16 Deploying Artificial Intelligence ^Top achievable. Prioritizing impact and scalability can help in striking a balance between aiming for the shiniest solution and settling for a lackluster design. Similarly, a complex and uninterpretable solution is not necessarily better. There might be a simpler solution that’s less dazzling but also less opaque and more impactful. Equivant’s complex 137-variable model ML algorithm for estimating reoffences and setting criminal sentences was outperformed by a simple 2-variable linear model that was significantly faster and easier to implement and interpret.32 Meanwhile, Equivant received bad press for its model’s opaqueness.33 In complex operations, like those in the oil and gas industry, ingesting data from thousands of pieces of equipment into a single platform and scaling it across the entire operation can be a gigantic and almost impossible task.34 Implementing and scaling AI solutions here will demand maturity of enabling technologies too. In such cases, firms prefer to aim for impact rather than seek perfection.35 Exxon Mobil deployed an AI-enabled platform that accelerates project development, provides access to data from multi-cloud applications, quickens decision-making, and reduces the return-on-investment cycle.36 32 Julia Dressel and Hany Farid, “The accuracy, fairness, and limits of predicting recidivism,” Science Advances 4, no. 1 (2018), https://doi.org/10.1126/sciadv.aao5580. 33 Equivant’s COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) estimates the chance of a defendant reoffending and has been used by judges in several states for setting criminal sentences. Derek Thompson, “Should We Be Afraid of AI in the Criminal-Justice System?,” The Atlantic: The Presence of Justice, June 20, 2019, https://www.theatlantic.com/ideas/archive/2019/06/should-we-be-afraid-of-ai-in-the- criminal-justice-system/592084/. Julia Angwin et al., “Machine Bias,” ProPublica, May 23, 2016, https://www.propublica.org/article/machine-bi- as-risk-assessments-in-criminal-sentencing. 34 Matthew V. Veazey, “AI Concerns Tied to Scalability, Complexity Assumptions,” Rigzone, June 12, 2019, https:// www.rigzone.com/news/ai_concerns_tied_to_scalability_complexity_assumptions-12-jun-2019-159055-article/. 35 Bernard Marr, “The Incredible Ways Shell Uses Artificial Intelligence To Help Transform The Oil And Gas Giant,” Forbes: Enterprise Tech, January 18, 2019, https://www.forbes.com/sites/bernardmarr/2019/01/18/the-in- credible-ways-shell-uses-artificial-intelligence-to-help-transform-the-oil-and-gas-giant/. 36 Barry Samria, “Oil & Gas: Operators Slow In Adopting Game-Changing AI,” Audere Partners, March 9, 2020, https://auderepartners.com/2020/03/09/oil-gas-ai/.