Which of My Jobs Can Use AI?
AI use cases in business usually get explained with dazzling examples: forecasting, image analysis, automation chains. The reality for a small or mid-sized business is far plainer. 🧩
Short answer: work carrying three conditions at once — repeating, text-heavy and requiring no judgement. Without all three, the gain is small and the risk is large.
Below: the three conditions, concrete use cases, work to stay away from, and how to build your own list. 📋
Which three conditions?
AI use cases in business get selected through these three filters.
What are the concrete use cases?
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- Quotes, correspondence and documents
- Summarising and pulling together
- Interpreting data and reading reports
The work that pays most in the field.
Quotes, correspondence and documents
Quote drafts, email replies, contract first drafts, product descriptions. The format is fixed and the content varies — where tools are most efficient. Quote preparation can drop to minutes. 📄
Summarising and pulling together
Long email threads, meeting notes, a pile of incoming enquiries. A question like “what did customers ask most this month” gets answered in minutes and feeds your content planning. 📚
Interpreting data and reading reports
Feeding it your existing report and asking questions: which product declined, which region grew. The tool doesn’t produce the numbers — it reads your report and interprets it. Measurement logic sits in the return article. 📊
Can it be used for customer replies?
As drafts yes, as direct sending no. A reply going out without human approval takes back most of what it saved.
Does the same job pay equally everywhere?
The same use case produces different value at different desks.
Volume and complexity
In a business issuing two quotes a day, a draft template is a small convenience; in one issuing twenty, it changes how the work runs. Likewise, a support line with standard answers and a consulting line where every answer differs won’t see the same gain. So “it works for that sector” is not enough guidance — look at your own volume. Gain pockets sit in the productivity article. 📐
Which work should be kept away?
Knowing the boundary protects the gain.
Three risky areas
1) Official document production — the cost of error is high. 2) Giving prices and commitments. 3) Unsupervised customer contact. In all three the gain is small and the risk large. The boundary sits in the rules article. 🚫
How do I build my own list?
One week of records gives months of right direction.
The hour hunt: a one-page table
Keep four columns for a week: job name, times per week, minutes each time, does it run on writing. The two rows with the highest weekly total that are text-heavy are your starting project. The sequence sits in the starting article. 📋
📝 Field Notes
At one client the top row of the hour hunt table was unexpected: reading incoming emails and routing them to the right person. Done dozens of times a day, counted as work by nobody. Once summarising and tagging were set up, it dropped to minutes. The job eating most time is usually the one nobody has named. 📋
📖 Quick Glossary
Hour hunt: listing repeating work with its time cost. Draft production: the first text prepared by a tool. Human approval: checking output before use. Templated work: fixed format, varying content.
⚡ Quick Summary
Three conditions: repetition, text, no judgement. 🧩 Best areas: quotes, correspondence, summarising, report interpretation. Stay away from official documents, price commitments and unsupervised contact. A one-week hour hunt builds your list.
🎯 Next Step
Let’s fill your hour hunt table together and pick the first two jobs: the digital audit is free. Scope on the consulting page. 📋
Frequently Asked Questions
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The gain is multiplied by repetition. Speeding up something done monthly is a small win; shortening something done five times a day changes your week. Frequency is the first thing to check. 🔁
Today’s tools are strongest with text: writing, summarising, reformatting, translating. If your work produces correspondence and documents, you’re in the right area. ✍️
Decisions carrying responsibility — price approval, customer allocation, quality acceptance — stay with people. The tool drafts, the human decides. Without that split, errors just get faster. ⚖️
One, two at most. Changing several at once splits the team’s attention and none becomes a habit. Show the gain on one and the second arrives willingly. All questions on the business AI consulting page. ✅
For number-crunching, the right tool is usually accounting or stock software rather than AI. AI helps interpret the reports those systems produce. Ask it to read the numbers, not to produce them.
It counts but may not be your priority. Visual production is fast, yet in most businesses the real time loss sits in text and correspondence. Pick the job eating most hours first; format comes second.
It can, and usually does. What matters is writing a separate setup and rule for each job: which information gets given, who approves the output. One tool, many jobs — but each job with its own rule.
Source: OECD — AI policy