The book, “Artificial Intelligence for Business,” by Kamales Lardi, aims to provide an accessible overview of AI for business managers. While offering a high-level understanding, a closer examination reveals some oversimplifications and potential pitfalls in its approach.

The book presents a generally sound overview of AI, but some of the specifics require careful consideration. For example, the assertion that machine learning is exclusively tied to AI overlooks its past presence in business intelligence (BI). while the book acknowledges BI, it doesn’t fully explore the overlap. Many applications now attributed to AI, such as categorization and clustering, have been successfully implemented using BI for years. the key differentiator lies in the scale of data, which can lead to increased accuracy but also higher costs. Business leaders should therefore evaluate whether the return on investment justifies the use of AI over BI for specific problems. It’s crucial to remember that AI is a valuable tool, but not a universal solution.

The Ethical Imperative

“AI-driven automation does not substitute human labor completely … the human workforce will be able to focus on complex tasks.”

One of the book’s strengths is its exploration of ethics in AI. The fourth chapter effectively covers both the theoretical concepts and practical examples,making it a crucial read.The preceding chapter discusses AI in conjunction with other modern technologies. While the broad strokes are accurate, some details are questionable. For instance,the book mentions distributed ledgers as a key element of blockchain,but lacks a clear definition. While distributed ledgers offer value in supply chains and other areas, many implementations prioritize them for reasons other than the need for consensus, which can slow down business processes. The promotion of blockchain as a buzzword may overshadow these nuances.

The chapter on the future of work presents a mixed perspective.While “the author does make an excellent case for massively increased unemployment,” this is tempered by the argument that “AI-driven automation does not substitute human labour completely … the human workforce will be able to focus on complex tasks.” However, this argument overlooks a critical challenge.

Employees typically develop the skills to perform complex tasks by first mastering simpler ones. If AI handles the entry-level tasks, how will humans acquire the necessary experience to advance to more complex roles? Will companies invest heavily in training new employees for advanced tasks, or will they seek AI solutions that can automate those tasks as well, potentially leading to widespread job displacement? This is a question that demands serious consideration.

The remainder of the book outlines the steps required to expand AI adoption in business and highlights the potential benefits of engaging a consultant. the former is valuable, while the latter is a standard expectation.

Ultimately, readers should consider the author’s background and objectives. “Artificial intelligence for Business” offers practical insights from a consultant’s perspective, providing a real-world view that contrasts with more theoretical approaches. By keeping this context in mind, readers can gain valuable knowledge from the book.