Gwion Kennard: Well I'm Gwion Kennard I'm the digital director for infrastructure and engineering.
Philippa Lamb: Gwion is with Atkins the global engineering company.
GK: I've been a director within Atkins for the past five years leading the digital transformation within a small team of around 250 within networks and drainage.
PL: Digital transformation, it’s a phrase we’re hearing a lot and the definition keeps getting broader. Now part of Gwion’s job is to explore automation, robotics and mechanisation at Atkins and as he told me the opportunities to mechanise tasks that were traditionally done by humans are many and varied.
GK: Oh they’re vast, absolutely, and it permeates everything that we do, all of the human interactions and interfaces but actually there's a lot of tasks that we have now started to automate and build that into daily routines.
PL: What sort of things?
GK: So we were doing some analysis on an airport site where we were looking at a range of different scenarios of different aircraft movement and the fire risk associated with the loading and with the human movement around that site and traditionally we were doing that, writing a report about it, looking at three or four scenarios, doing some modelling associated with that, having some conversations with the operations team, pulling some data back associated with that and then writing a report feeding that back. And that was taking around a three to four week iteration.
PL: And now?
GK: Well and now, well because we’re able to pull the data on the fly and we can run the simulations in the cloud automatically, and we can print the results back into the client database automatically, something that was taking three weeks now takes three hours.
PL: Wow! So AI and tech are already having a big impact on our working lives and their economic impact is on the rise too. Professional services giant PWC predicts that AI alone could boost UK GDP by 10% by 2030. Now that growth may generate more jobs but AI and tech could also replace jobs that exist now.
Jonny Gifford: My name’s Jonny Gifford, I am the senior adviser for organisational behaviour at the CIPD.
PL: But you already know that because Jonny is an old friend of the podcast and he's often shared his research findings with us in the past. This time he's leading the CIPD research into automation in the workplace and a new report called The Impact of Emerging Technologies on Work.
JG: There's no doubt that automation is a key part to the future of work. It’s something which has the power to make massive changes in how we work, a huge number of cognitive and physical tasks can now be automated and the number’s growing.
The problem is that it’s often difficult to tell what’s going on because the debate is very polarised. And you've got these two overly narrow positions: you've got the doom-mongers who say that robots are going to take over and we’ll have poor quality jobs and mass unemployment and then you've got the utopians who say that we’re all going to be freed up to write poetry in the afternoon, realise maybe the 15 hour working week that Keynes so famously predicted.
And there's some research which points to a large number of jobs being made redundant, for example being computerised, but when we look at tasks within jobs the number seems to fall dramatically and looking at tasks seems to be one of the keys to how we should look at this because it’s not jobs which are automated it’s tasks within jobs that are automated and for the most part jobs will be augmented by technology.
PL: Tasks, as Jonny says that is the key word here because when you listen to those doom-mongers he talked about much of their anxiety focuses on whole jobs disappearing to AI and robotics. That is already happening but much more commonly it’s parts of jobs that will be eaten up by tech. now sitting next to Jonny we also had Jennifer Cable. She's a talent management consultant at PA Consulting, CIPD’s partners on the research.
Now you've worked together on this big piece of research, you've talked in depth to three organisations about how they've been implementing AI, how it’s been received by their people, what did you find from that?
Jennifer Cable: Well one of the interesting things that comes out of the research is that perhaps employees, you see those same two groupings: the doom-mongerers [sic] and the people who are very optimistic and what we’re seeing in that is while those are the people you hear about in the press actually there's a much wider variation of passion and response from those who are involved in AI. And in fact quite a significant proportion of people are really excited and have found that there have been benefits, it really has liberated and created great opportunity.
There are of course others that perhaps haven’t had such a great experience with AI but that range of experience, we’ve got the doom-mongerers [sic] and the scare stories kind of persistent in the media and we see that in a same employee real lived experience of AI both positive and negative but a huge range of things in between.
PL: Overall based on the research Jennifer paints an encouraging picture of the impact that tech augmentation is having on people but it is clear that if human reactions to tech aren’t factored in overall performance can suffer; people become less motivated; there's less helpful human/computer interaction. And overall the organisation becomes less sustainable. So let’s get back to Atkins what does Gwion think that all their new tech means for the employees who used to do their jobs in different ways?
GK: Essentially what that does it takes time out of existing tasks. Now what that's enabling us to do is to use that time to actually think more creatively, more laterally, more holistically, about the problems, so the real client problems and the customer service we’re trying to provide. So that actually means that we’re getting smarter in relation to the service provision. That means that actually the end-user gets a better outcome.
PL: So you've got a bunch of smart people who've now got more thinking time.
PL: Presumably you have also lost some people have you?
GK: We haven’t lost anyone but we probably haven’t needed to recruit at the same levels that we have before because actually you spend a lot of time and effort in training, educating, that the main knowledge is exactly what you want to retain because that is the only real differentiator between say traditional engineering consultancies and the tech giants, they’ve got vast amounts of data, they’ve got access to all the algorithms but they haven’t got the main knowledge of actually how different forms of infrastructure and stakeholders engage with that infrastructure and that data.
