Covid-19 was a catalyst for digital change in 2020, accelerating trends that were already in motion. Tanya Ott talks to Deloitte’s Scott Buchholz, Mike Bechtel and Anh Phillips to find out what comes next.
Mike Bechtel: You know, philosopher Mike Tyson once said that everybody has a plan until they get punched in the mouth. What COVID did was it punched all these high-performing, exquisite supply chains right in the mouth. And what a lot of organizations have found is that, my goodness, we might need to trade away a little of this performance for agility, for resilience, for flexibility, and for margin.
Tanya Ott: What are the nine big trends you need to know to better leverage technology? That’s what we’re talking about today on the Press Room.
Tanya: I’m Tanya Ott. Thanks for joining me. For more than a decade, Deloitte has produced an annual Tech Trends report. Each year, we look back on the past year, see how things went, then make predictions about the near and mid-range future.
But looking back on 2020? I don’t have to tell you how different things were because I’ve got the team here with me today. I’ll have them introduce themselves.
Anh Phillips: I’m Anh Phillips and I lead our Tech Insights Research team at Deloitte. My team really explores all the emerging technologies, as well as its impact on the organization, and report on our findings.
Scott Buchholz: Scott Buchholz. I have the privilege of serving as Deloitte’s emerging technology research director. In that role, I lead our annual Tech Trends research. I also am the chief technology officer for our government and public services practice.
Mike Bechtel: Hey, everybody. I serve as Deloitte’s chief futurist, where I help our firm and our clients understand what’s coming next.
Tanya: I’m sorry, chief futurist. I still love that title. That’s great. And such a succinct description. So, what’s coming next? This is our 12th year of doing tech trends. And usually when we look back at the last year’s predictions and then we see how things have progressed. Obviously, this is not a usual year. So everything this past year has thrown up in the air. Scott, I want to just start broadly: What have we learned from doing this for 12 years, and what really stands out this year?
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Scott: What we’ve learned from doing trends research for 12 years is that there are actually recurring patterns that we wind up seeing over time. If you were to look back 12 years ago, what you would see was a collection of shiny objects. What’s been interesting about it is we’ve realized that there are actually patterns that started to emerge as we were looking at shiny objects over time. So we’ve been able to get much more deliberate about sensing and scanning and paying attention to the future, what’s just over the horizon, because at this point, we have 12 years of trying to predict what is next and just over the horizon for enterprise technology, 18 to 24 months in the future.
Anh: To be clear, this is not a COVID edition of tech trends, but there’s no doubt that the pandemic has had a big impact and influence on the trends that we’ve identified this year. It has accelerated a lot of changes in organizations, and it’s impacted a lot of their technology initiatives. We’ve interviewed a number of executives who say that in the past months they [had] experienced a lot of digital change and a lot of transformation that they really hadn’t before—years in the span of several months. It’s forced companies to interact with their customers and their employees in different ways. Because of all of that, we can see the trends are being affected by the pandemic this year. So from core to supply chain to our digital workplace and “bespoke for billions,” some of those are trends that we’re seeing this year that are going to continue to happen, that have been catalyzed by the pandemic.
Tanya: Mike, Anh mentioned that folks basically did some digital transformation in weeks or months that might have taken much longer at any other time. So COVID’s really accelerated the path to the future. Or is that an exaggeration?
Mike: No, it absolutely has. COVID has shown up as a catalyst for change. In chemistry, a catalyst speeds up and sustains a chemical reaction. What we found with COVID is that it hasn’t necessarily changed things. It hasn’t shown up as a wild card or a black swan. It’s accelerated things. Work from home, for example, or work virtually—that’s been a trend 10 years in the making, but strategies and plans that expected five-year turns have happened in five months. Mark Andreessen, the venture capitalist, said 10 years ago that software is eating the world. COVID has increased its appetite. It’s eating it faster. The big shifts are continuing in the directions they were. The velocity, that’s changed.
Tanya: Are there any new shifts that you’ve identified?
Mike: One of the novel things we’ve been seeing is that in a world where the best talent could be anywhere, any time, the opportunity to broaden our net of what a high-performer is broadens along with it. So we see that opportunities for greater diversity, equity, and inclusion seem to be creeping up in a world where you’re able to find the best people wherever they are.
Tanya: Let’s dig into the trends. Scott, you’ve got nine trends this year. How should we be thinking about these trends? How are you organizing them?
