It's hard to ignore the fact that our ability to cram more and more transistors onto a microchip — Moore's Law — is showing signs of slowing down. So much so, that tech giants are designing custom chips so they don't have to wait for the next generation of silicon chips to run ever more powerful AI algorithms. Not to mention the growing urgency of climate change and the exponential growth of the trillion-dollar ESG (Environmental, Social, Governance) movement which will likely accelerate demand for faster, cheaper more energy efficient processors. 

All of which indicates that we could be on the brink of what innovation expert Greg Satell calls the post-digital age. In other words, perhaps it's time to rethink how we compete, collaborate, and bring new products to market. But more than that, argues Satell, perhaps it's time to make wider, deeper connections between talent, technology, and information rather than just moving fast and breaking things.

"We've spent the last few decades learning how to move fast," says Satell. "Over the next few decades we're going to have to relearn how to go slow again. The digital age has been about agility and disruption. But it's time to think less about hackathons and more about tackling grand challenges."

Stay tuned as Satell debunks some of the biggest myths about innovation, gives us a preview of life after digital, and drops some serious knowledge on how to prepare for it. 

Question: You've written two books about innovation and what you call the physics of radical change. What was your inspiration for doing that?

Satell: You know, it was really just my personal experience. Well, my personal frustration running businesses. Because there's so much pressure to innovate, and so many ideas on how to do it.

"When you look for guidance (on how to innovate), you see really good ideas from people with really strong track records. But they often contradict each other."

So, I wasn't finding a good answer to the basic strategic question every leader must answer, which is "What should I do?" You'd see something like Design Thinking, which is a good example. Obviously super successful. Steve Jobs swore by it. Stanford University created an entire school behind it. You start with the customer. You figure out their needs. You work your way back. Rapidly prototype, and it makes a lot of sense.

Question: Yes, but some innovation experts, like Clayton Christensen, have argued that that approach is the wrong way to go about innovation.

Satell: That's right. You read Clayton Christensen's book: "Innovator's Dilemma." And he says that that's how you go out of business by listening too much to your customers, when the basis of competition changes. So, which is it? It doesn't seem like both of those things can be true.

And then you have the concepts of "Open Innovation" and "Lean Startups" and on and on and on. It can be incredibly confusing. So, I set out on this 10-year journey to figure it (innovation) out. And that's why I wrote "Mapping Innovation."

Question: So, what's the biggest takeaways on innovation for business and IT leaders today?

Satell: The biggest takeaway is that there's no one true path to innovation. Innovation is basically about solving problems. So, the first thing you have to do is figure out what kind of problem you're trying to solve. Because different strategies work well with different problems.

And the way a lot of organizations get stuck is that they say: "This is how we innovate. This is our innovation DNA or whatever." And they do that, because that's what Steve Jobs did, or that's what Elon Musk does. Or that's what worked for us the last time.

That approach can work well for a while until you hit a problem that doesn't fit. And then you can end up just spinning your wheels. So, you really have to focus on classifying the problem first. Then you identify the solution, rather than the other way around.

Innovation Isn't About Ideas; It's About Solving Problems

Question: So, based on your years of research, what do successful companies get right about innovation that other companies get wrong?

Satell: That's one of the questions I had in my mind while I was writing "Mapping Innovation." What do successful innovators (company leaders) do differently? I've been talking to and writing about great innovators for years. And I've studied hundreds of companies. And there never seemed to be a common denominator.

Some of them are very introverted. Some are extroverted. Some organizations are very fly-by-the-seat-of-your-pants. Others are very conservative. IBM is among the best innovators ever. They've been on the cutting edge of technology for over 100 years and they're a very conservative company too.

Question: So, what was it about these companies that stood out to you?

Satell: Nothing obvious stood out, you know — there wasn't one thing that successful innovators do differently than anybody else? But, in the course of researching the book, I finally realized the one thing that they all do consistently. And this is the thing that allows the best companies to innovate decade after decade, versus somebody who came up with one good idea but couldn't follow up on it. What makes the difference is that successful innovators focus on solving problems.

Question: So, what I hear you saying is that it's not some special brainstorming strategy or unique organizational culture that defines an innovative organization?

Satell: It's about how they (successful innovators) search for problems. Experian with DataLabs. They have a unit that just goes to customers and finds out what problems they're trying to solve, or what's giving them heartburn.

"So, what great innovators do is that they're always looking for new problems to solve. And what innovative organizations do is that they have a systematic and disciplined process for identifying new problems."

At Experian, when they find one (a problem that needs to be solved) that has potential, they take it back to DataLabs, and they have a team of world class data scientist there. And they can usually come back with a prototype solution in 90 days. If the customer likes what they see, the prototype goes back to an operational unit in Experian where it's scaled up as a real business.

