Artificial intelligence is changing every industry—from manufacturing to retail. It’s also changing the culture at companies as they strive to keep up with accelerating digital technologies.
In a recent survey, “2021 Thriving in an AI World,” KPMG found that across every industry—manufacturing to technology to retail—the adoption of artificial intelligence (AI) is increasing year over year. Part of the reason is digital transformation is moving faster, which helps companies start to move exponentially faster. But, as Cliff Justice, US leader for enterprise innovation at KPMG posits, “Covid-19 has accelerated the pace of digital in many ways, across many types of technologies.” Justice continues, “This is where we are starting to experience such a rapid pace of exponential change that it’s very difficult for most people to understand the progress.” But understand it they must because “artificial intelligence is evolving at a very rapid pace.”Justice challenges us to think about AI in a different way, “more like a relationship with technology, as opposed to a tool that we program,” because he says, “AI is something that evolves and learns and develops the more it gets exposed to humans.” If your business is a laggard in AI adoption, Justice has some cautious encouragement, “[the] AI-centric world is going to accelerate everything digital has to offer.”
Laurel Ruma: From MIT Technology Review, I’m Laurel Ruma, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.
Our topic today is the rate of artificial intelligence adoption. It’s increasing, and fast. A new study from KPMG shows that it’s accelerating in specific industries like industrial manufacturing, financial services, and tech. But what happens when you hit the gas pedal but haven’t secured everything else? Are you uneasy about the rate of AI adoption in your enterprise?
Two words for you: covid-19 whiplash.
My guest is Cliff Justice, who is the US leader for enterprise innovation for KPMG. He and his group focus on identifying, developing, and deploying the next generation of technologies, services, and solutions for KPMG and its clients. Cliff is a former entrepreneur and is a recognized authority in global sourcing, emerging technology such as AI, intelligent automation, and enterprise transformation. This episode of Business Lab is produced in association with KPMG. Cliff, thank you for joining me on Business Lab.
Cliff Justice: It’s great to be here. Thanks for having me.
Laurel: So, we’re about to take a look at KPMG’s survey results for its “2021 Thriving in an AI World” report, which looks across seven industries. Why did KPMG repeat that survey for this year? What did you aim to achieve with this research?
Cliff: Well, artificial intelligence is evolving at a very rapid pace. When we first started covering and investing in artificial intelligence probably seven years ago, it was at a very nascent form. There were not very many use cases. Many of the use cases were based on natural language processing. About 10 years ago was when the first public use case of artificial intelligence made the headlines with IBM Watson winning Jeopardy. Since then, you’ve seen a very, very rapid progression. And this whole field is evolving at an exponential pace. So where we are today is very different than where we were a year or two ago.
Laurel: It does seem like just yesterday that IBM was announcing Watson, and the exponential growth of artificial intelligence is everywhere, in our cars, on our phones. We’re definitely seeing it in more places than just this one kind of research case of it. One of the headlines from the research is that there’s a perception that AI might be moving too fast for the comfort of some decision-makers in their respective industries. What does too fast look like? Is this due to covid-19 whiplash?
Cliff: It’s not due to covid whiplash necessarily. The covid environment has accelerated the pace of digital in many ways, across many types of technologies. This is where we are starting to experience such a rapid pace of exponential change that it’s very difficult for most people to understand the progress. For any of us, even myself who works in this field, it’s very difficult to understand the progress and the pace of change. And getting an enterprise ready—getting the people, the process, the enterprise systems, the risk, the cyber protections prepared for a world that is powered more and more by artificial intelligence—it’s difficult in normal circumstances. But when you do combine the digital acceleration and adoption that’s taking place as a result of covid, along with the exponential development and evolution of artificial intelligence, it’s hard to understand the opportunities and threats that are posed to an organization.
Even if one could fully wrap their head around the progress of artificial intelligence and the potential of artificial intelligence, changing an organization and changing the mindset and the culture in a way to adopt and benefit from the opportunities that artificial intelligence poses and also protect against the threats take some time. So, it creates a level of anxiety and caution which is, in my view, well justified.
Laurel: So, speaking of that caution or planning needed to deploy AI, in a previous discussion at MIT Technologies Review’s EmTech Conference in 2019, you said that companies needed to “rethink their ecosystem when deploying AI”, meaning partners, vendors, and the rest of their company, to get everybody to come up to speed. At the time, you mentioned that would be the real challenge. Is that still true? Or do you think now that everything is progressing so quickly, that’s the discomfort that some executives may be feeling?
