AI for Work: Spotting Use Cases & Running a Project
The valuable workplace skill is not building AI — it is spotting where AI fits, and running an AI project from idea to result.
What you will learn
- Tell good AI use cases from bad ones
- Walk the lifecycle of a real AI project
- Apply a simple feasibility-vs-value check
What AI is good at — and what it is not
You do not need to build AI to be valuable at work. The in-demand skill is spotting where AI helps and running the project. That starts with an honest sense of what today’s AI can and cannot do.
| AI is great at… | AI is poor at… |
|---|---|
| Repetitive tasks on lots of data | Tasks with very few examples |
| Finding patterns and predicting | Common-sense reasoning and ethics |
| Sorting, ranking, recommending | Explaining exactly why it decided |
| Working tirelessly at huge scale | Anything needing true understanding |
How to spot a good use case
A task is a strong candidate for AI when it ticks these boxes:
- Repetitive — it happens often, the same way (so automating it pays off).
- Data exists — you have many past examples to learn from.
- Clear outcome — there is a definite right answer or measurable goal.
- Tolerant of some error — an occasional mistake is acceptable (not, say, life-or-death with no human check).
Worked example: a shop wants to predict which customers will cancel their subscription. Repetitive? Yes (every month). Data? Yes (years of past customers). Clear outcome? Yes (cancelled or not). Some error OK? Yes (you just call the at-risk ones). All four boxes ticked — a great AI use case. Compare “decide who to fire” — high stakes, little tolerance for error, full of bias risk: a bad use case for automated AI.
The lifecycle of an AI project
Real AI projects follow the same path, whatever the company:
- Define the problem — what decision will this improve, and how will we measure success?
- Get & prepare data — gather, clean and label examples (usually the longest, hardest step).
- Build the model — train and test it (the five steps from “Build Your First AI Model”).
- Deploy — put it into the real workflow where people or systems use it.
- Monitor & improve — watch its accuracy over time and retrain as the world changes.
# An AI project is a loop, not a one-off
# define -> data -> build -> deploy -> monitor -> (back to data/build)
#
# Rule of thumb often quoted in industry:
# about 80% of the effort is DATA (collecting & cleaning),
# only about 20% is the model itself.Note: Output: (No output — the takeaway is that most AI work is data work, and that a project is an ongoing loop you must monitor, not a one-time build.)
A simple value-vs-feasibility filter
When choosing between ideas, score each on two questions and start with the top-right corner:
- Value — if it works, how much does it help (time saved, money, happier users)?
- Feasibility — do we have the data, the skills and the budget to actually build it?
High value and high feasibility → do it first. High value but low feasibility → a longer-term bet. Low value → skip, however cool it sounds.
Watch out: The most common workplace mistake is using AI where a simple rule, a spreadsheet, or a small script would do the job better, cheaper and more transparently. “Could a clear rule solve this?” is always worth asking first.
Tip: You can be the person in the room who says “this is a great AI use case” or “this one isn’t” — and who knows the project will live or die on the data. That judgement is worth as much as the coding.
Q. Which task is the BEST candidate for an AI project?
✍️ Practice
- Score two AI ideas from your own work/school on value and feasibility, and say which you would do first.
- Pick a task and check it against the four “good use case” boxes — does it qualify?
🏠 Homework
- Choose a real process you know and write a one-page mini-plan using the five lifecycle steps (problem → data → build → deploy → monitor).