Over the last five years, Experian has launched more than a dozen new businesses that way. Extremely successful. Then, you look at IBM and they have a very similar idea. They call it Grand Challenges. Deep Blue was one. Then Blue Gene, then Watson. They didn't have any idea about how they were going to make money with Watson. But they knew that it would open up numerous possibilities. They're also doing the same thing now with Project Debater (the first AI system designed to debate humans on complex topics).

Question: But that's a different approach that takes a longer view of innovation in terms of when it pays off.

Satell: IBM isn't looking for problems that they can solve in 90 days. They're looking for problems that may take them years and sometimes decades to solve. For example, they've been working on quantum computing for 30 years. And Google takes a similar approach to innovation.

Satell: Is that what you mean when you talk about systematic exploration as it relates to the innovation journey?

Satell: Yes. The disruption of GE is the opposite of that (systematic exploration). I mean GE hasn't invented anything innovative since CT scanners in the 1970s. And you're talking about a very capable company that makes serious investments in R&D. But what they don't do is explore. 

"If you don't explore, you won't invent and then you eventually get disrupted."

Question: But how do you get a big, traditional, legacy type company to adopt that kind of mindset?

Satell: Look, it's a basic equation, right? If you don't explore you won't invent. And if you don't discover, you won't invent. And then you're eventually going to get disrupted.

Question: So, what's the playbook for getting innovation right?

Satell: Some companies get closer to groundbreaking innovation by sponsoring academic post-doctoral research at universities. One of the most successful innovation programs at Google is one that nobody ever talks about. In fact, I've never heard of this program outside of Google. I'm talking about their practice of bringing in top-flight academics to work at Google for a year.

That's how Google Brain started. They bring in about 50 people, including high-flyers like (top AI experts) Andrew Ng and Geoffrey Hinton. But the point is that Google brings in about 50 people a year working on all kinds of things. Think about that, a company the size of Google bringing in 50 extra salaries. That's not a significant investment.

So, it's not about spending lots of money on investment in innovation. It's about realizing that exploration is important and it's about having the will to do it.

Don't Believe the Innovation Hype

Question: Let's change gears and look at some of the misconceptions about innovation. You've talked about the myth that innovation takes lots of money. But what are some of the other things people get wrong about innovation?

Satell: The biggest misconception, which is something that I've already touched on, is that innovation is about ideas. Innovation is not about ideas, it's about identifying problems. If you identify a meaningful problem, the ideas will come. So, that's the first thing. I've spoken to all kinds of innovative people and companies. And they were all focused on solving a problem, not on an idea. That's the first thing. The second misconception is that you need to have a leader like Steve Jobs.

Question: What's wrong with taking a Steve Jobs approach?

Satell:  You don't need someone who's spouting off ideas all the time, breaking all the china. That's actually the last person you want. People like Steve Jobs are good at going off and starting their own companies. But you don't want them working in yours. When Steve Jobs worked for other people, he was a disaster.

Question: So, what's a better approach?

Satell: An innovative culture that's a collaborative culture. You want people who are good listeners. People who can work effectively with other people. One of the most interesting things that I found through my research is that many of most innovative people that I've talked to, including world-class scientists, are among the nicest people I've ever met. But that's what great innovators are like.

"Great innovators aren't necessarily smarter or more talented than anybody else. They don't know everything, but they know somebody who knows."

And because they build up these fantastic knowledge networks, they can be information brokers, which makes it possible for them to come across that random insight or piece of information they need to solve a difficult problem.

And the way you do that is by building a strong network of connections. And the way you do that is by being generous with your time and expertise. So generosity can be a competitive advantage.

Question: The conventional approach to innovation is listening to customers, investing in continuous improvement and watching the bottom line. But we live in a volatile world and if market conditions shift in a crisis — like the pandemic — you can end up getting continuously better at things that customers no longer want.

Satell: That's when it's time to innovate your business model not your product. The truth is many organizations get stuck because they end up locking themselves into a single strategy. They find something that works and say, "This is how we innovate," and end up trying to apply essentially the same solution no matter what the problem is. Eventually, that ends badly.

That's why organizations that were once called great innovators fall behind. They get stuck on the mindset of being a square-peg company in a round-hole world and they lose relevance. It's better to match solutions to problems, not the other way around.

Question: I want to shift gears and talk about the argument you make that it's time to think less about hackathons and more about tackling big challenges. What did you mean by that?

Satell: Yeah, I was talking the new era of innovation. We've come to associate innovation with digital technology and agility. And that's largely because we understand digital technology really, really, well. And we've gotten used to this idea that every two years, we're going to get a new chip that's twice as powerful and better than the last one.

Question: It was like free innovation.

Satell: And it's also like the value has shifted to the front end, to things like the user interface and design. Think about the iPod, and Steve Job's idea of putting a thousand songs in your pocket.

