AI for Everyday IT

Accelerate Workplace Productivity

What this book really is

AI for Everyday IT is best understood as a workplace operations guide for the AI era. It is not trying to be a research book about large language models, and it is not a narrow programming manual. The “about this book” section says very clearly that it is a practical guide to using artificial intelligence in IT operations, development, and management, and that it emphasizes core knowledge and practical applications rather than getting caught up in theory or chasing new products. That statement sets the tone for the entire book.

The authors also frame the book as a response to a real professional gap. In the preface, they explain that when ChatGPT became widely visible, many people either used it casually, struggled to get meaningful results, or failed to see its workplace potential. Their goal is to turn that confusion into competence. They explicitly present AI as a powerful but limited tool with specific capabilities and constraints, and they say the book’s primary strength lies in real-world applications across professional roles and technical levels. That is a very grounded starting point.

Who the book is for

One of the book’s biggest strengths is that it knows exactly who it is speaking to. The “about this book” section names several audiences: people new to AI, service desk specialists, systems administrators, database professionals, developers, DevOps engineers, managers, and IT professionals looking to advance their careers. That is broader than many AI books, which often speak either to developers alone or to business leaders alone. This book is clearly aimed at people who actually run systems, solve incidents, write scripts, manage infrastructure, support users, and lead technical teams.

That wide audience could have made the book unfocused, but the structure helps keep it coherent. The four-part layout moves from foundations into IT operations, then into development and integration, and finally into leadership and career growth. That means the reader can first build a shared vocabulary around AI and prompting, then apply those ideas in role-specific ways, and only later widen the lens toward management and advancement. It is a sensible progression for the intended readership.

The foundation: AI literacy for working IT people

Part 1 appears to do a careful job of building practical AI literacy. The early chapters cover generative AI, chatbot tools, basic intelligence concepts such as tokens and context, prompt engineering, prompt engineering in action, document handling, and workplace communication. The contents show that the authors do not stop at “what is AI?” They go on to explain why token limits matter, what statefulness and context retention mean, and how prompt construction, recursive prompting, explicit constraints, templating, and problem formulation improve results. This makes the book more serious than a simple “top prompts” guide.

A particularly strong feature here is the repeated connection between prompt engineering and professional control. In the chapter on prompts in action, the authors contrast asking the AI what to do with telling it what to do. The excerpt on project documentation shows that when the user provides structured inputs and a precise problem, the AI becomes less of a teacher and more of a tool. That is an important conceptual shift. It moves the reader away from vague chatbot conversations and toward purposeful AI-assisted work.

Early practical value: document and communication work

The contents also suggest that the book understands how much ordinary IT work depends on handling information, not just writing code. Chapter 6 covers summarization, format conversion, OCR-style text extraction from images, document comparison, classification, anonymization, and translation. Chapter 7 then focuses on email summarization and drafting, instant messaging, meeting transcription, integrations, automation, and writing improvement. That is important because these are some of the fastest areas where AI can actually change day-to-day productivity. The book seems to know that practical AI adoption in IT often begins with documents and communication, not with exotic model-building.

Middle of the book: where it becomes most concrete

The middle of the book is probably its most distinctive section because it moves into actual IT roles. Chapter 8 on support and service desk work looks especially strong. The excerpted material shows the authors using AI not only for technical support but also for emotional support and de-escalation. They describe service desk therapy, practice prompts for tense situations, technical troubleshooting, and then custom GPTs that can analyze ticket data and generate trends, insights, and recommendations. In one example, a custom GPT categorizes 1,000 service tickets, identifies the most common issue, and offers concrete recommendations around VPN, hardware, security, and network problems. That is exactly the kind of example that makes the book feel real.

The book then pushes further into future-facing service desk work by discussing AI agents. The chapter says these systems can take initiative, navigate systems, analyze data, and complete multistep tasks. It highlights ChatGPT Operator, Claude Desktop User, and Google Gemini Flash, then connects those ideas to platforms like Salesforce Agentforce and Zendesk AI. Whether those tools age well is a separate question, but as a snapshot of where the authors think the support desk is going, this section seems particularly strong. It treats AI not only as a chatbot but as a collaborator and, eventually, as a semi-autonomous coworker.

