In the eyes of many leaders, artificial intelligence and cognitive technologies are the most disruptive forces on the horizon. But most organizations don’t have a strategy to address them.

Artificial intelligence (AI) and cognitive technologies are burgeoning, but few companies are yet getting value from their investments. The reason, in our view, is that many of the projects companies undertake aren’t targeted at important business problems or opportunities. Some projects are simply too ambitious — the technology isn’t ready, or the organizational change required is too great.

In short, most organizations don’t have a strategy for cognitive technologies.

Managers may question whether having a strategy for a specific technology is necessary, but in the case of cognitive technology the justification seems clear. A 2018 survey of senior executives in 60 large companies by Boston, Massachusetts-based NewVantage Partners, where one of us (Tom Davenport) is a fellow, found that 72% of respondents saw cognitive technologies as the force most likely to disrupt their companies over the next decade (up from 44% in 2017), and 93% said their companies were already investing in cognitive technologies.1

Similarly, a 2017 survey of 300 C-suite and other senior executives by Genpact, a global professional services firm (where Vikram Mahidhar works), found that 96% of AI leaders — companies that achieve significant business outcomes from AI — believe AI will transform their workforce, but only 38% said their companies currently provide employees with re-skilling options.2

The size of both the opportunity and the disruptive threat of cognitive technologies makes cognitive strategy different from other technology strategies — say, e-commerce. Cognitive technology stands to be transformational. Driving the kind of widespread organizational change it will require won’t be easy, especially when it comes to implications for the workforce. Companies need to give careful consideration to how boldly they will step forward into the cognitive world and how much risk they are willing to take on. Developing a coherent cognitive strategy — and a means to fund it — can give companies a distinct competitive advantage. The first critical step in this process is to define the purpose, goals, and key components of such a strategy. We aim to help you lay this groundwork in this article.

How to Approach Cognitive Strategy

Broadly speaking, cognitive technologies employ capabilities — including knowledge, perception, judgment, and the wherewithal to accomplish specific tasks — that were once the exclusive domain of humans. The question for managers is where and how to apply them.

References

1. Big Data Executive Survey 2018,” NewVantage Partners, Boston, Massachusetts, http://newvantage.com.

2. “Is Your Business AI-Ready?” Genpact and Fortune Knowledge Group, 2017, www.genpact.com.

3. T.H. Davenport and R. Bean, “How Verizon Is Building a Big Data and AI Culture,” Forbes, Nov. 15, 2017, www.forbes.com.

4. T.H. Davenport and R. Bean, “How P&G and American Express Are Approaching AI,” Harvard Business Review (blog), March 31, 2017, https://hbr.org.

5. T. Simonite, “Google’s New Brain Could Have a Big Impact,” MIT Technology Review, June 14, 2012, www.technologyreview.com.

6. J. Bresnick, “IBM Watson Becomes Unique Clinical Decision Support Tool,” HealthIT Analytics, Oct. 22, 2014, https://healthitanalytics.com.

7. T.H. Davenport and J. Kirby, “Just How Smart Are Smart Machines?” MIT Sloan Management Review 57, no. 3 (spring 2016): 21-25.

8. Ibid.

9. T.H. Davenport, “What Data Scientist Shortage? Get Serious and Get Talent,” Data Informed, May 17, 2016, http://data-informed.com.

10. Z. Obermeyer and E. Emanuel, “Predicting the Future — Big Data, Machine Learning, and Clinical Medicine,” New England Journal of Medicine 375 (Sept. 29, 2016): 1216-1219.

11. D. Hernandez, “Hospital Stumbles in Bid to Teach a Computer to Treat Cancer,” The Wall Street Journal, March 9, 2017.

1 Comment On: What’s Your Cognitive Strategy?

  • HP Bunaes | May 20, 2018

    There is a fourth, far faster and less expensive, alternative – – leverage DataRobot, an automated machine learning platform. DataRobot puts sophisticated modeling capabilities directly into the hands of business analysts. Alternatively, DataRobot can offer companies with scarce data scientist capacity an order of magnitude jump in productivity. Automated machine learning offers better predictions and speed to market at lower cost.

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