Reading Guide: Applied Artificial Intelligence in Business

Most business people do not need to build neural networks, but nearly all of them now need to understand what artificial intelligence and big data can and cannot do for an organization. Applied Artificial Intelligence in Business: Concepts and Cases, by Leong Chan, Liliya Hogaboam and Renzhi Cao, was written for exactly that reader. Published by Springer in its Applied Innovation and Technology Management series (first edition dated 2022, about 368 pages), it pairs core concepts with application-oriented overviews and cases drawn from many sectors. This guide suggests how to work through it, whether you are studying alone, leading a team, or teaching a course.

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What this book is (and who it's for)

The publisher describes the book as an introduction for students to the concepts of big data and AI and their uses in business. It sets out to answer two practical questions: what are the main ideas behind AI and big data, and which applications are actually used in business settings? Every chapter includes discussion questions and a summary, and instructors can get supplementary materials such as answers and presentation slides.

Catalog and retailer listings show the range of application areas it covers, including machine learning algorithms for business, AI in marketing, financial services, human resources, accounting and auditing, healthcare, real estate, tourism and education.

It suits:

• Undergraduate and MBA students taking a course on AI, analytics or digital transformation.
• Managers and analysts who need a structured overview before evaluating AI projects or vendors.
• Instructors looking for a case-based text with ready-made discussion questions.

No programming background is required to follow the main ideas. Comfort with basic business concepts and a little statistics (averages, correlation, prediction) will help.

Before you start

• Pick one industry you care about, such as your own employer's sector, and keep it in mind as a lens for every chapter.
• Keep a running glossary. Terms like supervised learning, classification, clustering and big data recur throughout.
• Start a "use case log": for each application you read about, note the business problem, the data needed, the AI technique, and the risks.

How to read it

The book naturally divides into foundations and applications, so read it in two passes:

1. Foundations first. Read the opening material on AI and big data concepts and on machine learning algorithms carefully. These chapters give you the vocabulary for everything else. Use the end-of-chapter summaries to check your understanding before moving on.
2. Applications by relevance. The sector chapters can largely be read in any order. Start with the two or three sectors closest to your work or studies, then sample the rest.

Suggested paths for different readers:

• Marketing and sales professionals: foundations, then marketing, then financial services and tourism for customer-facing comparisons.
• Finance and accounting readers: foundations, then financial services, accounting and auditing, then real estate.
• HR and people leaders: foundations, then human resources, then education for learning-related applications.
• Healthcare and public-sector readers: foundations, then healthcare and education.
• Instructors: assign the foundations early, then let student groups each take one sector and present its cases using the chapter's discussion questions.

On a first pass, skim technical details of individual algorithms if they are not essential to your role. Focus instead on what problem each technique solves and what data it needs.

Key ideas to take away

• AI is a toolkit, not a single technology. Different business problems call for different techniques, from prediction to classification to pattern discovery.
• Data comes first. Big data and AI are closely linked; without suitable, well-managed data, even good algorithms disappoint.
• Applications are sector-specific. The same technique can look very different in marketing, auditing or healthcare.
• Cases build judgment. Working through real-world examples helps you recognize promising opportunities and unrealistic hype.
• Questions matter as much as answers. The discussion questions are designed to push you from understanding concepts to evaluating applications.

Practice and discussion questions

1. Choose one business process in your organization. Which AI technique from the book could improve it, and what data would you need?
2. How do applications of AI in marketing differ from those in accounting and auditing in terms of risk and accountability?
3. What makes a dataset "big data" in a business context, and when does that matter for AI?
4. Pick one case from the book. What were the measurable benefits, and what could have gone wrong?
5. How should a manager evaluate an AI vendor's claims? Draft five questions to ask.
6. Which ethical or privacy concerns arise in HR or healthcare applications, and how would you address them?
7. Where would you not recommend using AI, and why?

Pair it with

• Data literacy and basic statistics. A short course on interpreting data and model results will deepen what you take from each chapter.
• AI policy, law and society. Also in our store, Artificial Intelligence in the Digital Era: Economic, Legislative and Media Perspectives broadens the view beyond individual business cases.
• Hands-on Python for data analysis. If you want to try the techniques yourself, Python for Data Science, also on our shelf, is a beginner-friendly next step.

Closing thoughts

This book is at its best when you actively connect its cases to your own work. Read the foundations closely, follow the sectors that matter most to you, and use the discussion questions to sharpen your judgment, and you will be ready to talk about AI with both technical teams and executives. If it sounds right for you or your class, get Applied AI in Business on Amazon.

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Sources

• LexisNexis Shop: Applied Artificial Intelligence in Business (description, edition, pages, subject areas)
• Managementboek: Applied Artificial Intelligence in Business (description, series, publisher)
• University of Economics in Bratislava library catalog record