The repetitive tasks worth automating with AI are the ones that involve reading, sorting, summarising or drafting text at volume: replying to enquiries, logging calls, extracting invoice data, writing first drafts and routing requests. Below are 25 such tasks grouped by department, each with what AI does, what a person keeps and how hard it is to set up.
Tick the ones your team does every week, then use the scoring table near the end to pick a starting point. For the strategy behind choosing and sequencing projects, read how growing businesses should approach AI automation.
How do you know if a task is a good AI candidate?
A good candidate is frequent, rule-ish, text-heavy and low-risk if the first draft is slightly wrong. If a task fails any of those four tests, either keep a human firmly in the loop or leave it manual.
Plain automation (no AI) is still better for purely mechanical steps, such as copying a form submission into a spreadsheet. AI earns its place where the input is messy: free-text emails, PDFs, voice notes, chats.
Which sales and lead-handling tasks can AI take over?
The ones between an enquiry arriving and a rep picking up the phone. Speed matters most here, so these often pay back first.
- Instant first reply to new enquiries. AI reads the message, answers common questions (availability, rough pricing, location) and asks one qualifying question. Keep human: anything involving a quote. Effort: low to medium.
- Lead qualification and scoring. AI tags enquiries by budget, timeline and fit based on what the person wrote. Keep human: final judgement on borderline leads. Effort: medium.
- Call and meeting notes. Transcription plus a structured summary with next steps. Keep human: a quick check before it is saved. Effort: low.
- CRM field updates after calls. AI proposes stage, budget and close-date changes for one-click approval. Our playbook on using AI to automate CRM updates and call notes covers the setup. Effort: medium.
- Personalised follow-up drafts. Messages that reference what the prospect actually said. Keep human: send button and pricing details. Effort: low.
Across the Meta ad accounts we manage, click-to-message campaigns on WhatsApp and Instagram were usually the cheapest source of enquiries. The catch is that each conversation needs a fast reply, which is exactly where tasks 1 and 2 help.
Which customer support tasks are worth automating?
Repeat questions, sorting and summarising. Complaints and judgement calls stay with your team.
- Answering repeat questions from your own documentation. A chatbot that responds using your FAQs, policies and product information. Keep human: escalation for complaints and edge cases. Effort: medium.
- Ticket triage and routing. AI reads incoming emails and tickets, labels the topic and urgency and assigns the right person. Effort: low to medium.
- Reply drafting for agents. The agent gets a suggested response with the relevant policy linked. Keep human: always reviewed before sending. Effort: low.
- Conversation summaries on handover. When a bot or one agent passes a case to another, AI writes a three-line recap so the customer is not asked to repeat themselves. Effort: low.
- Review and feedback analysis. Group hundreds of reviews or survey answers by theme and sentiment. Keep human: deciding what to change. Effort: low.
What finance and admin work can AI handle?
Mostly data entry and reminders built from documents you already receive. Approvals and payments stay manual.
- Invoice and receipt data entry. Extract supplier, amounts, tax and dates from PDFs and photos. Our explainer on AI document processing for invoices, forms and contracts explains the validation steps that make this reliable. Effort: medium.
- Expense categorisation. Suggest account codes for transactions based on description and history. Keep human: month-end review. Effort: low to medium.
- Payment reminder messages. Polite, escalating reminders drafted from the invoice status. Keep human: tone on overdue accounts with key clients. Effort: low.
- Contract renewal tracking. Pull renewal dates and notice periods from agreements into a calendar. Keep human: the decision to renew. Effort: medium.
- Form and application checking. Flag missing fields, unreadable uploads or inconsistent answers before a person processes them. Effort: medium.
Which internal operations tasks suit AI?
Anything that turns scattered notes, files and numbers into something people can act on.
- Meeting minutes and action items. Summaries posted to the team channel with owners and dates. Effort: low.
- Internal knowledge assistant. Staff ask questions in plain language and get answers from SOPs, policies and past project notes. Keep human: owners who keep source documents up to date. Effort: medium.
- Report narration. AI turns a weekly numbers export into a short written summary of what changed and why it might matter. Keep human: interpretation and decisions. Effort: low to medium.
- Scheduling and booking coordination. AI handles back-and-forth over WhatsApp or email to find a slot, then books it in your calendar. Effort: medium.
