Reading Guide: Artificial Intelligence and Technology (Sustainability in the Digital Age)

Artificial Intelligence and Technology: Systems Management, Decisions and Control for Sustainability in the Digital Age is a large edited collection published by Springer in 2026 as volume 645 of the Studies in Systems, Decision and Control series. Its editor, Bahaa Subhi Awwad of the College of Business and Finance at Ahlia University in Bahrain, has gathered roughly ninety chapters on how AI and related technologies are changing the way organizations manage systems, make decisions and pursue sustainability. With so many contributions, you need a plan. This guide gives you one.

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

This is a multi-author research volume, not a single-voice textbook. According to the publisher's description, it mixes theory with real-world applications and covers AI-supported decision-making, governance frameworks, and innovation in energy, healthcare, education and climate change mitigation. It also takes on harder questions about ethical AI, resource optimization and social equity.

Chapter titles on the Springer page show how broad the scope is. They range from a bibliometric study of AI in business, management and accounting, to digital technologies in agricultural marketing, machine learning for diagnosing cardiovascular disease, AI and decision quality in Jordanian commercial banks, and big data adoption in organizations.

The publisher names professionals, policymakers, academics and students as the audience. It fits especially well for:

• Business and management researchers studying AI adoption, digital transformation or decision quality.
• Policymakers and sustainability officers who want evidence on where AI is being applied to social and environmental goals.
• Graduate students in business, information systems or public policy looking for research questions and methods.

Most chapters are written for readers comfortable with academic writing. You do not need to code, but some familiarity with survey research, regression and bibliometric methods will help.

Before you start

• Pick your sector. Finance, healthcare, education, agriculture, energy and the public sector each deserve their own reading path.
• Know the common methods. Many management chapters use bibliometric analysis, questionnaires or case studies. Knowing how each works helps you judge the strength of a claim.
• Keep a findings log. Record the country or region studied, the sector, the method and the main finding. Because the book draws on many settings, context matters a lot.
• Bring a sustainability lens. Have a framework in mind, such as the UN Sustainable Development Goals, so you can map chapters to concrete outcomes.

How to read it

The Springer table of contents presents the chapters as a single long list rather than formal parts. Sort them yourself into reading tracks. The following tracks reflect the themes in the publisher's description and the chapter titles we could confirm:

Track 1: The big picture

Start with the review and bibliometric chapters, such as the study of AI trends in business, management and accounting and the one on big data adoption. These chapters map the field and help you place everything else.

Track 2: Finance, banking and business decisions

Read chapters on AI in banking, accounting and marketing next if you work in business. Look at how "decision quality" is defined and measured, since that idea runs through the book's subtitle.

Track 3: Health, education and public services

Chapters on clinical machine learning, telemedicine, e-learning and cybersecurity in education show AI in high-stakes settings. Pay attention to the limitations sections, which often matter as much as the results.

Track 4: Sustainability, energy, agriculture and climate

Readers mainly interested in the sustainability angle should go here early. Look for chapters that measure environmental or resource outcomes, not just intentions.

Track 5: Ethics, governance and equity

Finish with chapters on ethical AI, governance frameworks and social equity. Reading them last lets you test their recommendations against the applications you have already seen.

On a first pass, read the introduction and conclusion of each chapter in your track and pick a handful to study in depth. Skip detailed statistical tables until you know a chapter is relevant to you.

Key ideas to take away

• AI is a management issue, not only a technical one. Many contributions focus on how organizations adopt, govern and benefit from AI.
• Context shapes results. Findings from one country, sector or organization type may not transfer elsewhere. The book's international mix makes this easy to see.
• Sustainability needs measurement. The most useful chapters link AI to specific gains in efficiency, access or environmental impact.
• Ethics and equity are part of the design. Responsible use, fairness and governance appear alongside performance as conditions for success.

Practice and discussion questions

1. Choose two chapters from different sectors. What do their findings have in common about the conditions for successful AI adoption?
2. How is "decision quality" defined in the chapters you read? Could you measure it in your own organization?
3. Pick a bibliometric chapter. What can citation and keyword analysis tell you about a field, and what can't it?
4. Where does a chapter claim a sustainability benefit? Is it measured directly or assumed?
5. Which risks (privacy, bias, security, job change) come up most often across your track, and how do authors propose handling them?
6. If you were advising a government agency, which three chapters would you hand them first, and why?
7. What research question is missing from the volume that you would like to see studied?

Pair it with

• AI in business practice. Applied Artificial Intelligence in Business: Concepts and Cases, also in our store, offers a more structured, case-driven introduction that complements this research collection.
• Research methods for management. Survey design, structural equation modelling and bibliometric tools will help you evaluate and extend the studies here.
• AI governance and ethics. A grounding in responsible AI principles and emerging regulation will sharpen your reading of the governance chapters.

Find your track and begin

This book rewards selective, purposeful reading. Choose the sector you care about most, follow one track, and you will build a strong evidence base on how AI can support better decisions and a more sustainable future. Get Artificial Intelligence and Technology on Amazon, or browse more titles on the BooksPlusAi shelf.

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Sources

• Springer: Artificial Intelligence and Technology (Studies in Systems, Decision and Control, vol. 645)
• Apple Books listing
• Amazon product page