loop.
06:21 You talk about prompt engineering, people who understand how to use the tools smartly,
06:27 who have the judgment to be able to manage the guardrails when the technology goes astray.
06:35 How do you train people for that?
06:37 You need to do it on multiple levels.
06:40 So the training, there's a level of cultural training that you need to do that we believe touches everyone.
06:45 We have something called TQ, technology quotient, which is for all of our 700,000 people at Accenture.
06:50 And AI has been a part of that. So all of our people have been trained in AI.
06:53 Now they're all learning about generative AI, no matter what their role is in the company.
06:56 Because everybody needs to understand it to some extent, because they need to think about and understand how it's impacting our jobs
07:01 and help shape how it's going to impact their jobs.
07:03 Then there's breaking it down into the specific skills you need in different professions.
07:08 You mentioned coders. So how's the job of a coder changing?
07:11 Again, we believe we need more, not less, going forward.
07:15 But you need them to be doing different things.
07:18 We talk about shifting left and doing different things in the software development lifecycle.
07:22 Similarly, when you look at legal professionals or paralegals versus lawyers, you look at the call center,
07:27 you look at plant engineers in refineries, it's about looking at that work change
07:32 and then modeling what are the new skills that are required.
07:36 So that's what we're doing in detail for every job category across every industry,
07:40 and kind of looking at what that change in profile looks like and how do you prepare people.
07:44 So what's your advice? I want to open it up, by the way, to questions.
07:48 If you have any, actually raise your hand now so we can get the microphone to you.
07:52 There's a question over here. But what's your, while we're setting up for that, what's your advice?
07:57 This is a big journey. You're talking about reinvention.
08:02 Companies are saying, OK, how do I get ready for this? How do I get my workforce ready for this?
08:07 What is your advice to companies that are beginning this journey?
08:11 The first piece of advice I'd start with is invest more in the people than the technology.
08:16 Those that really focus on the people are going to be the ones that are successful.
08:19 And we have the underpinning of what I just was talking about with these roles across industries
08:25 is that we've done a comprehensive research that shows that 44 percent of working hours,
08:30 44 percent of working hours across industries will be impacted by generative AI.
08:35 It's up to, it's close to 80 percent in banking. It's less than some industries.
08:40 And you need to prepare for that future. And there's five steps, you know,
08:44 that we think you need to take to prepare for that future.
08:46 They're not all around workforce, but this is the key to driving success.
08:49 And the first is having a value led approach.
08:51 That sounds like a consultancy thing to say and sounds obvious, but not many companies are doing that.
08:57 By value led, we mean looking at across your whole value chain,
09:01 at everywhere where you need to apply generative AI and building that in a systematic manner.
09:06 And with the business case in mind, rather than just doing experimentation on use case
09:10 and shifting from this use case mindset to value chain is really a big change we need to see in 2024
09:16 as companies shift out of the use case mode of 2023.
09:19 The second thing is build a gen AI ready digital core.
09:23 We believe generative AI requires a fundamentally different and new enterprise architecture.
09:27 There's a new intelligence layer. There's a new gen AI backbone that you need
09:31 that can do things like isolate you from changes and isolate you from training differences across models
09:36 so you can get the results you need in the business and you need to invest in that.
09:39 The third is this change around talent and workforce and developing your change competency
09:43 and the learning capabilities around that.
09:45 The fourth is responsible AI done intentionally because there's a big gap there as well.
09:51 95% of executives say they believe in responsible AI and they're going to manage the risks.
09:56 95%, 6% have responsible AI programs in place in the company.
10:00 So big gap between intent and execution.
10:03 And then the fifth point is it's a continuous reinvention.
10:06 I think there's a lot of people that think this is a find my big use case and go and it's a big shot success.
10:11 We believe this is a multi-year reinvention.
10:13 You need to again look across the value chain, set that architecture carefully, prepare your workforce.
10:18 And it's a multi-year change program.
10:20 Wow, that's good advice. Thank you for those five. Question right over here.
10:23 Good morning. My name is Raquel Rodriguez.
10:25 I'm associate general counsel at the AES Corporation, a renewables energy company.
10:30 Something that we're struggling with is the cultural integration of AI at our company.
