After many years of promise and hype, artificial intelligence is now being applied to activities that not long ago were viewed as the exclusive domain of humans. It wasn’t all that long ago that we were wowed by Watson, Siri, and self-driving cars. But it’s getting harder for our smart machines to truly impress us. Earlier this year Google’s AlphaGo won a match against one of the world’s top Go players. Go is a very complex game, for which there are more possible board positions than there are particles in the universe. Yet, we seem to be taking AlphaGo’s impressive achievement in stride.
“Any sufficiently advanced technology is indistinguishable from magic,” is one of the most memorable quotes of science fiction writer Arthur C. Clarke. But, as we better understand its promise and limitations, technology becomes just another tool we rely on in our work and daily life. With familiarity, the romance begins to fade, - as was the case with electricity, cars, airplanes and TV in the early decades of the 20th century, and as has been the case more recently with computers, the Internet, and, - increasingly now, - with AI.
Over time, our feelings turn from wonderment and admiration for the seemingly magical achievements of the technology in its childhood years, to the far more practical questions of what the technology can actually achieve when it grows up. This has been a particular issue with information technologies in general, including the Internet and AI.
The IT industry has long been associated from what’s been called the Solow productivity paradox, in reference to Robert Solow's 1987 quip: “You can see the computer age everywhere but in the productivity statistics. We all thought that the Solow paradox was finally behind us when IT-driven productivity surged between 1996 and 2003. But despite the continuing advances in technology, productivity is now back to its slow pre-1995 levels, for reasons that are still not well understood.
Digital technologies are all around us. But, are they a major source of competitive differentiation? Are they a strategic value to business? Can they help increase innovation and productivity and drive long term growth? These questions have no easy answers, as we have learned over the years.
A recent Harvard Business Review article, - Designing the Machines that Will Design Strategy, - takes these questions a few steps further. Given that our advanced AI technologies can now play championship-level Go and assist in the diagnosis and treatment of rare forms of cancer, - can they help us address broad, open-ended and ambiguous problems like developing and executing a competitive business strategy? Can our increasingly smart machines assist us in translating technological advances into strategic advantage?, ask the paper’s authors, Martin Reeves and Daichi Ueda.
On its own, technology does not guarantee competitive advantage, argues the paper. “No matter how advanced technology is, it needs human partners to enhance competitive advantage. It must be embedded in what we call the integrated strategy machine,” which they define as “the collection of resources, both technological and human, that act in concert to develop and execute business strategies.”
In their view business strategy consists of a series of highly interrelated conceptual and analytical operations, including problem definition, signal processing, pattern recognition, abstraction and conceptualization, analysis, and prediction. There are feedback loops between all these various steps. Experimentation and real market data help to continuously redefine and reframe the problems being addressed as well as its solution. Within this integrated strategy machine, people and technology must closely collaborate, with each playing the particular role they are best at.
“Human beings are still unique in our capacity to think outside the immediate scope of a task or a problem and to deal with ambiguity. Machines are good at executing a well-defined task or solving a well-defined problem, but they can’t think beyond the specified context (at least not currently). Nor can they pose new questions, invent answers beyond what’s being asked, or reframe or connect the problem to a different challenge they’ve previously faced.”
The paper brought to mind a very interesting book, Innovation - the Missing Dimension, by MIT professors Richard Lester and Michael Piore, which I used in a graduate course on business transformation. The book explored the essence of innovation in new product development by examining a few truly novel products in different market areas. The authors concluded that innovation involves two fundamental processes: analysis and interpretation.
Analysis is essentially rational, quantitative, data-driven decision making and problem solving. Given its reliance on data, and algorithms, this is what data science and AI are particularly good at. It’s the standard approach underlying management and engineering practice. It involves a relatively linear set of steps and works quite well when you’re looking for a solution to a relatively well defined problem.
But where do the problems come from in the first place? How do you decide what problems to work on and try to solve? This second kind of innovation, - which the book called interpretation - is very different in nature from analysis. You are not solving a problem but looking for a new insight about customers and the marketplace, a new idea for a product or a service, a new approach to producing and delivering them, a new business model.
Their research showed that interpretive innovation generally takes place through a process of conversations among people and organizations with different backgrounds and perspectives, until the problems can be identified and clarified to the point where a solution can be developed. It requires curiosity, imagination, and a business culture that encourages these conversations and removes the organizational barriers that might prevent them from taking place.
In their HBR article, Reeves and Ueda suggest that business leaders should start the design of an integrated strategy machine by asking a few key questions:
- What strategic aims do I want to realize through a technology-enhanced process? “The initial set of questions must always come from human beings. Only people can define the objectives and use the holistic judgment necessary.”
- What technology, people, and design do I need to address these aims? Companies must be realistic about what it takes to build a competitive strategy machine. The best such companies are continuously investing in both technology and talent.
- How can people and machines interact in a way that augment each other? The objective should be “to enhance rather than marginalize or inhibit human thinking,” by stimulating people’s ability “to create new insights, challenge their own thinking, and continuously reframe their understanding.”
- How can the machine evolve and update itself? A well designed AI system includes mechanisms enabling it to get feedback, learn from experience and get better over time.
- How can the broader organization embrace the strategy machine? “In the end, a strategy is only valuable to the extent that it’s embraced and leveraged by the organization. Business leaders must pay attention to what can feasibly be achieved within organizational constraints - or have a clear path to removing them.”
“Electricity led to enormous productivity gains only when factory layout was revisited and optimized for the new technology,” write the authors in conclusion. “We believe that the integrated strategy machine can do for information technology what new factory designs did for electricity. In other words, the increasing intelligence of machines will be wasted unless businesses reshape the way they develop and execute strategy. Businesses leaders must start thinking now about how they can integrate their two key assets - people and technology - or risk falling behind.”