100 practical ways to automate repetitive tasks across your work, business, and everyday life — organized so you can find yours in minutes.
No coding. No jargon. You don't need to understand how large language models work — you only need to recognize the shape of a repetitive task. That's where AI automation pays off.
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You're probably not unproductive. You're just spending your best hours on tasks that no longer need your full attention from start to finish.
Every one of those is a shape AI recognizes. The hard part isn't the technology — it's noticing which of your tasks qualify.
100 Things You Can Automate With AI is a field guide, not a technical manual. Each of the 100 entries names a task and explains, in a couple of sentences, what AI actually does in that automation and why it saves time.
The ideas are grouped into ten chapters of ten, moving from personal life outward to business and specialist fields — so you can read it front to back once, then keep it as a reference you come back to.
Real tasks people repeat every week, with a plain explanation of what gets automated.
Written for people new to AI automation. No coding, no model internals, no setup rituals.
Patterns outlive apps. Recognize the pattern, then search for the best current tool for it.
Ten chapters of ten, numbered 1–100. Skim the chapter that matches your day and start there.
From personal life outward to business and specialist fields.
The automations that quietly give back the hours lost to small, repetitive decisions.
Smart-home devices that learn routines, predict needs, and run the house with less input.
Removing the blank-page problem and the repetitive formatting work around it.
Dozens of small recurring tasks — posting, testing, reporting — with structured inputs and outputs.
Moving information between systems — and flagging the exceptions — without a human relay.
Every conversation generates data that can be used to improve the next one.
Repetitive, rules-based, high-stakes when errors creep in — the clearest return of all.
The employee lifecycle is a predictable sequence of documents, approvals, and messages.
Code and logs are highly structured data — which is why this tooling is the most mature.
AI as a patient, always-available collaborator for study, health, and making things.
Twelve entries pulled from different chapters. There are 88 more inside.
Urgent mail flagged, newsletters archived, routine replies drafted — you open a shortlist instead of a wall.
A note-taker joins the call, transcribes it, and pulls out decisions and next steps. Nobody gets assigned.
Deadlines, calendar, and stated priorities become a suggested order — re-sequenced as new work lands.
A day-by-day itinerary from a simple prompt, factoring in flight times, opening hours, and distances.
A passing car, a delivery, and an actual intruder get told apart — so you're only alerted when it matters.
A topic and a few bullets become a full first draft. You edit, fact-check, and add your perspective.
A webinar or podcast is scanned for quotable moments and cut into captioned social clips.
A plain-language summary of campaign performance instead of a spreadsheet full of raw numbers.
Vendor, amount, and due date extracted, matched against the purchase order, discrepancies flagged.
Staff ask a chatbot trained on internal docs instead of searching a shared drive for policy answers.
Applications ranked against the role's requirements, so hiring starts from a shortlist, not a stack.
Dozens of papers condensed, with the most relevant findings surfaced before you read in full.
This isn't about removing people. The book is explicit about it: keep a human in the loop for judgment calls. Anything involving nuance, sensitive relationships, or high stakes should keep a person reviewing the output.
Technical expertise isn't required for most of the ideas inside.
It's a practical idea and reference guide — written to help you spot opportunities, not to configure software for you.
The average person spends hours a week on small, repetitive decisions — what to do next, when to meet, what to read. This chapter covers the AI automations that quietly give that time back.
AI tools scan incoming email, flag what's urgent, archive newsletters, and draft short replies to routine messages, so you open your inbox to a shortlist instead of a wall of unread mail.
Scheduling assistants read everyone's calendar availability, propose times, and handle the back-and-forth of finding a slot.
Pages shown are reproduced from the actual PDF.
Specific apps and platforms change constantly, but the underlying pattern — the type of task being automated — stays useful far longer. So the book names the pattern, not the product of the month.
Once you recognize a pattern in your own life or work, a quick search for “AI tool for [that task]” turns up several current options to try. That's a skill that doesn't expire when a tool does.
Name the repetitive tasks that already annoy you. That's where the time savings will feel most real.
Read the list and circle the five or six items that map onto your life or work. Don't automate everything at once.
Search for a suitable current tool and start experimenting. Expect to tune it a few times before it runs smoothly.
“Start with annoyance, not novelty.”
— WHERE TO GO FROM HERE, P.14No. The book was written for people new to AI automation. You only need to recognize the shape of a repetitive task — the entries explain what AI does in plain language.
No. The introduction is explicit: you don't need to understand how large language models work under the hood.
No. The chapters move from personal life outward to business and specialist fields — the first two cover personal productivity and the home before business appears at all.
Deliberately not. It stays tool-agnostic because apps and platforms change constantly. It teaches you to recognize the pattern so you can search for the best current tool for that task.
Chapter 9 covers Software Development & IT — code generation, code review, test generation, documentation, helpdesk resolution, log monitoring, CI/CD, data pipelines, security scanning, and internal dev assistants.
Yes. Chapter 2 is entirely home and smart living, and Chapter 10 covers learning, health, and creative projects.
No. It's an idea and reference guide. Each entry names the task and explains what AI does in that automation and why it saves time — it isn't a build tutorial.
An introduction, ten chapters of ten numbered items each (1–100), and a closing “Where to Go From Here” with the principles for getting started. 14 pages in total.
Yes — that's who it was written for. It opens by welcoming readers who are new to AI automation.
A 14-page PDF you can read on a phone, tablet, or computer, and print if you prefer paper.
After payment you're taken to a download page with a “Download Your eBook” button. No account or login required.
Yes — the checkout sends you straight to the download page once payment goes through. Questions about your order: {{ contactEmail }}
You don't need to automate everything. Start with one repetitive task that frustrates you — then see what else becomes possible.
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