Scott: When we put them all together and we’re looking at them, what we realized was that there are actually three groupings of three, Tanya. The first grouping is this idea around the heart of the enterprise. What are the things that drive companies? How does strategy inform technology and vice versa? All of those sorts of things are heart of the enterprise.
The second grouping is all about data. Here what we’re seeing is, what’s the art of the possible? Whether it’s machine learning or making machine learning run at scale or zero trust for cybersecurity, there’s a ton of change that’s been happening and that’s continuing to happen as we are speaking now.
The last grouping is really all about experience. Whether it’s inside the organization with customers and constituents, whether it’s about diversity, equity, and inclusion, there’s a tremendous amount of change as we’ve gotten comfortable with digital interactions because we’ve been forced to do more ordering online, but still want the feeling of dealing with our fellow humans in person.
Tanya: Dialing back to that first one, act one, you talked about the heart of the enterprise, and that is this idea that even those shiny, big new tech things get all the buzz, it’s actually often the operations behind the scenes that keep things running and move things forward. What were the trends that you’re seeing within that?
Scott: We had three trends, actually, in that grouping. The first one we called strategy engineered. That’s the idea that organizational strategy and technology strategy are becoming one and need to interact in a much more positive and continuous fashion, as opposed to being dramatically different because they’re done by different groups. The second is the idea of core revival. Here we have all of the major systems that run organizations and basically the changes that are happening, whether driven in part by COVID, whether driven in part by changing environments, but basically for new technologies, what is it that the art of the possible now represents in those areas? Then we actually have supply unchained, which is the realization of all of those change possibilities in a particular case where we’re seeing a lot of these things come together.
Tanya: So much to unpack there on. Let’s start with the idea of corporate and technology strategy increasingly, as Scott said, being inseparable. Why is it important to think about it that way? And what’s the challenge for some organizations?
Anh: You might have heard the phrase that every company is a technology company, and what that phrase speaks to is how integral technology is to an organization’s future and success. In a separate, multiyear study that we did on digital maturity and transformation, we found that companies that were furthest along in this whole process, and this whole journey had digital at the core of who they are and how to integrate it into their overall company strategy. So when you look at a technology strategy and a company strategy as two separate things, you’re missing an opportunity to really think of your company in terms of a technology company. You’re also missing the opportunity of using technology to help feed into your strategy. It’s becoming increasingly difficult to separate both of these.
The challenge for a lot of organizations is they don’t quite think that way. They operate in silos—the technology folks are thinking through the technology strategy and the business folks are thinking through the business strategy. But increasingly, the technology and the business are going to have to work more and more together to come up with an overall company strategy.
Beyond that, what this trend speaks to is the idea that technology really can support strategy development. It can help better sense triggers and trends in the marketplace to inform your organization on decisions that it’s making. It can help better monitor your progress and your outcomes against those strategies. It can help with scenario planning. A lot of this is about using data and the latest AI and technologies to think of strategy in a way that we hadn’t before. It used to be such a once-a-year thing. But people haven’t really thought about using technology to support those efforts.
Tanya: Do the strategy, the short, the mid, the long, put it in a binder up on the shelf and revisit it every once in a while.
Tanya: Does that mandate to think of it as inseparable change, the kinds of positions that companies might have? Does corporate leadership at an accelerated pace need to understand technology better? Do technologists need to understand the business argument better?
Anh: Both. Increasingly, business leaders need to be more technology savvy and tech leaders need to have a deeper understanding of the business.
Tanya: OK, short and simple. They just have to, right? What are the new approaches and technologies and business cases that can revitalize core assets?
Scott: The technologies over the past decade for revitalizing, refurnishing, touching up, or replacing and modernizing a lot of the systems of people’s enterprises have improved dramatically. Whether they are the newer, low-code platforms that are enabling more development to be done without strong development skills, whether it’s business rules, migration engines that are figuring out what’s actually going on inside legacy code, and whether it’s some of the creativity that COVID has forced around cloud migrations and other things, it’s really interesting to see the level of creativity and the level of technology that now supports people as they’re gussying up the core systems.
Tanya: Has COVID itself served to surface some of the real issues with some of the legacy systems?
Scott: A number of organizations ran into problems when COVID came up because they hadn’t really thought about how their systems would work and be accessed and be utilized in a remote work environment. As a result, there has been a realization and a set of changes that a number of organizations have gone through, driven by COVID sort of indirectly, driven by the need for remote work more directly.
Tanya: The third element in this part of the Enterprise Act of your Tech Trends report is about supply chains and how they’re being transformed. What are we seeing?