PL: So that brings people to the fore again as a very important asset?
PL: So a slightly unexpected outcome there, it’s not just about the tech you're actually highlighting the need for smart people to do some thinking?
GK: Oh absolutely. They are at the heart of that digital transformation. They're the ones with the creative ideas, they’re the ones actually who know in the real world how people react. They can bring the emotion, they can bring the intelligence – the human intelligence aspect – that at the moment the programme can't.
PL: Yes and as you say at the moment, and playing devil’s advocate, presumably if you play in all this human thought and response and emotional reaction to what you’re doing you can see how a couple of years down the line you could produce technology that would replicate that and would you then still need the people to do the thinking?
GK: Well and therein lies the conundrum. As that evolves and as it does become more intelligent will we continue to envisage ever greater solutions or ever greater benefits from the technology? We might not be able to imagine where we might end up so I think it might take a leap at this point but you just don't know is the answer.
PL: A pretty frank assessment there from Gwion about where they’ve got to with tech and where they may, or may not, choose to go in future.
Jonny and Jennifer’s researchers interviewed 136 people from three organisations in three different sectors. They wanted to explore how they perceived the tech that now plays a part in their work. Useful and labour-saving, were their most popular choices when they were asked to choose from 14 positive and negative words. And over 90% felt confident about using the tech but they had some reservations too.
Yeah I was interested to see and I pulled out one statistic which was around the kind of change in workload, which is as you know a big issue for most working people and about half said they didn’t really see much change in their workload after the AI had been implemented, which surprised me. I think it was roughly about a quarter said there was more, about a quarter said there was less.
JG: But the pace increases.
PL: But the pace increased and I did think that was really key. So nearly half of them were saying, ‘It’s the pace.’ So they are working harder aren’t they? They don't have a bigger workload but somehow they’re working faster?
JG: By contrast the amount of work that they have on their plates doesn’t seem to vary so much. My reading of that is that again it’s a sensible application of new technology. So there's only so many hours in the day, there's only so much work a person can do, so in effect my reading of that is that the employer’s kind of regulating the amount of work that people have on their plates but they're enabling people to do things faster.
JC: There is a hugely positive spin to this report, we are seeing a number of people who have experience of working with different types of AI and automation really reporting a positive well-being. For example we have 33% of people saying they spent more time working on more interesting tasks.
But it’s not all a good story. There are other people who found that actually they didn’t feel the monotony of their work changed. And there were people who were saying, ‘Well I didn’t really see any benefits, there were pitfalls associated with it.’
So that raises an interesting question about is it the type of technology, is it the way it’s implemented? Or is it actually the readiness of the employees or the readiness of the technology?
PL: So people need to be ready for the tech and the tech needs to be ready for the people. And that's all the people, not just some of them.
Asif Sadiq: So my name’s Asif Sadiq. I am the head of diversity and inclusion for EY Financial Services.
When you look at any sort of artificial intelligence, robotics and so on, there's this whole piece around who’s designed it? Is there diversity within the design? And has diversity been factored into the end product? And there's numerous examples of when that hasn’t been taken into consideration. There was some software designed for robotics and they didn’t consider anyone who was black, so couldn’t facially recognise black people. Similarly there's designs where products have been put together for designing access in gyms and you find that because of the way the technology has been designed and those people might have biases themselves, or unconscious biases, technology had been designed where when you think of a doctor the automatic assumption is it’s a man and therefore access for a doctor in a gym would be to the make changing rooms not the female changing rooms.
AS: So I guess there's all these kind of things.
PL: We’re talking about trust here.
AS: How do I perceive the technology if I don't have trust in the most simplest of technology? And just to give you a very quick example on that a lot of different banks and online systems, including the HMRC, are using voice recognition software where they ask you a question and you input yes and then you give further details. Now my English, I would consider this fairly good, but I still of course being from an ethnic minority background, pronounce certain things slightly differently and I always struggle with those softwares because it will not recognise certain things that I say. Therefore I dismiss it straightaway. And that's I think a big, big issue. So if I can't trust something that basic how do you expect me to trust something that's going to take over so much more?
PL: Well this is the interesting thing isn’t it because as you say it lies at the heart of the whole debate around AI and tech because the thing about data sets you imagine they're a thing but they’re created by humans aren’t they? There's a selection process and everyone brings their inherent biases, unconscious biases, or implicit biases, to that process. There's the designers themselves, who are they? Are they all white men? Who knows? We don't know, there's no transparency. And all the tech that's coming into our workplace stems from that.
So from your point of view absolutely as someone from an ethnic minority why on earth would you believe it was any good for you or anything to do with you? So do you think, I mean this is the thing I think that isn’t being discussed that this is the real barrier to adoption that millions of people feel this stuff is not for me. It doesn’t know who I am.
AS: Exactly and I think even more so it’s where you have experiences and then the experiences are not positive the likelihood of you then using it again or using any further technology is next to none. So everyone’s currently talking about Alexa and the advancements in that and how it doesn’t pick up certain accents. So again that is a barrier. So it’s supposed to be a great thing for everyone but is it really for everyone?
