Calm Down About the Hype Part 2

Research shows that since early 1950s, experts consistently predicted artificial intelligence (AI) overtaking humans “in the next 20 years” for the last 70 years. There may be hype.

Yes, it’s likely that many machines will be playing the role of managers in the future. But we’ve been in a world of mechanical managers for many years: A traffic light directs drivers; an automated call router delivers work to call center employees. Most people don’t find either situation threatening or problematic.

What this means is that the most important roles for computers is as a communication tool that allows much larger groups of people to think together productively.

And that’s the thing we’ll need to gear up for.

The ongoing, loud debate about how many and what kinds of jobs machine-learning will leave for humans to do in the future is missing a salient point: All uses of computers will need to involve humans in some way.

As we incorporate smart technologies further into traditionally human processes, even more powerful forms of collaboration are emerging—a kind of “collective intelligence”—to create new and smarter ways to organize work than our planet has ever known.

Picking up where we left Part 1

I mentioned in Part 1 basically this: Virtually all human achievements—from developing written language to managing data lakes to making a turkey sandwich—require the work of groups of people. People will be involved in deciding when to use different specialized (computer) intelligence mechanisms in different situations and what to do when things go wrong. For that we need human intelligence.

Any entity with intelligence has to have five cognitive processes. It must: (1) create possibilities for action, (2) decide which actions to take, (3) sense the external world, (4) reflect on the past, and (5) learn from experience. 

I was using P&G as an imaginary scenario and laid down 2 of these 5 ways that companies might use collective intelligence and how it plays into daily work. These are the other three. 

Sense

To be deemed “intelligent,” there must be some effective sense-making capability. That starts with asking powerful questions. It’s staggering to think where some organizations would be today if the obvious question — Are we addressing the right opportunity? — hadn’t been asked. The most visible means for improving insight with relevance are big data and data analytics. What do customers want now? Where do our core skills lie? What new technologies might change our industry? What are we now?

Going back to the example of P&G, it might use massive data to develop detailed models of many parts of its business, such as customers’ responses to prices, ads, and recommendations, and sort out how supply chain costs vary with inventory policies, delivery methods, and distributor locations. With tools like these, computers take over much of the quantitative work of strategic planning by running the numbers. People use their general intelligence to do more of the contextual analysis.

Reflect

Another way technology helps groups create better plans is by helping them remember good ideas that others have had in similar situations. For example, software assistants embedded in an app could automatically suggest generic strategies, such as: 

• Integrating forward by taking on some of the tasks done by customers, or integrating backward by taking on some of the tasks done by partners;
• Outsourcing more of the things you do internally to specialized providers or contractors;
• Moving into related market segments, nearby geographical regions, or other side markets frequented by your customers.

For instance, if P&G had a product strategy of using selfies to customize cosmetics, a software assistant could suggest similar strategies that let customers use smartphones to customize other P&G products: shampoos, toothpastes, laundry detergents, potato chips, and so on. Many of these combinations will be impractical or silly and no doubt quickly eliminated. But some might be surprisingly useful. Even silly options sometimes give rise to good ideas.

True story. A few years ago P&G developed a process for printing entertaining pictures on Pringles potato chips. An approach like this might have led to another promising idea: using this technology to let customers buy Pringles that are preprinted with images that customers specify themselves.

Learn

If a system is used over time, it can help groups learn from their own experiences to actually accelerate performance improvement. The system could even be used to recognize strategic ideas that most people wouldn’t recognize in the early stages. Think about the early days when Steve Jobs and Bill Gates were first playing around with what we now call PCs. Most people had no idea that these strange, awkward devices would turn out to be among the most innovative and influential products of the next several decades. 

It’s easy to reject out-of-the-box ideas and miss these diamonds-in-the-rough. But it’s now possible to identify people with unusual creative gifts who have this skill by systematically tracking over time how accurately and early people predict trends and technological advances. Then you could ask these people to take a second look at some of the “crazy” ideas that might be otherwise rejected.  

The Future Workplace

Given how generic much of this work would be using machine-learning and how complex it would be to keep it all on servers in the basement, it’s unlikely that companies would develop proprietary systems for this purpose. Instead, platforms using partnership models, even future competitors, will increasingly provide much of this functionality as a service. Based on early evidence from successful platform companies, businesses can have a stable of people at many levels of expertise on-call across the globe who could rapidly generate and evaluate various strategic possibilities, along with software to automate some parts of the process and people to help coordinate the rest.

As exponential technologies make this easier, we’re going to see many more examples of humans working in concert with machine-learning to solve all kinds of business and societal problems—not just corporate strategic plans, but also designs for smart cities, smartphones, educational systems, antiterrorism approaches, and medical treatment plans.

The possibilities are bounded only by unlimited human imagination.