Mike: The supply chain story is, in many ways, the recognition that two words are not synonyms. Those words are “efficient” and “resilient.” For a generation, our clients have shown excellence at building highly-efficient, highly-precise, five nines, Six Sigma, pick your language, super-performance supply chains. You know, philosopher Mike Tyson once said that everybody has a plan until they get punched in the mouth.
Mike: What COVID did was it punched all these high-performing, exquisite supply chains right in the mouth. What a lot of organizations have found is that, my goodness, we might need to trade away a little of this performance for agility, for resilience, for flexibility, and for margin. This is showing up in a number of ways. Number one, they’re not chains. We call this trend supply unchained because the idea of a chain, it’s one directional and you pull on it. They’re giving way to hubs, hub, and spoke, a lot like the internet, where one part of the chain understands what the link three down is doing. That’s the second point: Interoperable data, the idea that the left hand and the right hand know what each other are doing and that the whole chain is better off for it. Then the third is all the really fun stuff. The gadgets, the gizmos, the emerging technologies like drones, like computer vision, that can bring automation and do-it-yourself, if you will, to processes that have historically been manual and inexpensive.
Tanya: What have been the biggest challenges to this kind of transformation?
Mike: Orthodoxy. Supply chain and procurement, per Scott’s point earlier about the core, these are corners of the business that haven’t been drinking from the innovation and modernization firehose like, say, digital or customer-facing sides. So, it’s less about teaching old dog new tricks. It’s really more about demonstrating that old orthodoxies, old ways of doing things are themselves not necessarily led by customer value and showing folks that they can show up on the right side of the balance sheet as opposed to the red.
Tanya: Moving on to the art of the possible. This is all about data. As enterprises move further toward automation and machine-led decisioning and human capacity, all of that kind of stuff, what are the trends that we’re seeing there?
Mike: So the data story also comes in three parts. The first is this move from data science to data engineering. The idea that AI is growing up and moving from the realm of the exceptional individual to the realm of the professional team. The second is an idea we call machine-data revolution, the idea that organizations need to rethink from first principles, the kinds of data they store and critically how they store it, putting it together in a way that is optimal for machine processing and insights as opposed to lonely human analysts. Then the third is the idea of zero trust—the evolution of cybersecurity postures from sort of castle and moat, where the presumption is that the baddies live outside and we can trust everyone inside, to the idea that, let’s not trust anyone from first principles, rather, let’s check, verify, and ensure that the right folks have the right privileges and access at all times.
Tanya: One of the questions that it raises is how can automated machine learning, model development, maintenance, delivery, all of these sorts of things affect the life cycles and industrialize AI? Like what does that mean?
Mike: AI and machine learning have felt artisanal. And by artisanal, I mean custom, one-off, custom tailored suits, if you will, in organizations that are otherwise about industrialization, production, speed, scalability. That’s resulted in two problems. One, machine learning talent, you know, the mythical data scientist, they’re hard to find and they/’re expensive. The second problem is that these folks, once engaged, spend a huge percentage of their time doing data cleaning, data preparation, data sorting, things that aren’t necessarily data science. They’re data engineering. So what we’re seeing is this move away from singular heroics to a team sport wherein teams of “merely” very professional engineers are there to gladly do all of that prep work so that the data scientists can do their work with excellence and with speed. This is the key to sustainability. There’s that old quote: If you want to go fast, go it alone. If you want to go far, build a team. At its heart this is a story about AI growing up and becoming a team sport.
Tanya: What are the challenges to making it a team sport?
Mike: One of them is accountability. When you go from me to we, you can create a diffusion of accountability, a lack of transparency. Who made this decision? One way to overcome that challenge is to ensure that the algorithms and the approaches underpinning AI models are themselves transparent and explainable. So sometimes we hear about explainable AI as this sort of virtue-signaling play. In reality, it’s a way to ensure that the left hand knows what the right hand is doing in a world where no singular developer developed the algorithm.
Tanya: How do organizations have to rethink their data strategy if all of this data is going to be tuned for machine consumption and not necessarily human consumption?
Scott: It’s a really important question, Tanya. As Mike was alluding to, as organizations are going from hundreds and thousands of machine learning models to tens of thousands and hundreds of thousands, what we’re seeing is for the past 50 years, we’ve collectively organized data to give a small number of decision-makers a small number of key performance indicators to make a small number of decisions. Now these machine-learning models can use thousands or tens of thousands of pieces of data to make decisions in real time in order to have massive impacts and do things at scales we’ve never seen before, because humans just don’t scale to that. As a result, what people are looking at is rethinking what they store, how they store it, how it’s made available, and a whole slew of other things that essentially feed the processes that Mike was talking about.
