One thing I\u2019ve learned in the last several years is that, just like other high-profile company initiatives, AI isn\u2019t immune from corporate politics. The most prevalent example of this is people battling over AI ownership, a topic I covered in "The internal disruption of AI."\nBut ownership isn\u2019t the only hot potato being tossed between teams who recognize the potential of AI\u2014and the pitfalls. I\u2019m currently advising an after-market automobile products supplier on revamping its AI team. Digital innovations are rapidly changing the industry, and the phenomenon of autonomous vehicles seems to have captivated executives\u2019 imaginations, if not their budgets. My client was newly-deliberate about its AI roadmap, and in order to secure budget we were creating an incremental plan.\nThe AI team complained to me about a manager\u2014I\u2019ll call him Ken\u2014who had been labeled \u201canti-AI.\u201d Citing his disruptive questions and accusing him of having a penchant for drama, the team decided to exclude Ken from weekly AI progress calls.\u00a0\nAmidst all the exciting news about potential uses of AI it\u2019s tempting to dismiss naysayers as modern-day Chicken Littles portending AI\u2019s ability to send the heavens above into freefall. I advised the team to hear Ken out.\nI wasn\u2019t defending Ken\u2019s position. But AI has the potential to do harm\u2014remember Microsoft\u2019s racist chatbot of a few years ago?\u2014as well as good\u2014AI can enhance disease diagnoses and even spot abnormal cells in patients. The popular press celebrates the combination of AI and gene-editing technologies to cure disease. Conversely the prospect of \u201cdesigner babies\u201d has sparked fierce debate. The topic of AI\u2019s safety and ethics has gone from the nervous fringes to the boardroom.\nThat\u2019s actually a good thing.\nThe auto industry is uniquely poised to exploit technology innovation. Speech recognition, enhanced navigation, digital displays, night vision, warning systems, driver monitoring, and other advancements have enhanced driving comfort and safety.\nIt turns out my client\u2019s deliberate roadmap was the very thing needed to assuage Ken\u2019s fears. Since use cases for AI are so numerous (and increasing as algorithms and their users become more sophisticated), it\u2019s not AI as a class of tools but the context of discrete AI usage that matters. It was likely that recent news about failures in self-driving cars were making Ken particularly twitchy. Will AI and machine learning be used to distinguish physical objects like other cars, or humans? Or to enhance navigation systems? Or to display real-time traffic suggestions on the driver\u2019s windshield? Or to channel edge analytics for predictive maintenance?\nYou see my point: show me an industry and I\u2019ll show you dozens if not hundreds of use cases for artificial intelligence. It turns out we were able to overcome organizational resistance to AI by adopting three considerations into our planning:\u00a0\n1. What are the most promising use cases for AI?\nI name several above, but it\u2019s a good idea to actually list these opportunities as a team. Which ones align with strategic objectives? Which could help gain market share? Which could make operations smarter? We used a weighted scoring method to establish priorities, thus making the roadmap more tactical.\n2. How do we ensure AI safety?\nOnce you land on a few opportunities for AI, engage your analytics or data science team in describing the best type of algorithm for the job, and brainstorm ways it could be compromised. For instance, could a deep learning algorithm that recognizes red lights be hacked into thinking \u201cred\u201d means \u201cgo?\u201d What are the necessary measures to avoid such risks?\nAny AI application could have unintended consequences. While you might be reluctant to go through this process\u2014it could discourage some promising AI ideas\u2014it\u2019s a worthwhile exercise for engaging a cross-functional team.\n3. What\u2019s the goal for the AI project?\nBe clear about the desired outcome(s). Will candidate AI capabilities be additive? Are you after industry dominance or competitive parity? The continuum here could inform whether AI functionality is developed in-house or using a partner that has already proven the viability\u2014and the security\u2014of its AI deployment.\nI recommended including Ken in these conversations. After all his perspective, while a bit contrarian, could drive some interesting\u2014and in these days, increasingly necessary\u2014conversations.