PL: And that's a global technology.
PL: I mean does this surprise you?
AS: No, no it doesn’t because I mean and that’s on languages right? There was another example around YouTube where YouTube when they changed something on their software on how to upload videos and they discovered after a few hours that, I can't remember what number but a large number of videos were upside down and then when they looked at it they discovered that actually the way they'd designed it was for right handed people not left handed, and anyone who was left handed if they put it up it came up upside down.
Unless you've built into the processes the diversity of thought, diversity of experiences, which you can't really get through surveys or saying that we consulted a large group of people it is actually in the design stage and then the whole situation arises that actually when you look at this sector there isn’t much diversity. For diversity to truly bring about innovative ideas you don't need 20 people like me but you need a mix of people, a mix of everything. That's when you've got true diversity. And that goes for any industry or in any sector.
PL: So Asif’s point about tech trust is as relevant in the workplace as it is in our lives outside work and Jennifer agrees if we don't trust tech we’ll be far less likely to adopt it.
JC: Well this is it so it’s do I feel that I personally am going to be better off? So I need to have confidence that I'm still going to have employment, that perhaps I might have greater opportunity, that I'm still going to be able to contribute and in fact I'm doing a thing that's going to help me. And these examples here I think are where the organisation as a whole is looking for cost efficiency but perhaps there's a surplus of work so therefore I'm still going to be employed as a result, so there are other things I can do.
The outside of this of course when we look at the doom-mongering reports I think you do see an awful lot where if you've got very repetitive tasks that are taken over by a machine I may not cease to stay in employment with that particular employer but that's not what we’re seeing in this survey because those are not the kinds of tasks that we’re seeing replaced with these particular examples.
PL: Gwion has some strategies for employees who may be real assets to the organisation but who have concerns about the tech that's being introduced and what it might mean for them.
GK: Well I've some quite practical solutions to that, I am a practical guy by background, so things such as reverse mentoring are a real obvious solution to that problem and actually that demystifies a lot and takes away a lot of their fear.
PL: Does it also play into how you reward people because presumably in the past it was about how much time they might spend resolving whatever the issue was, now it’s not so much about the time is it because technology’s going to enable them to perhaps come up with a solution faster but it doesn’t necessarily mean they’re being less valuable to you? So in terms of how you build clients, how you pay your own people, is that an issue too?
GK: Yes that's a real issue. So some of our very best people that we’re equipping with this independent thinking and with knowledge of the art of what’s possible and we’re deploying that in real life scenarios, they’re at a premium within the market. At the moment we’ve kind of got a very inflexible benefits approach within the industry and so those people probably have more value in different industries at the moment which have got maybe some more advanced thinking of it and I'm thinking of the software industry in particular where potentially you're paid and your benefits are related to the value that you bring in and you may be paid over a shorter period of time on say a consultancy basis to produce something, an outcome, rather than on say a time-based salary.
PL: Right so that's clearly an issue isn’t it because you don't want to be losing these people…
GK: That's right.
PL: …to places where they might be better rewarded so you’re going to have to rethink the whole way you do it.
GK: Yes so actually the salary and benefit structure we’re having to really think hard about what is going to be models because I don't think that this is going to happen overnight but what models are we going to need to deploy going forward.
PL: And finally back to Jonny and Jennifer. It sounds, listening to the pair of you, as if the researchers, you found it encouraging in terms of how this is going to play out, amidst all the doom we read and hear about that actually you do see that it’s a real positive force for good if we implement in the right way?
JG: Broadly I think so. We’re looking at concrete examples within this research. I think that one of the things that we need to do to get beyond the polemic of both the doom-mongerers [sic] and the utopians is to be less fixated with the futuristic technology and to look at what’s happening now with recently developed technology, because there are really concrete examples out there, we don't need to speculate about the technology of tomorrow, we can look at the technology of yesterday and today as it’s happening now.
So that's what we’re doing with this research is to take really concrete examples of how in particular automation, AI and robotics are being used in practice and it’s perhaps not surprising that they're used pretty sensibly. There are some glitches in the system that organisations come across, so there are risks there but broadly speaking applied sensibly the benefits are there to be had.
JC: And what we found is less than 5% of the survey participants thought the emerging technology had a negative effect and that for me is incredibly encouraging about humans’ ability to really have their life and work enabled and enriched by AI and automation rather than diminished.
PL: That's it for this month but something tells me that we’ll be looking at this subject again before long, well I say ‘we’ of course the podcast might be entirely automated by then.
What is definite is that tomorrow is Day 1 of the 71st annual conference and exhibition. It’s come round fast hasn’t it? Manchester will be heaving with the HR and L&D community, all there to hear from people like Lenny Henry and Shami Chakrabarti – trust, good work, diversity, security and all the other big issues we spend our time thinking about. Now if you can't be with us this time don't worry we’ll be recording interviews for upcoming podcasts so you will be able to hear from some of the best speakers right here on the podcast. Thanks for listening.