Artificial Intelligence has been the leading topic in technology circles, financial publications, LinkedIn posts, and even dinner table discussions for the last two years since OpenAI released ChatGPT, a groundbreaking generative AI chatbot. While AI has existed for decades, Kavita Ganesan’s The Business Case for AI: A Leader’s Guide to AI Strategies, Best Practices & Real-World Applications came at the right time to latch on to the zeitgeist. It’s a book directed toward business leaders eager to ensure their companies aren’t missing the AI bandwagon, which many fear could be a fatal misstep.
Dr. Ganesan has extensive knowledge of AI and her credentials include a PhD from the University of Illinois Urbana-Champaign in Computer Science, specializing in applied AI, natural language processing, and machine learning. She has worked as an AI consultant for over a decade, helping businesses identify if AI is the right tool for them and setting them up for success as they implement their AI initiatives.
This book is written from a veteran consultant’s perspective and lists out the how-to’s for businesses thinking about starting an AI initiative within their operations. Ganesan focuses on creating rationale and rubrics for almost every step of the way, from deciding if AI is the right tool for your company (surprisingly, often calling out false-AI opportunities that would best be addressed by simple software automation) to setting up continuous maintenance for your tool.
The book provides a great introduction to the different types of AI available, explains the myths surrounding AI, and even touches on leaders attempting to ‘use AI for the sake of AI’. Ganesan pulls no punches when she in no uncertain words states that AI is a long-term outlook, rather than short-term impact, it has massive costs for hiring qualified employees and computing power, and that ROI of AI cannot and should not be measured in dollars but instead in process efficiency.
While the book touches on the importance of preparing a business’s culture to accept AI, it vastly underrepresents the downstream impact of incorporating AI into a business’s operations. It mentions that you can see which engineers can be transitioned over to ML engineers when the need arrives, but doesn’t mention the countless jobs that would be affected such as data analysts and customer service reps. Ganeson does mention some shocking related stats:
“According to a survey conducted by the University of Oxford’s Center for the Governance of AI, Americans fear a future where AI becomes too intelligent. When people were asked what kind of impact machine intelligence would have on humanity, 34% thought it would be negative with 12% leaning toward human extinction! These responses indicate that there’s going to be resistance toward the adoption of AI, not just by customers but also internally by employees.” (p.145)
This cultural readiness section is relatively small and specifically focuses on spurring leaders to educate employees about what AI is and does in order to reduce their fear. Ganeson also takes the opportunity to address for the first (and last) time the ethics and accountability aspect of using AI–a section which is only 1-½ pages long, most of which is spent recounting stories of large corporations like Meta overstepping data protection and regulation laws and how large their fines were. It pinpoints that a focus on not losing customers due to your AI decisions is important:
“If you want to maintain good standing with customers and society at large, it’s important for companies to take a strong stance on where they stand in terms of data privacy, ethics, and accountability.” (p.151)
But this is not enough. We need more guidance and louder voices about ethics and integrity as businesses incorporate AI. While this book is geared toward business leaders making strategic decisions for their shareholders or private boards, there must be more thoughtful discourse and decision-making surrounding how AI will affect employees and customers directly and indirectly. It shouldn’t be the fear of fines or losing customers that prevents a company from pushing facial recognition software onto their product–it should be driven by deep discussions around the ethics and integrity of the company and where the technology lands within that company’s guardrails.
This book provides business leaders with no previous knowledge of AI a hand to hold throughout the stages of learning about AI, identifying opportunities, building out AI initiatives, measuring RO(A)I, and continuous improvement and maintenance. The downloadable assets are extremely useful for those looking to handle their first AI initiative. But this is not a book for first-line managers, as some of the book blurbs mention. This information is for leaders much higher up in organizations, with access to budgets and wide perspectives on the company’s near-term and long-term future.
But while this book may not be for first-line managers, it can still provide them with the knowledge of how their business leaders are thinking about these decisions. As a manager, understanding business priorities and strategic thinking is important and can help them communicate new initiatives to their team members and align their team members more easily when AI initiatives inevitably impact them.
Ganesan, Kavita. The Business Case for AI: A Leader’s Guide to AI Strategies, Best Practices & Real-World Applications. Opinosis Analytics Publishing, 5 Apr. 2022.
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