Cliff: Well, that’s true. It is still true. The ecosystem that got you to a level in more of an analog-centric world is going to be very different in a more AI-centric world. That AI-centric world is going to accelerate everything digital has to offer. What I mean by digital are the new ways of working—the digital business models, the new ways of developing and evolving commerce, the ways we interact and exchange ideas with customers and with colleagues and coworkers. All of these are becoming much more digital-centric, and then artificial intelligence becomes one of the mechanisms that evolves and progresses the way we work and the way we interact. And it becomes a little more like a relationship with technology, as opposed to a tool that we program because AI is something that evolves and learns and develops the more it gets exposed to humans.
Now that we have much more human life-perceptive capabilities, thanks to the evolution of deep learning, (so by that, today, I mean more computer vision), technology is able to take on much more of the world than we were before. So understanding what technology, what AI, all of the capabilities that AI can bring and enhance and augment human capabilities is critical. Reestablishing and redeveloping the ecosystem around your business and around your enterprise is important. I think the bigger and more long-term issue though is culture, and it’s the culture of the enterprise that you’re responsible for, that one’s responsible for. But it’s also harnessing the culture, the external culture, the adoption, and the way you work with your customers, your vendors, suppliers, regulators, and external stakeholders. The mindset evolution is not equal in all of those stakeholder groups. And depending on the industry that you’re operating in, it could be very unequal in terms of the level of adoption, the level of understanding, the ability, and the comfort to work with technology. And as that technology becomes more human-like, and we’re seeing that in virtual assistants and with those types of technologies, it’s going to be a bigger chasm to cross.
Laurel: I really like that phrasing of thinking of AI as a relationship with technology versus a tool, because that really does state your intentions when you’re entering this new world, this new relationship, and that you’re accepting that constant change. Speaking of the survey and various industries, some of the industries saw a significant increase in AI deployment like financial, retail, and tech. But here was it that digital transformation need or covid, or perhaps other factors that really drove that increase?
Cliff: Well, covid has had an acceleration impact across the board. Things that were in motion—whether these were adoption of digital technologies or growth or a change in consumer behavior—all of those trends that were in place before covid accelerated them. And that includes business models that were on the decline. We saw the trends that were happening in the malls. That’s just accelerated. We’ve seen the adoption of technology that’s accelerated. There are industries that covid has less of an effect on, not a zero effect, but less of an effect. Banking, financial services are less affected by covid than retail, hospitality, travel, logistics. Covid has really accelerated the change that’s occurring in those industries.
AI, separate from covid, has a material impact across all of these. And as our survey said, industrial manufacturing, the use of robotics, the use of computer vision, artificial intelligence to speed productivity, and improved efficiency have really begun to become mainstream and at scale in industrial manufacturing. Same thing with financial services, consumer interaction has been improved with artificial intelligence in those areas. Technology, not surprisingly, has fully adopted AI or pretty close to fully adopted AI. And then we’ve seen a dramatic increase in retail as a result of AI. So online shopping, the ability to predict consumer demand has been a strong use case for AI in those industries.
Laurel: So, the laggards though, laggard industries were healthcare and life sciences at only, I say only, a 37% increase in adoption from last year’s survey. That’s still a great number. But do you think that’s because fighting covid was the priority or perhaps because they are regulated industries, or there was another reason?
Cliff: Regulation is a common theme across those laggards. You have government, you have life sciences, healthcare. Financial services, though, is regulated too, and they’re a large adopter, so it can’t be the only thing. I think the hypothesis around covid is probably more plausible because the focus in life sciences has been getting the vaccine out. Even though from our point of view and from what we see, government is a massive adopter. Just in terms of the potential within government, it’s still behind. But the sheer numbers and the sheer amount of activity that’s taking place in government when you compare it to private enterprise is still pretty impressive. It’s just that you’re dealing with such a large-scale change and a lot more red tape and bureaucracy to make that change within a government enterprise.
Laurel: For sure. You mentioned earlier the industrial manufacturing sector, and that sector saw 72% of business leaders were influenced by the pandemic to speed AI adoption. What does that actually mean for consumers in that industry, as well as that sector as a whole?