So, the manufacturer of the technology (behind the iPod) made money, but not nearly as much as Apple did with the iPod. So, over the last 20 or 30 years, a lot of that value has shifted to the front end. But now, that's all ending. Moore's Law is ending. So, the question is: "What do we do when that happens?"

Post-Digital Computing and the AI Conundrum

Question: So, what comes next? And how do we prepare for it?

Satell: We need to come up with new fundamental technologies. And there's a bunch of candidate technologies, primarily quantum computing and neuromorphic chips. But they don't work anything like the old microchips. And that's going to cause some problems. People are using quantum computers right now, and we know that they're potentially extremely valuable for things like simulating physical systems. It won't pay off for at least five years. But in five years, the companies that aren't preparing for the age of quantum computing are going to have a real problem, because they won't know how to take advantage of the technology.

Question: Which gets back to your argument that it's time to prepare for life after digital. But what does that look like and what does it mean for business leaders today?

Satell: It means that we've spent the last couple of decades learning how to go fast. We're going to have to spend the next couple of decades learning how to go slow again. You can't rapidly iterate a quantum computer. You can't rapidly iterate a revolutionary new material for solar panels. And you can't rapidly iterate a cure for cancer.

One of the organizations I talked to quite a bit is the Joint Center for Energy Storage Research at Argonne National Laboratory (JCESR). This is a consortium of five national laboratories, research universities and a network of over 100 private sector companies.

Question: I'm not familiar with JCESR. What do they do and what did you talk to them about?

Satell: They're on a mission to identify the battery chemistry of the future. They were supposed to come up with a prototype for the grid and another for devices and cars. Because when you think about it, it's kind of crazy that we use lithium ion for both.

By the way lithium ion has a similar problem to Moore's Law. It's also reaching its theoretical limits. Not quite as fast or as imminent as Moore's Law, though — we're talking perhaps five to 10 years out. So, JCESR not only identified one chemistry for each. They identified two chemistries for each. Now we know the chemistries that are viable for next generation batteries. And we know what it takes to make them work. The only problem is that the materials don't exist yet.

Question: Let's switch gears for a minute.Intelligent automation is hot right now. KPMG recently released a study saying that nearly half of all corporations intend to use some form of intelligent automation at scale. Is that how companies are going to compete in the future? If that's the case, which companies do you think are getting automation right?

Satell: So, if you want to understand the value of automation, just go to an Apple store, which is one of the most automated retail experiences you can imagine. When you walk into an Apple store, you see a sea of blue shirts. That's because the function of a retail store is no longer to drive transactions. It's to do everything that you can't do online. Get advice, up sell, get service, get training, whatever it is that you can't get online.

Question: In wrapping up, let's talk about another hot topic — the ethical implications of AI and intelligent automation.

Satell: Yeah, so that's a big problem. That is a major, major problem. I wrote about that in Harvard Business Review a couple of years ago. I was at an academic conference up at IBM, and everybody was saying that we've got a serious, serious problem. The thing is, there's not one ethical problem, there are a number of them.

Question: Can you be more specific?

Satell: You've got the basic (science fiction) stuff like machines taking over the world and Skynet (a fictional general AI system that seeks to eliminate humanity). And you have other philosophical problems that are good for cocktail party conversations.

"But AI problems are becoming real. If you put AI in a car, sooner or later that car is going to have to make a decision that will harm a pedestrian or the driver."

Question: There's a lot of commentary out there about the urgency of dealing with the problem of AI bias? What do you make of that argument?

Satell: The technical term for that is bias in the learning corpus. That's a really big problem. Do you have kids?

Question: Yes.

Satell: When they were growing up, you worried about what they were learning in school, and who their friends were, and what TV shows they were watching, because you worried about what was influencing their learning process. So, the question is what influences are our algorithms being exposed to? Who's watching that?

Microsoft Tay is a classic example. They put a (AI chatter) bot on Twitter. And within 24 hours, it was exposed to Twitter trolls that had it repeating bigoted and misogynistic misinformation.

Even more subtle than that, there's a great book by Cathy O'Neal called "Weapons of Math Destruction," in which she makes the point that we don't know how any of these algorithms are being trained.

Question: The thing is, algorithms are already touching almost every aspect of our lives.

Satell: Yes, they're making decisions about who gets exposed to predatory marketing, who gets a job, who goes to jail, who gets paroled, who gets a mortgage. So, the ethics question is a major, major challenge. The big tech companies seem to be trying to get ahead of it.

When I wrote about AI and ethics two years ago, they were setting up something called The Partnership for AI to come up with ethics standards for AI.  The thing is, everybody's worried about who's influencing our kids. But we should also be worried about who's teaching our algorithms.