17 Deploying Artificial Intelligence ^Top Another solution is to deploy in a limited environment, giving the organization a chance to experiment, iterate, and solve before affecting operations on a larger scale. For example, Shell implemented an AI solution for precision drill- ing and controlling drilling equipment, with an aim to scale it eventually.37 For successful firmwide adoption, expertise for implementing AI can be built over time through a cohesive data and cloud strategy, collaboration and sharing best practices, and dissemination of the learning across the organization.38 For such pilots, it’s essential to focus on a core business area. If the pilot fails, the learning is still relevant and can be used for further experiments. And expanding a successful pilot can be quicker, offering greater competitive advantage. pitfall #5: implementing without a clear value proposition. Prioritize thorough evaluation of the solutions’ value proposition over timeline for implementation. Else, it can cause greater damage than the lost opportunity of not implementing — like the expensive ~$1 billion biometric exit solution of the Department of Homeland Security (DHS) that flags travelers for scrutiny by comparing them with a biometric facial database. It was proven ineffective and the additional value it brings is questioned by DHS itself.39 Taking time to evaluate the value proposition to the customer and making sure the business case is clear will save many a resource.40 This involves determining exactly what you are solving for and why, and the business driv- 37 Barry Samria, “Oil & Gas: Operators Slow,” 2020. 38 Pam Sahota, “On the road to AI adoption, slow and steady wins the race,” Thoughts on Cloud: Cognitive (blog), IBM, November 29, 2018, https://www.ibm.com/blogs/cloud-computing/2018/11/29/ai-adoption-slow-steady/. 39 Harrison Rudolph, Laura M. Moy, and Alvaro M. Bedoya, “Not Ready For Takeoff Face Scans At Airport Depar- ture Gates,” Georgetown Law: Center on Privacy & Technology, December 21, 2017, https://www.airportfacescans. com/. 40 Paul J.H. Schoemaker and V. Michael Mavaddat, “Scenario Planning for Disruptive Technologies,” in Wharton on Emerging Technologies, ed. George S. Day and Paul J.H. Schoemaker with Robert E. Gunther (Hoboken, NJ: John Wiley & Sons Inc., 2000), chap. 10.