Tanya: Scott, this idea of zero trust cybersecurity, which we previewed sort of at the beginning of this part of the conversation, I loved Mike’s moat and castle wall analogy. But when he started moving into the like, trust no one, zero trust confirm everything that seems like incredibly, you know, “today” in this environment.
Tanya: How exactly does that work and how does that change the approach to the data?
Scott: What people are increasingly realizing is the idea that somehow we can keep the baddies out and the good people on the inside in some trusted place is long gone. Whether it’s clouds or VPNs or just phishing attacks and other things, the world of safety where desktops were plugged physically into walls is long behind us. This idea of zero trust is basically to say, let’s assume every system, server, application, device is sitting publicly available on the internet, and if that were true, what would we need to do to secure it? To Mike’s point, going back to first principles, what’s really interesting is many of us casually assume that more security means more inconvenience. The most interesting irony of this trend is what we’re finding is in a number of cases, you can have things that are both more secure and more convenient, because by securing things to be safe enough to put on the internet, we no longer have to make people jump through hoops to get to them. So there’s some really interesting changes that we’re seeing in this regard.
Tanya: So we’re reengineering data for machines, but ultimately our businesses are made up of and serve people, actual humans. So how do we help make organizations more human when at the same time they’re becoming more machine-driven?
Anh: You’re hitting on one of my favorite topics, which is the intersection of people and technology. Sometimes we approach technology for technology’s sake or get caught up in the technology for its sake, doing something just because we can and not necessarily because we should. We constantly have to remind ourselves that technology is a tool, often a very cool tool, but like any tool, we have to be intentional about the way that we use it in order to enable better human outcomes. These three trends this year in this category are about that. Bespoke for billions is about, how do you use technology to create more human connections with your customers and more personalized experiences with your customers? The other two are about how to use technology to create better experiences for your employees, [using] the concept of the future digital workplace, as well as using technology to support diversity, equity, and inclusion initiatives.
Tanya: What are some of the ways that you’re seeing that play out in a concrete manner?
Mike: For starters, in our digital workplace trend—rebooting the digital workplace—there’s an acknowledgment that the life that many of us lead mediated over Zoom, over Teams, over Slack, this is not the end game, right? This is the old way of working, lifted and shifted into little rectangles with even littler heads inside. When you look at the current state of work, it feels like a diminished proxy, right? Like a sad shadow of the way it used to be. But to Anh’s point, what we’re beginning to see the very inklings of is that now that everything’s mediated through tech, our interactions kick off what we like to call a digital exhaust, which in plain English means metadata or information about who works with who? Who works on what? Who’s happiest working on what? Who works best when? Concrete examples would be, imagine not auto-picking the next available time with a colleague, but auto-picking the best available time based on their demonstrated availability, demonstrated capacity, enthusiasm. This [plays on the] idea of effectively you can’t manage what you don’t measure. Interestingly, our virtual work is much more measurable and so old thoughts around facetime give way to new thoughts around data.
Tanya: Does it give way to new concerns or fears among employees about that digital exhaust they’re putting out there being tracked in a way that they're not comfortable with?
Mike: All of these trends carry the potential for a Black Mirror episode. But what I would tell you is that the workplace trend is less of an Orwell Big Brother concern and really more of an employee flexibility and wellness opportunity. It can sound Pollyanna when you frame it that way, but it’s the plain truth that if we understand what our employees are up to and how they’re doing and where they’re doing best and where their aspirations sit, we can deliver on the employee value proposition promise.
Tanya: Anh, to that point that Mike just made, this sounds to me like leveraging the data that you can get, the digital exhaust on people to look and say Anh, look, you’ve been in front of your computer 10 hours a day working nonstop, six days in a row perhaps. You need to take a break.
Anh: That’s exactly right, and it’s really about approaching, how do you handle that data and what do you use it in service of? If you’re looking at the data as a way to penalize people, that’s not going to go over well. But if you look at the data as a way to empower people, to give them the tools that they need to figure out what’s optimal for their performance and enable them to actually chart what’s working for them and what’s not working for them and help them improve their performance, most people are going to be okay with that, if they know that it’s being used to help them and not to penalize them.