- Data cleanup. Standardise messy names, addresses, phone formats and duplicates in a customer list. Keep human: approve merges. Effort: low.
How can AI help with marketing and content tasks?
As a fast first-draft and variation engine, with a person owning facts and voice.
- First drafts of routine content. Product descriptions, social captions, email newsletters and ad variations. Keep human: editing, facts and brand voice. Effort: low.
- Repurposing long content. Turn a webinar, podcast or long article into short posts, email snippets and FAQs. Effort: low.
- Ad creative variations for testing. Generate headline and copy variants to test against each other. Keep human: selection and compliance with ad policies. Effort: low.
- Image tagging and alt text. Describe product or portfolio images for search and accessibility. Keep human: spot checks for accuracy. Effort: low.
- Competitor and market monitoring. Summarise changes on competitor pages or industry news into a weekly digest. Keep human: deciding what matters. Effort: medium.
Which of the 25 should you start with?
Start with the task that scores highest on frequency and lowest on risk, not the one that sounds most impressive. Score each candidate from 1 to 5 on the columns below and add them up (reverse the risk score so lower risk scores higher).
| Criterion | What to ask | Example high scorer |
|---|---|---|
| Frequency | How many times a week does this happen? | Replying to new enquiries |
| Time per instance | How many minutes does it take a person each time? | Invoice data entry |
| Input messiness | Is the input free text, PDFs or audio rather than clean fields? | Call notes |
| Error tolerance | If the first draft is slightly wrong, is that cheap to fix? | Meeting minutes |
| Data sensitivity (reverse) | Does it involve health, ID or financial account data? | Social captions (low sensitivity) |
| Integration effort (reverse) | How many systems must it connect to? | Review analysis (none) |
Two quick decision rules:
- If a task scores high but touches sensitive data, run through a privacy review first. Our checklist of data privacy questions to ask before starting AI automation is a good template.
- If three tasks share the same data source, such as your inbox, build one shared foundation (capture, classification, logging) and add tasks on top. It is cheaper than three separate tools.
What does automating these tasks typically cost?
Many of the low-effort tasks can be handled with existing subscriptions and a workflow tool. Custom builds start at the "simple internal tool or automation" level, typically around US$6k–20k, and AI agents with several integrations typically start around US$40k. These are market estimates; your scope sets the real number.
The equivalent ranges we see in other markets are roughly ₹1.5–5 lakh or A$10k–30k for a simple automation, and ₹8–25 lakh or A$60k+ for an integrated AI agent. Plan for maintenance at roughly 15–25% of the build cost each year, because prompts, models and the systems you connect to all change.
What should you not automate yet?
Anything where a wrong answer is expensive, hard to spot or legally significant. That usually includes:
- Final pricing, quotes and discounts.
- Hiring and firing decisions, or screening candidates without human review.
- Medical, legal or financial advice to customers.
- Approving refunds, credit or payments above a small limit.
- Public statements during a complaint or crisis.
AI can still prepare information for these decisions. It just should not make them.
Frequently asked questions
Do I need custom software to automate these tasks?
Not always. Many tasks on this list can run on tools you already pay for, plus a workflow automation platform. Custom software makes sense when the task touches several systems, needs your own business rules or handles enough volume that per-task subscription fees add up.
How many tasks should we automate at once?
One or two to begin with. Getting a single workflow reliable teaches you how your data behaves, how your team reacts and what review steps you need. Then expand.
Will automating these tasks mean reducing staff?
For most small businesses, the result is that existing staff handle more volume and spend time on higher-value work, such as closing sales or serving customers. Whether to change headcount is a business decision, not something the technology forces.
Which department usually benefits first?
Sales and support, because they handle the most incoming text and the speed of reply directly affects revenue. If most of your enquiries arrive on WhatsApp, start there.
How do we measure whether an automation is working?
Compare a before and after on one or two numbers: time per task, response time, error rate or volume handled per person. Keep the measurement simple and check it monthly for the first quarter.
Pick one and prove it
Choose the task from this list that frustrates your team most often, measure it for a week and then automate it properly. If you want help scoping it, send us the task and we will quote a fixed price. You can also browse what we've shipped for other businesses.