Thoughtful adoption of intelligent technologies will be essential to survival for many companies. But simply implementing the newest technologies and automation tools won’t be enough. Success will depend on whether organizations use them to innovate in their operations and in their products and services — and whether they acquire and develop the human capital to do so.
In a recent Deloitte survey of 250 executives familiar with how their companies are thinking about intelligent technologies, nearly three out of four said that they expected AI to substantially transform their organizations within three years.1 Of course, the workforce will be deeply affected by all this change. Yet even as AI eliminates some jobs in the coming decade (it most certainly will), it may create as many positions as it kills and open up vast new opportunities for collaborations between humans and machines. Earlier talk of large-scale job loss2 has subsided somewhat. In the Deloitte survey, for example, reducing head count through automation was the lowest-ranked objective for AI — only 7% of the respondents selected that as their first priority. Indeed, many observers are shifting their expectations away from job loss to job change, as humans find ways to work closely with machines.
Given the likelihood that many jobs will change rather than disappear, organizations need to understand the new skills required. In a recent McKinsey survey of executives at companies with revenues of more than $100 million, 66% of respondents said “addressing potential skills gaps related to automation/digitization” within their workforce was a “top 10 priority”; 64% of the U.S. respondents and 70% of the European respondents said they needed to retrain or replace at least a quarter of their current workforce.3 Significantly, just 16% of the business leaders responded that they were “very prepared” to address potential skills gaps, raising serious questions about their readiness to compete. Other recent surveys suggest that the high expectations executives have for intelligent technologies exceed their skills and experience in integrating such technologies into their companies.4
Although we have observed and worked with many large companies and startups on AI issues, we know of very few that have begun significant job redesign, re-skilling, or retraining programs. Moreover, most individuals aren’t being adequately re-skilled or retrained for automation-enabled work.
1. T.H. Davenport, J. Loucks, and D. Schatsky, “Bullish on the Business Value of Cognitive: Leaders in Cognitive and AI Weigh In on What’s Working and What’s Next,” Deloitte, 2017, www2.deloitte.com.
2. C.B. Frey and M.A. Osborne, “The Future of Employment: How Susceptible Are Jobs to Computerisation?” Technological Forecasting and Social Change 114 (January 2013): 254-280.
3. P. Illanes, S. Lund, M. Mourshed, et al., “Retraining and Reskilling Workers in the Age of Automation,” McKinsey Global Institute, January 2018, www.mckinsey.com.
4. “Avoiding Setbacks in the Intelligent Automation Race,” KPMG, accessed April 3, 2019, https://advisory.kpmg.us.
5. T.H. Davenport, “The Business Value of Digital Workflows,” Workflow Quarterly (spring 2019), https://workflow.servicenow.com.
6. M. Beane, “Shadow Learning: Building Robotic Surgical Skill When Approved Means Fail,” Administrative Science Quarterly 64, no. 1 (March 2019): 87-123.
7. M. Cohn, “For Internal Auditors, Innovation Is a Work in Progress,” Accounting Today, March 12, 2019, www.accountingtoday.com.
8. S. Lauchlan, “After the Self-Driving Car, Welcome the Self-Driving Enterprise — and All Its Pyramid Organization Implications,” Diginomica, May 2, 2018, https://diginomica.com.
9. T.H. Davenport and K.J. Dreyer, “AI Will Change Radiology, but It Won’t Replace Radiologists,” Harvard Business Review, March 27, 2018, https://hbr.org.
10. A. Prakash, “Forget the Markets, Robots Are China’s New Worry,” Forbes, Jan. 28, 2016, www.forbes.com.
11. N. Wingfield, “As Amazon Pushes Forward With Robots, Workers Find New Roles,” The New York Times, Sept. 10, 2017.
12. “Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles,” SAE International, J3016_201806, revised June 15, 2018, www.sae.org.
13. D. Silver, T. Hubert, J. Schrittwieser, et al., “A General Reinforcement Learning Algorithm That Masters Chess, Shogi, and Go Through Self-Play,” Science 362, no. 6419 (Dec. 7, 2018): 1140-1144.
14. AlphaZero learns through complex deep-learning algorithms, and it uses a so-called reinforcement learning approach similar to the way humans and other living beings learn. If a decision is made that is shown to be appropriate over time, a positive reinforcement is obtained that reaffirms that decision for the future; decisions that don’t work are penalized.
15. H. Edwards and D. Edwards, “How Tesla ‘Shot Itself in the Foot’ by Trying to Hyper-Automate Its Factory,” Quartz, May 1, 2018, https://qz.com.
16. S. Schrader, “This Time Lapse of a Tesla Model 3 Getting Built Is Weirdly Soothing,” The Drive, Jan. 5, 2019, www.thedrive.com.
17. T.H. Davenport interview of D. Burns, chief information officer of GE Aviation, Feb. 5, 2018.
18. E. Brynjolfsson and A. McAfee, “The Business of Artificial Intelligence: What It Can — and Cannot — Do for Your Organization,” Harvard Business Review, July 7, 2017, https://hbr.org.
19. K. Leswing, “Jeff Bezos Just Perfectly Summed Up What You Need to Know About Artificial Intelligence,” Business Insider, April 12, 2017, www.businessinsider.in.
i. J. Loucks, D. Schatsky, and T. Davenport, “State of AI in the Enterprise, 2nd Edition: Early Adopters Combine Bullish Enthusiasm With Strategic Investments,” Deloitte Insights, Oct. 22, 2018, www2.deloitte.com.
ii. Davenport, “The Business Value of Digital Workflows.”