Cliff: When I look at these numbers, there’s not going to be an industry that is not affected by AI. The industries that are going to adopt it sooner and more rapidly or have an impact as a result of the pandemic, that is almost all been driven by remote work, the inability to get resources to a location, the impetus to drive automation, and AI being one of the foundational elements of automation. Because if you look at other parts of the survey where we ask, “Where are the biggest benefits?” it’s going to be found in efficiency and productivity. That’s fairly consistent across all industries when you look at where AI is being applied. So automation, productivity, predictive analytics, all of these areas are being driven by these themes around productivity. The use cases are different based on the industry, but the needs are very similar. The overarching themes and the overarching needs are very similar. You had some industries that were just impacted by the pandemic differently.
Laurel: Excitingly, maybe a difference in industrial manufacturing though, as you mentioned, are robotics. So a bit of our hardware play versus always software.
Cliff: Right. Yeah, in industrial manufacturing, you’re seeing a retooling of factories. You’re seeing what some people call the “Tesla effect,” where there is a focus on the transformation and the automation of factories—where building the factory is almost as important as the product itself. There’s a lot of debate and a lot of discussion in that sector around how much to automate, and is there too much automation? I think in some of these public events where you’ve seen a rapid ramp-up in production where automation was used, you’ve seen some backing off of that as well. Too much technology can actually have counterproductive consequences and impact because there has to be human involvement in decision-making and the technology just isn’t there yet. So, a lot of changes happening in that space. We’re seeing a lot of evolution, a lot of new types of technologies. Deep learning is allowing more computer vision, more intelligent automation to take place in the manufacturing process within the factories.
Laurel: Speaking of keeping humans involved in these choices and ideas and technologies, strong cybersecurity is a challenge, really, for everybody, right? But the bad guys are increasingly using AI against companies and enterprises, and your only response and defense is more AI. Do you see cybersecurity specifically being an area that executives across the board accelerate spending for?
Cliff: Well, you’re exactly right, cybersecurity is one of the biggest threats as technology advances, whether it’s AI-powered by classical computing or five or 10 years down the road when we have quantum computing made available to governments or to corporations. The security risks are going to continue to accelerate. AI is certainly an offense, but it’s a defense as well. So, predictive analytics using AI to predict threats, to defend against threats that are posed by AI, which are increasing the sophistication of penetration, phishing, and other ways to compromise the system. These technologies are sort of in an arms race between, as you said, the good guys and the bad guys. There’s no end in sight to that as we start to move into an era of real change, which is going to be underpinned by quantum computing in the future. This will only accelerate because you will need a new type of post-quantum cryptography to defend against the threats that quantum computers could pose to a security organization.
Laurel: It’s absolutely amazing how fast, right? As we were saying, exponential growth especially with quantum computing, perhaps around the corner, five, 10 years, that sounds about right. The research though, does come back and say that a lot of respondents think their companies should have some kind of AI ethics policy and code of conduct, but not many do, not many do. So those that do are smaller companies. Do you think it’s just a matter of time before everyone does or it’s a board requirement even to have these AI ethics policies?
Cliff: Well, we do know that this is being discussed at the regulatory level. There are significant questions around where the government should step in with regulatory measures and where self-policing AI ethics… How does your marketing organization target behavior in its customer base? And how do you leverage AI to use the psychological profiles to enable sales? There are some ethical decisions that would have to be made around that, for example. The use of facial recognition in consumer environments is well debated and discussed. But the use of AI and the ethical use of AI targeting the psychology of consumers, I think that debate has just started largely this summer with some documentaries that came out that showed how social media is using AI to target consumers with marketing products and how that can be misused and misapplied by the bad guys.
So, yeah, this is just the tip of the iceberg. What we’re seeing today is just the initial opening statements when it comes to how far should we go with AI and what are the penalties that are applied to those who go further than we should, and are those penalties regulated by the government? Are they social penalties and just exposure or are these things that we need laws and rules that have some teeth for violating these agreed-upon ethics, whatever they may be?
Laurel: It’s a bit of a push-me, pull-you situation, right? Because the technology is advancing really quickly, but societal or regulations may be a bit lagging. And at the same time, companies are not necessarily, maybe in some cases, adopting AI as quickly or are having problems staffing these AI initiatives. So, how are companies trying to keep up with talent acquisition, and should enterprises start looking, or perhaps already have, been looking at upskilling or training current employees how to use AI as a new skill?
