21 Deploying Artificial Intelligence ^Top Firms like Google, Uber, and Tesla have sufficient and credible data to create and implement an AI solution for autonomous vehicles. But upon leaping into high-risk domains using AI such as self-driving cars, they ran into accidents, some of which were even lethal.50 Despite their maturity in technological capabilities, they found it extremely difficult to build a fool-proof AI solution in certain areas. In order to be AI-ready, first, firms need to take time building data quality fundamentals into their data strategy, maintain an audit trail for keeping a check on biases, and obtain an independent, rigorous quality assurance by either an internal quality assurance department or a reliable third party.51 Second, domain knowledge factors that are unique to the incumbent’s busi- ness are to be considered to make a strong business case for AI. Straying from core competencies can be risky and wasteful. Third, defining boundary conditions should be done before building capabil- ities within the organization, to both develop and implement solutions. Else, biting off more than one can chew can cause disastrous and expensive results, like in the case of IBM’s Watson for Oncology.  50 Greg Bensinger and Tim Higgins, “Uber Suspends Driverless-Car Program After Pedestrian Is Killed,” Wall Street Journal, March 20, 2018, https://www.wsj.com/articles/uber-suspends-driverless-car-program-after-pe- destrian-is-killed-1521551002. Jeff Catlin, “How To Underwhelm With Artificial Intelligence,” Forbes: Forbes Technology Council, March 20, 2018, https://www.forbes.com/sites/forbestechcouncil/2018/03/20/how-to-underwhelm-with-artificial-intelligence/. 51 Thomas C. Redman, “If Your Data Is Bad, Your Machine Learning Tools Are Useless,” Harvard Business Review, April 2, 2018, https://hbr.org/2018/04/if-your-data-is-bad-your-machine-learning-tools-are-useless.