Systems, databases, and development

The later role-based chapters appear to maintain this same practicality. Chapter 9 covers systems administration tasks like change requests, multiserver administration, error-log analysis, automation, compliance, and auditing configuration files. Chapter 10 covers database administration, including query and object creation, optimization, documentation, heterogeneous database environments, maintenance jobs, memory analysis, and advanced administration prompts. Even the promotional material and epilogue excerpt emphasize maintenance prompts for SQL Server and Oracle, statistics updates, backup schedules, and schema-level maintenance. That suggests the authors are speaking from real operational experience rather than offering recycled generalities.

The development chapters also look practical rather than abstract. Chapter 11 compares code assistants like GitHub Copilot, Cline, Cursor, Project IDX, and Aider, and then lays out best practices such as starting with tests, using logging, managing AI-generated changes through source control, and breaking large problems into smaller ones. Chapter 12 adds a real-world DevOps example: upgrading nearly 7,000 tests with AI. That kind of case study matters because it shows scale, process, and limits instead of only showcasing toy examples. Chapter 13 then moves into AI-powered applications through function calling, database copilots, SQL validation, and practical application patterns.

The final section: unusually broad for an IT AI book

What makes the book more unusual is the final part on leadership and growth. Many practical AI books stop after operations or development. This one keeps going into conflict resolution, crisis management, policies and benefits, performance reviews, management interventions, team cohesion, interviewing, time management, and career advancement. That is a significant choice. It treats AI not only as a productivity layer for tasks, but as a tool that affects leadership, organizational politics, communication, and professional mobility.

The case study on leading AI adoption in the organization is especially revealing. Chrissy identifies three concrete AI applications: processing HR data into organized database entries, automating metadata assignment in SharePoint, and generating documentation from a codebase. The chapter then explains how sharing those wins, finding allies, planning infrastructure, and meeting security requirements helped build support for AI inside the organization. This is a strong example because it shows AI adoption as social work as much as technical work.

The career sections seem similarly practical. They cover project documentation, presentations, skills gaps, certifications, AI-powered job search, resume optimization, interview practice, and salary negotiation. The advice is not blindly enthusiastic. For example, the resume section explicitly warns that companies are getting better at spotting generic AI-written content and says AI should enhance a resume, not write it for you. That kind of caution strengthens the credibility of the book. It suggests the authors are trying to help readers use AI intelligently, not lazily.

Writing style and tone

From the front matter and excerpts, the writing style appears conversational, direct, and experience-based. The authors speak as practitioners, not theorists. The foreword reinforces that by describing the book as a roadmap for IT professionals and aspiring AI engineers who need to understand generative AI operations in order to streamline workflows from prompt to production. The preface also emphasizes experimentation, breakthrough moments, and immediately applicable strategies. This makes the book sound practical, slightly informal, and oriented toward real work rather than abstract authority.

Strengths

The biggest strength is scope paired with practicality. The book does not only explain prompting or only review tools. It moves across communication, support, systems, databases, development, DevOps, applications, management, and career growth while staying anchored in prompts, workflows, and examples. That breadth is rare, and it seems especially useful for IT readers whose responsibilities cross formal job boundaries.

A second strength is that it treats AI as a tool, not as magic. The authors repeatedly emphasize effective use, clear prompting, structured inputs, and grounded application. Even in career sections, they warn against overusing AI in ways that become generic or untrustworthy. That practical realism makes the book more durable than books that sell AI as effortless transformation.

Weaknesses or limits

The main limitation is that a book covering this many roles cannot go deeply into every one of them. Developers, DBAs, systems admins, support leads, and managers may each find sections that are stronger for them than others. The book’s strength is breadth, but breadth also means that some topics will remain introductions rather than exhaustive treatments.

A second limitation is time sensitivity. Because the book discusses current chatbot offerings, code assistants, AI agents, and rapidly changing platform features, some tool-specific details may age faster than the broader advice. The more durable value is likely to be in the prompting methods, structured problem formulation, role-based use cases, and pragmatic adoption mindset.

Final judgment

My judgment is positive. AI for Everyday IT looks like a very useful book for IT professionals who want to make AI concretely useful in their jobs. It is strongest when it reframes ordinary IT work—support tickets, documentation, scripting, database maintenance, test updates, policy writing, reviews, and career prep—as places where AI can save time and improve quality if used thoughtfully. It is not the deepest possible AI book, but it does not need to be. Its success lies in turning AI into everyday professional practice.

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