Cliff: Yeah, these are very hard problems. If you look at the study and dive in, you’ll see the difference between large companies and small companies. I mean, the ability to attract talent that has gone through years and years of training in advanced analytics, computer engineering, deep learning, machine learning, and understanding the complexities and the nuances of training the weights and biases of complex, multilevel, deep learning algorithms—that talent is not easy to come by. It’s very difficult to take a classical computer engineer and retrain them in that type of statistical-based artificial intelligence, where you’re having to really work with training these complex neural networks in order to achieve the goals of the company.
We’re seeing the tech companies offer these services on the cloud, and that is a way to access artificial intelligence and access some of these tools is through the subscription to APIs, application program interfaces, and applying those APIs to your platforms and technologies. But to really have a competitive advantage, you need to be able to manipulate and develop and control the data that goes into training these algorithms. In today’s world, artificial intelligence is very, very data hungry, and it requires massive amounts of data to get accurate and high-quality output. That data accrues to the largest companies and that’s reflected in their valuation. So, we see who those companies are. A lot of that value is because of the data that they have access to. And the products that they’re able to produce are based on much of that data. Those products many times are powered by artificial intelligence.
Laurel: So back to the survey, one last data point here, 60% of respondents say that AI is at least moderately to fully functional in their organization. Compared to 10 years ago, that does seem like real progress for AI. But not everyone is there yet. What are some steps that enterprises can take to become more fully functional with AI?
Cliff: This is where I go back to what I said last year, which is to re-evaluate your ecosystem. Who are your partners? Who is bringing these capabilities into your business? Understand what your options are relative to the technology providers that are giving you access to AI. Not every company is going to be able to just go hire an AI expert and have AI. These are technologies, like I said, they’re difficult to develop. They’re difficult to maintain. They’re evolving at a lightning-fast exponential pace. So, the conversations that we would have had six months or a year ago would be different now, just because of the pace of change that’s taking place in this environment. The recalcitrance is low to change in AI. And so, it’s moving faster than Moore’s Law. It is accelerating as fast as the data allows it. The algorithms themselves have been around for years. It’s the ability to capture and use the data that is driving the AI. So, partnering with these capabilities, these technology companies that have access to data that’s relevant to your industry is a critical element to being successful.
Laurel: When you do talk to executives about how to be successful with AI, how do advise them if they are behind the competitors and peers in deploying AI?
Cliff: Well, we do surveys like this. We do benchmarks. We harness benchmarks that are out there in other areas and other domains. We look at the pace of change and the relative benefit to that specific industry, and even more narrow than that, the function or the activity within that industry and that business. AI has not infiltrated every single area yet. It’s on the way to doing that, but there are areas in customer service, the GNA, the back-office components of an organization, manufacturing, the analytics, the insights, the forecasting, all of that, AI has a strong foothold, so continuing to evolve that. But then there are elements in product design, engineering, other aspects of design that AI is moving into that there’s barely a level playing field right now.
So, it’s uneven. It’s very advanced in some areas, it’s not as advanced in others. I would also say that the perception that will come out in the survey of generalists in these areas may not consider some of the more advanced artificial intelligence capabilities that might be six months, a year, or two years down the road. But those capabilities are evolving very quickly and will be moving into these industries quickly. I would also look at the startup ecosystem as well. The startups are evolving quickly. The technologies that a startup is using and introducing into new industries to disrupt those industries are not necessarily being considered by the more established companies who have existing operating models and existing business models. So, a startup may be using AI and data to totally transform how an industry consumes a product or a service.
Laurel: That’s good advice as always. Cliff, thank you so much for joining us today in what has been a great conversation on the Business Lab.
Cliff: My pleasure. It’s great talking to you.
Laurel: That was Cliff Justice, the US leader for enterprise innovation for KPMG, who I spoke with from Cambridge, Massachusetts, the home of MIT and MIT Technology Review, overlooking the Charles River.
That’s it for this episode of Business Lab. I’m your host, Laurel Ruma. I’m the Director of Insights, the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology. And you can find us in print, on the web, and events each year around the world. For more information about us and the show, please check out our website at technologyreview.com.
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