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FROM THE EDITOR
You don’t know it, but you have more in common with Garry Kasparov than you think. And your organization — any organization — has more in common with the world of chess than you imagine.
In 1985 Kasparov, at 22, became the youngest World Chess Champion in history. Over most of the following two decades he was not only the top-ranked player in the world but the first champion to play repeated public matches against computers. In the beginning of that time span, he won. He always won. Then in 1997 he famously lost — to the International Business Machines Corp.’s supercomputer Deep Blue. And before the next 10 years had passed and Kasparov had retired, technology had changed so much that Kasparov not only couldn’t beat specialized computers like Deep Blue; he couldn’t beat good chess programs running on commercially available servers.
Andrew McAfee, the MIT Sloan research scientist and author of Enterprise 2.0, loves this story. But the part he loves best comes next.
“Where it really gets interesting, and where I get really optimistic,” McAfee says, “is that Kasparov says they ran a series of contests where they let any combination of people and computers play against each other. And the winning combination was not a team of the best chess players. It was not a collection of the fastest computers with the biggest horsepower. In fact, it was some good chess players working in combination with PCs. The winner was this wonderful blend of human intuition and pattern-matching backed up by a lot of computing horsepower. [That team’s] process for figuring out the next move was superior to both the extraordinarily good chess masters and the extraordinarily fast computers.”
There are two morals to this story. One: We are all Garry Kasparov. We’ve all seen what he’s seen as meteoric technological gains in computational power, data storage capacity and communications speed have transformed his world and ours, and have raised new questions about how leaders, managers and workers fit into it. Add the fourth key dimension of technology’s current evolution — the rise of sensors and instrumentation (the “smart” world) — and we have arrived along with Kasparov in the era of the data deluge, a fresh moment when both the amount of information we can know and the ability we have to analyze it is almost impossible to comprehend.
Certainly, companies don’t comprehend it yet. What will all this mean for organizations? What will it mean for how best to get work done?
Here’s where our chess story’s second moral comes in. As the contests between different computer-and-man combinations revealed, it turns out that in the chess world — as in probably the world of your organization — the best method for getting the best work done is to invent systems that merge the gifts of humans and the capabilities of machines, and that marshal those contributions in concert.
The challenge of doing that inventing is what most excites McAfee, as the following interview with him shows. It’s also why we at SMR have launched a new innovation hub project, in collaboration with the IBM Institute for Business Value, called “The New Intelligent Enterprise.”
“The New Intelligent Enterprise” aims to understand how management practice will be transformed by the newly emerging opportunities to capture, analyze and act on information. How will the competitive landscape be altered? What threats and opportunities will companies face? What new business models, organizational approaches, competitive strategies, work processes and leadership methods will emerge? How will the best organizations reinvent themselves to use technology and analytics to achieve novel competitive advantage?
In the coming year you’ll find insights about all those questions in SMR and here at sloanreview.mit.edu. They’ll be delivered in special reports, survey research results, articles and interviews. The interviewing of thought leaders worldwide — both research thinkers and leading executives — is under way. And the first “New Intelligent Enterprise” global management survey, benchmarking current practices and examining future plans, has just been put in the field; results will be reported in a few months.
Here at this website, you can read selected interviews with top MIT Sloan experts on the coming management implications of the data deluge and changing technology — implications that include the ascendance of experimentation, new applications of collective intelligence, the spread of distributed decision making and the invention of prescriptive analytics.
The conversation with Andrew McAfee is a sample of what “The New Intelligent Enterprise” inquiry promises. He spoke with SMR editor-in-chief Michael S. Hopkins.
You’ve gone on record saying that technologically we’re in a fast — and “weird” — place. Do you think we know what’s ahead for us?
We have a clue where we are, and we have a clue where we’re headed, but we don’t have more than a clue.
A couple things are pretty clear, though. We know that we are using technology to reach out to each other as people and to interconnect as people and to form human communities. This is the phenomenon that when I observe it inside corporations, I call it Enterprise 2.0. A lot of people call it Web 2.0.
I find this a fundamentally heartening thing because in the wake of all of this amazingly powerful technology, we’re not marginalizing people but putting them front and center in the middle of this great glue and letting them interconnect and share what’s inside their heads. We’re not trivializing what’s in their heads or trying to make it less important. I just find that grounds for great optimism.
But are organizations just doing old things in better ways? Or are they exploiting the new capabilities to entirely new things?
The Leading Question
How can organizations exploit new technology?
- Companies need to reconsider how they blend intuition and data when making decisions — and get more scientific.
- Future leaders will be able to impose technology-bred consistency and enable creativity to emerge.
- Competitive companies will propagate good ideas through-out their organizations more quickly than others.
