Boston Consulting Group (BCG) posted an intriguing tweet this week:
“In traditional models of learning, the knowledge that matters is static & enduring. Going forward, it will be necessary to build organisational capabilities for dynamic learning”.
The tweet linked to an article, titled “Competing on the Rate of Learning”, which details the changed landscape for information, how companies are competing to learn at speed and the advantage of combining humans and machine.
Its authors, Martin Reeves, senior partner & managing director and director of the BCG Henderson Institute in New York, and Kevin Whitaker, a BCG economist, say “new technologies, particularly artificial intelligence, have the potential to propel the rate of learning in business to new heights – the volume and velocity of data have exploded, and algorithms can unlock complex patterns and insights with unprecedented speed”.
Crucially however Reeves and Whitaker say, “learning at the speed of algorithms requires more than algorithms themselves. New technology can accelerate learning in individual process steps, but to create aggregate organisational learning and competitive advantage it must be complemented by organisational innovation”.
They also say companies can’t ignore global megatrends, the “slow-moving contextual shifts, driven by social, political, and economic forces” which are “becoming just as important to business as fast-moving technologies”.
Like much of the technological enhancements being wrought on business and the economy, in digitisation and beyond, the authors highlight a common theme evidenced in the research – the human element is still vital to the process. The result, Reeves and Whitaker say, is “leaders must reinvent their organisations to leverage both human and machine capabilities synergistically in order to expand learning to both faster and slower timescales”.
That’s critical because while the algorithms enable “companies to operate at superhuman speed, learning about the market and reacting in seconds or even milliseconds” it’s humans which are best able to comprehend the changed “range of timescales that need to be considered”.
Let machines do what they do best
Ideally a company will evolve to allow technological, information gathering, and the algorithms that are collecting and processing the data to “become ‘self-tuning’ — sensing changes in the market immediately and responding on algorithmic timescales”.
That’s going to be uncomfortable for some companies and managers who still cling to the command and control approach to organisational operations and hierarchy. But, the authors say, “given the power of today’s technologies, leaders should let machines do what they do best—and focus on the critical issues that require distinctly human capabilities”.
Companies connect “data, AI algorithms, and automated execution in an integrated fashion with minimal human intervention” in order to allow the machines to do their bit. The result is the “closed-loop algorithmic learning process generates a virtuous cycle: more data makes algorithms more powerful, helping decision engines improve the firm’s selection or fulfilment of products, thus increasing volume and generating yet more data”.
Human brains should be used for high level issues and longer term time frames
While businesses are focused on speed of information gathering and processing and in personalising the customer experience as a result of what the algorithms learn, the reality is the disruptive forces of technology and machine learning are resulting in “corporate longevity” decreasing. This means “companies are falling from their competitive peaks faster than ever”.
That’s where the humans become crucial once more to the process.
Slow moving forces and megatrends can’t be seen as constants the authors say, noting, “recent events have shown, these non-competitive issues are becoming both less predictable and more relevant to long-run company performance, demanding correspondingly more attention”.
But, “even today’s most advanced technologies cannot easily analyse slow-moving external forces,” Reeves and Whitaker say, noting we are a long way from HAL or Skynet. Sure, the machines can “identify correlations extremely powerfully, operating at much greater speed and complexity than humans can” but they lack the “higher levels of reasoning” humans have which are “necessary to decode and shape these longer-term trends”.
That is AI can’t reason with regard to “causal inference (what happens when we act on a system) and imagination (what would happen if the system were different than observed in some significant way)”.
Thus business needs a “human + machine machine in which artificial and human intelligence are focused on their respective advantages,” Reeves and Whitaker say. They also highlight that “while machines collect data and find patterns at rapid speeds, humans concentrate on higher-order objectives”.
What companies need to improve their competitive position
Reeves and Whitaker say leaders need to act, “strategically to gain an advantage through learning, fully leveraging the potential of new technologies”. And to do that they need to:
- Invest in autonomous learning systems to allow the machines and algorithms to find highlight complex patterns at rapid speeds.
- Design effective human–machine interfaces to allow the higher level or non-repeatable tasks to be performed seamlessly in the business process.
- Embed autonomous learning structures throughout the enterprise, beyond just the market place and across the broad spectrum of your business.
- Measure and govern the business on all timescales allowing oversight for the short-run time frames of the machines as well as the longer-run timeframes also applicable to the business.
All this, Reeves and Whitaker say means “companies can unleash both the power of technology for rapid learning and human ingenuity on longer timescales. But this will require leaders first to re-imagine the organisation and how it is managed’.