If you look at diversity, equity, and inclusion, you can see some similar approaches and analogies to the way that you would use technology to support those initiatives, too. How do you use data to better understand the breakdown of your workforce? At a basic level, how people have been looking at diversity and inclusion is, hey, how many women do I have and how many people do I have of certain ethnic backgrounds? But DEI is really about going beyond that and using technology to understand not just your makeup, but how are these people given equal opportunity in terms of new projects, new chances to try new things, development opportunities? Then, how are they being paid? How are they being managed? How are they being promoted throughout their career and their time there? You can use some of that data that you’re collecting in your organization to track your progress against diversity, equity, and inclusion, because it’s not just about the makeup, it’s also about how is their experience in the organization. Just because you have X number of people representing different types of gender and ethnic background doesn’t mean that you have a diverse organization.
It’s more than that, right? It’s about diversity of thought. It’s about diversity of background. It’s about diversity of socioeconomic class, education, all of that. Studies have shown that the more diverse teams are the more intelligent, creative, successful teams as well. But just because you have the right makeup doesn’t mean that everyone is speaking or contributing in equal ways. Technology can also help monitor and see, for example, in this conversation that we’re having, who is talking the most and who is talking the least. It could actually be used to nudge you and say, Tanya, we’ve noticed that so-and-so on your team hasn’t been speaking. Maybe you should give them an opportunity to share. Maybe they’re just a little bit shy. We can use technology in that way to help us be better at identifying biases in our decision-making, in our processes.
Tanya: What you’re really talking about on is looking at this much more holistically, instead of just sort of the very bare bones that we might have been tracking recently.
Anh: Exactly. And to be clear, Tanya, we are at a very nascent stage in this technology. The technology will continue to advance and we’ll be able to do more with it.
Tanya: So you refer to it as a nascent stage, but how do we keep making predictions as things become less nascent and the technology accelerates and it goes lots of different directions? It’s not a supply chain anymore. It is a supply spoke-and-wheel or web or whatever you want to call it. In an unsettled world, how do you, because you guys are the ones making predictions, keep doing that?
Mike: I tell you that when you carry a title like chief futurist ...
Tanya: Oh, you’re in trouble.
Mike: You break your audience into neat halves of people who say, “That’s cool,” and people who say, “Oh, yeah, tough guy, bring it”. What I would tell you is, rule number one, we’re actually not in the prediction business. I like to say that we’re in the projection business. And that is to say that there’s a cone or like a spotlight of possibilities, of probabilities, a matrix of maybes. And after a year like COVID, the flashlight’s been shaking around a bit, but when it settles down it still tends to point in the same direction. So some of the things we look at, some of the enduring themes we know, thanks to in part what we call our macro technology forces, our taxonomy for technology change, we know that user interfaces are and always have been and probably always will be moving in the direction of simplicity. We used to sit down at a desktop to click and type and then we would lean back into a chair to touch and swipe. Pretty soon we’re going to be looking through smart glasses and then smart contacts and then—chips in our brains. Any of those ideas, la carte, sound crazy. But over the long lens, it’s a gradual move toward simplicity. So number one, don’t bet against simplicity.
On the data front, it’s a gradual move toward, get ready for nerd alert, but ... omniscience. Toward being ever more all-knowing, ever better informed. So data fads and storage techniques come and go, but the move toward artificial intelligence, exponential intelligence, artificial general intelligence ... don’t bet against intelligence.
Mike: Finally, underneath it all, the computer. Calculation, math. It’s moving toward abundance, which in plain English means more, more, more ... Growth, growth, growth. Right? One of the oldest tropes in futurism is Moore’s Law. These things double in power and halve in price every 18 months. You know what, they still do. And discontinuities notwithstanding, barring the apocalypse, we’re going to look back at the things we consider futuristic today as children’s games tomorrow. So in summary, don’t bet against simple. Don’t bet against intelligence. And don’t bet against capacity.
Tanya: That’s a fantastic way to end ... Thank you so much for your time today. There is so much to unpack in this year’s tech trends. We’re going to make sure that everybody knows where to find it.
Tanya: Anh Phillips leads the Tech Insights Research team at Deloitte Consulting LLP. Scott Buchholz is emerging technology research director at Deloitte Consulting LLP. And Mike Bechtel is Deloitte Consulting LLP’s chief futurist.
You can find their full Tech Trends 2021 report at deloitte.com/insights.
We’re on Twitter at @DeloitteInsight and I’m on Twitter at @tanyaott1.
Thanks for listening and have a great day.
This podcast is produced by Deloitte. The podcast provides general information only and is not intended to constitute advice or services of any kind. For additional information about Deloitte, go to Deloitte.com/about.
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