It’s always easier to just think about doing what you’re doing now, faster, better, more automated, more productively. That’s not bad. It’s absolutely part of what organizations are doing and should be doing. The really hard work is understanding what new possibilities have opened up, and what important constraints are gone because of this cornucopia of technology that we’re sitting on.
It’s actually a really subtle art, and I think a lot of the business innovation going forward is going to be people saying, “Wait a minute. We can approach this situation, this problem, this market opportunity very differently than we have in the past.” One of the single biggest changes that I see coming is that when you have this unbelievable amount of horsepower and a mass of data to apply it to, you can be a lot more scientific about things. You can be a lot more rigorous in your analysis. You can generate and test hypotheses. You can run experiments. You can adopt a much more scientific mind-set.
I think if you don’t try to migrate your company and your decision making in that direction, you’re missing out on a huge opportunity, and you had better hope your competition is also not moving in that direction. Because when you compare scientific to pre-scientific approaches, there’s one clear winner over and over.
Can you give an example?
Watch excerpts from editor-in-chief Michael Hopkin’s conversation with Andrew McAfee.
Here’s one. If you build a pretty simple model of a lot of medical, clinical decisions, then run a bunch of patients through that model and also run them by an experienced clinician, the model’s going to do a better job of diagnosing them and improving health outcomes.
It’s really intriguing. The pattern appears to be that if you have to make a choice between complete reliance on human intuition and turning things over to a computer to spit out an answer, you might want to turn things over to the computer.
But I set up a false choice. You actually don’t have to choose exclusively between human intuition and push the button and run with what answer comes out of the computer. You can blend the two. What the machine’s not going to be really good at is what some people have termed complex communication.
The challenge is changing how organizations make important decisions, from front-line employees to middle-level managers to the top of the organization. People get to make decisions, but computers get to be part of it to let them know if they seem to be heading in the wrong direction.
But it’s easy to imagine a lot of impediments to meaningful change in how companies actually make decisions…
Well, this transformation toward a more “scientific organization” is a long, slow, uphill battle. It’s uncomfortable in a lot of ways for people to be second-guessed, to be put into a process in tandem with a machine. It’s not easy to become more data reliant, to become more enumerate, to become more quantitatively oriented, to understand what an experiment is and what a control group is and what’s a significant difference. We’re not terribly well trained for it, most of us. And so instilling this philosophy inside an organization is a long, slow transition.
Right, people in business talk all the time about “experimenting.” But they don’t mean experimenting; they mean “trying stuff.”
Or they mean, “Let’s design something that’s going to confirm what I really want to have happen here.” An eight-month process to spit out exactly the result that they want.
When we look at what real experimenting organizations do, they approach this in an open spirit of, “I don’t know what the answer is, and that’s what’s really exciting. I’m going to throw something out to see if this heads us in more the correct direction or the wrong one. Based on what I learn, I’m then going to do a subsequent trial.”
Are there particular characteristics companies will need to cultivate in order to make this “scientific” transition?
F. Scott Fitzgerald has a fantastic quote, I think in his book The Crack-Up. I’m going to mangle it, but he talks about how one of the characteristics of a first-rate mind is the ability to hold two opposing viewpoints at the same time and not go crazy. That’s really becoming important in organizations today.
The two opposing viewpoints are, first of all, that in a lot of ways companies have the opportunity to become even more tightly orchestrated, regimented, regulated via technology. We have all this amazing business-process technology that specifies with incredible detail what happens when, what the workflow is, who does what, what the roles and responsibilities are, what the decision rights are. While we can think about that as some kind of soulless destroying of the human spirit, it’s actually incredibly valuable. If I’m in charge of an organization, I want all of my vendors to get paid via a standardized, completely repeatable process that makes sure that they are going to get paid, that there’s no fraud and that the potential for abuse is as low as possible.
At the same time, we can use technology to do exactly the opposite thing, which is essentially to get out of the way and watch what happens. Let people self-select into their roles what they’re going to do, who they’re going to work with, what they want to share. Stop presupposing that we know what the right answer is and who should and shouldn’t be involved. What we see over and over again is that surprisingly good stuff emerges, and the bad stuff that happens is not worth worrying about.
This contrast between systematized and self-organizing regimentation — what terms do you like to use for it?
I use verbs to describe the difference between these two approaches. One of the verbs is “impose.” People at the center and the top of the organization get to impose throughout the rest of the organization their ideas for how work should be done. This is what the business process is. This is what the org structure is. This is what the roles and responsibilities are.
The other verb I use is “emerge,” which is basically get out of the imposition business altogether and start watching what emerges, what people actually want to do and how they want to use technology to work with each other.
This is exactly the shift that happened during the history of Wikipedia. They started out trying to impose a workflow for developing encyclopedia articles. People stayed away in droves. It was only when the leaders of that organization got themselves out of the middle of the process, deployed some weird new technology and watched what happened that the Wikipedia we know emerged.
