In 2018, cars are commonplace, but it took 250 years for the automobile to develop from an idea to the car-oriented life we lead today. The world had to be moulded around it first.
Artificial intelligence (AI) is developing in a similar vein. The idea emerged more than 60 years ago, but needs time to be woven into the fabric of modern life.
“AI has tremendous potential, but such an immature technology also poses severe risks for those investing in it,” says Jamie Popkin, Distinguished VP, Analyst, at Gartner. “Think about a time frame similar to the automobile to reduce the risks and increase the benefits of AI.”
Most CEOs recognise that AI will be critical to their business, but few enterprises have actually undertaken AI initiatives. A recent Gartner CIO survey found only 4% of respondents had deployed AI in their organisations.
U.K. online grocer Ocado is one such pioneer, envisioning a fully automated AI delivery chain. Swarms of warehouse robots bring items to human pickers to place in baskets. Another AI-based system of conveyors directs the baskets to trucks for delivery. It takes 10 minutes for items to go from where they’re stored to the trucks. Ocado’s warehouse now moves 1.3 million items per day.
Advice for CIOs
AI can solve business problems today if you develop applications carefully. But don’t let early success deceive you, Popkin says. Instead, recognise that we’re in the pioneering era.
“In the long run, you’ll get more value out of what you learn about AI than from the uses it addresses today,” says Popkin. “As the technology matures, the more obsolete today’s applications, uses and AI-driven business models will become.”
AI is still immature across key dimensions of its future function and capabilities. It’s heavily focused on core generic technologies like graphical processing units, deep neural networks and natural-language processing.
A rapid set of improvement cycles will occur as AI develops, along with a diffusion of the core technology. This will produce a vast array of everyday products and services.
“We’ll see multiple generations of the AI industry over the next 75 years, spawning new uses, markets and possibly industries,” Popkin says.
Extend your planning horizons
Your strategic planning process today needs to have a forward-looking perspective for the long journey ahead. Encourage the executive team to ask, “What will our use of AI look like in another 60 years?”
Zoom out your strategic planning lens to 7 – 10 years to avoid hyperlocal planning horizons. Otherwise R&D, business planning and investment decisions may be negatively affected.
Why is it hard to plan on long time scales? Most strategic planning processes are short-term relative to a 75-year cycle. They focus on three- to five-year strategic business plans, two-year budgeting cycles and quarterly financial results.
An extended planning horizon will help you avoid several common pitfalls:
Failure to build required competencies
Over-investment in short-lived products and architectures
Opportunity costs of flawed decisions or investments
Inability to detect and interpret important technical, social, economic and political events that will determine the course of AI over time