10:36 People are afraid, people are resistant.
10:38 They want to keep doing their work the same way they've always done it.
10:43 So what have you seen works in terms of motivating and encouraging people to adopt AI?
10:50 I think there's a gap a lot, but there's a gap also I think between in companies of understanding.
10:57 I think the main thing I'd encourage is back to what I said earlier around the education and cultural sharing and learning across the organization.
11:05 There's some research we're going to release shortly that's where we asked executives about the disruption they expect to their workforce.
11:11 And 80% said there's not going to be a lot of disruption.
11:14 There's not going to be a lot of reduction in their workforce.
11:16 There's going to be a lot of change in learning and education.
11:18 But the workers don't believe it. They're afraid.
11:21 So 50% of workers are really afraid for their jobs and what this is going to mean.
11:24 So there's a disconnect between the executive mindset and intent and the employees.
11:29 And I think that's where the training and understanding and learning and sharing the mindset of where you're going is really important to bring people along.
11:36 Because there is no AI ready workforce you can hire for a year from now or two years from now or three years from now.
11:42 If you need a gen AI enabled plant manager for your refinery, you're not going to be able to hire that.
11:47 You need to bring your workforce with you and develop it, which is why this is.
11:50 I can't remember who said it yesterday. Someone on the stage yesterday talked about, you know, have celebrated successes, get a team, have a success, then show them to the rest of the organization so people can be less afraid, more excited about the device.
12:03 But just if I could add one other point, I think the other thing is thinking is educating the educating people where the gains from an individual perspective are going to be.
12:12 There's productivity and doing work more effectively, which is which is good.
12:15 There's creativity, people producing different results.
12:18 We see this in our when we're starting to apply it, our sales professionals where they're they're getting different results and coming up with different shapes of things that they would have otherwise.
12:27 Then there's outcomes that they produce. People produce. They can get more output, do more generative design in product design, as example that.
12:33 And then the final one, which is a surprising one, is people like their work better when they're using generative AI.
12:38 We're finding this in a number of scenarios where you're taking some of the drudgery and tension out of their work.
12:43 And one of the things we're finding in a lot of use cases, people just like what they do.
12:45 It's a great point. We have two questions over here. Why don't we take both questions and then let Paul respond to both.
12:50 OK. Hey, Paul, Vijay from UiPath. On the telco example that you spoke about, the productivity lift.
12:58 Did you advise the telco on which conversational AI product to use? Did the client of the telco know which product to use?
13:08 How did how does the decision get formed? And you are the trusted advisor.
13:12 How does that decision get formed? How much does the client know about Gen AI and which product to get?
13:19 And how do you shape that decision?
13:22 A really important question. And part of that, those five things I talked about, what is in that digital core is needing to get into those decisions.
13:32 I believe the foundation models you select in the architecture you build is strategic and will dictate whether you're going to be successful.
13:39 So in that case in particular, some of their existing products they had led to a pretty clear decision on which products were the right ones to build around in that case.
13:47 But it's not always so obvious. And you have to decide, do you use something from your company, the products that you have, as an example?
13:54 Do you use something from your enterprise software companies, SAP or Oracle or Salesforce, who are bringing out their own capabilities?
14:02 Or do you use a model and build your own pre-trained or customized or prompt engineer it?
14:08 And in that case, is it going to be open source or proprietary? A lot of decisions to be made.
14:12 And we believe these are senior level strategic decisions that people need to be educated on, because the wrong choices today will limit you going forward.
14:20 And what we're seeing is similar to the early days of cloud is proliferation, where companies are going down a path that's going to lead to proliferation of different models for different purposes,
14:28 connected to different data, producing inconsistent results all over the organization, leading to the need for both dissatisfaction and disenchantment with generative AI,
14:37 but also a lot of cleanup work to be done. And you can head that off by taking the right approach.
14:41 Paul, a lot of really great advice crammed in a very short period of time. Thank you so much for being with us and sharing those insights.
14:48 Thanks, Alan. Thanks to all of you.
14:50 [APPLAUSE]
14:52 [BLANK_AUDIO]
Recommended
0:32
|
Up next
1:31
1:31
1:07
1:31
1:31
1:07
0:46
Be the first to comment