So, recapping: Companies need to, (1) change their decision-making practices, (2) instill the scientific mind-set and (3) think about the impose-and-emerge dichotomy. What else?
The clearest part of my crystal ball about the future is that it’s going to be a busier place than the world we live in today, in a business or a competitive sense. The rate of change is only going to continue to increase. We have the ability to get smarter more quickly than we used to. The question is, when you come up with a good idea, can you impose it very broadly across your organization with technology?
As consumers of that industry’s products, we should be thrilled that everything is faster. We’re going to get better and better stuff at an increasing rate. As a competitor, it’s a little bit less comfortable. We’re going to have to keep up with that pace and play that game. Sitting it out is a really horrible idea as a business strategy.
Another favorite quote of mine is from Norbert Wiener, who was this incredibly bright, weird guy who worked at MIT in the middle of the 20th century. He said the world of the future is going to be an ever more demanding struggle against the limitations of our intelligence, not a comfortable hammock in which we can lie down to be waited on by our robot slaves.
So our robots are getting fantastic, but they’re not going to make life calmer and easier for us as businesspeople. They are going to push against the limits of our intelligence.
Is there anything people should be paying less attention to when it comes to technology?
The main redirection I would urge is turning away from relying on HiPPOs. I forget who coined the acronym, but it’s wonderful. It means highly paid person’s opinions. These are the business gurus of the world who have been around the longest and who are relying only on their business intuition.
Now, let me try to phrase the answer with a more positive spin, which is, what are the things that we can encourage leaders to do in the short term? There are two things I’ve already mentioned: developing an analytic or scientific mind-set, and thinking about how easy or difficult it is to propagate it.
Specifically, I ask, “What are some of the most important decisions that need to get made in your organization?” On a tactical level, on a day-to-day, repeatable level and then also at a more periodic and a more strategic level. Just what are the important decisions in your organization? How are you making them right now? Would you characterize them as more intuition/experience based? Would you characterize them as rigorous and supported by data and experimentation? And can we imagine how to shift more of that decision making into the realm of what I would call science?
Our colleagues at MIT’s Center for Information Systems Research, Peter Weill and Jeanne Ross, have done a ton of work on the digital infrastructure of big organizations, and the rule is fragmentation and the exception is consistency. Most big organizations are fragmented, and that impedes the ability to propagate good ideas throughout the organization. For instance, a company might have eight different versions of the same ERP [enterprise resource planning] system — one in Latin America, another in Brazil and different versions or software in Western Europe and North America. You don’t have data that stays consistent. You don’t have work flows and business processes that are consistent. And if you come up with a better way of doing business, you can’t spread it as widely, as quickly, as you can with an integrated infrastructure.
Last question. Imagine a zero-to-10 scale where at zero are people who use technology to do old things faster and more accurately. At the other end is a 10, where the organization has remade the entire way it works to capitalize on how technology can transform the ways we capture, analyze and act on information. Where do you think organizations rate, on average?
In every decent-sized organization that I am familiar with, there are pockets that are at the high end of the spectrum you just identified. There are individual knowledge workers and managers doing hugely innovative stuff. The question is, how many of those pockets are there? How much are they listened to?
They’re there, but they’re not making a sizable dent yet in how the organization thinks about itself and how they’re conducting business.
In aggregate, where are we?
In aggregate? Oh my. We’re about four.
You’re an optimist. That’s higher than I might have guessed.
I think it’s that high for a couple of reasons. The Internet has been unignorable. Everyone has an Internet these days. Everyone has an internal communication platform. The ERP revolution, the enterprise software revolution, is unignorable for big organizations. So when you look at where they are in their ability to coordinate and orchestrate work versus where they were prior to the mid-90s, it’s night and day different.
Those things make me think that we’re not back at zero, one or two. And companies are getting more analytical about at least parts of their business. There’s always some smart people with big computers sitting in some part of the organization, turning out analyses.
Imagine doubling that four to an eight. Forget getting to 10. How long do you think it’s going to be before we’re twice as good?
We are drinking from a fire hose, and the fire hose is getting higher pressure over time. To get from four to eight, we are talking about a 10- to 20-year process. And that’s with a lot of heavy lifting and heavy thinking, and struggles against the limitations of our intelligence, by the people who run and design organizations.
This is such an interesting time. It’s not exaggeration to say that throughout human history, we have been very sharply constrained in our ability to communicate either one on one or to a broad audience, and we’ve been really sharply constrained in our ability to do calculations that are of interest to us. Now, there are few situations and few contexts where those constraints are still binding.
We are in this strange world where it’s essentially costless to communicate, where we have so much computational power that we don’t know what to do with it, more memory than we know what to do with, more storage than we know what to do with. We haven’t been here before. This is a new period in human history.