AI Applications
80 Ways AI Is Already Earning Its Keep
Grouped by industry, because the same technique looks very different in a factory and a hospital. Predictive maintenance on a loom and on an MRI scanner share the mathematics and almost nothing else.
A list this long is not much use on its own, though. The question worth answering is which one to build first — so that is where we start.
Start Here
How to Choose Your First Use Case
Six tests. A candidate that fails two of them is usually the wrong place to begin, however appealing it looks.
It happens a lot
A small saving per item only counts when the job runs all day. A job done two hundred times a day beats a hard job done once a week. The hard one looks better on a slide. It pays back less.
The data already exists
If the record is already there — in your ERP, your logs, your ticket system — you can start this month. If someone must go and collect it first, add six months before you have anything to test. Start with something else and come back to this one later.
Success is measurable
You should be able to say the number today and the number you want: hours per week, defect rate, stockouts per month. If nobody can say what good looks like, the project ends in a fight about whether it worked.
Being wrong is survivable
Start where a wrong answer costs a re-check. Not a rule breach, and not a lost customer. A first project has to build trust, so the cost of being wrong must stay small.
Someone owns it
One named person who wants this fixed, and who will act on what comes out. Without an owner you get a dashboard nobody opens. That is the most common way an AI project fails, quietly.
It ships in weeks, not quarters
Your first project should be live soon enough that people see it work. Four to eight weeks to a working build is a fair target. Keep the scope small for now; the big idea goes second.
By Industry
Ten Industries, In Depth
Each industry has its own write-up covering all eight use cases — what they need, what they cost, and which to build first.
Prediction
Puts a number on something that has not happened yet, using what came before.
Vision
Reads photos and video — to check, to measure, or to watch for one thing.
Language
Reads and writes text and speech — it can sum up, answer or translate.
Extraction
Turns paper and PDFs into clean data your systems can read.
Optimisation
Picks the best plan when time, cost and space pull against each other.
Detection
Flags what does not fit — fraud, faults, breaches — against a learned sense of normal.
Ranking
Scores and orders things: which lead, which product, which patient goes first.
ManufacturingPredictive Maintenance · Visual Quality Inspection · Demand Forecasting and 5 more.Read the deep dive →
Textile & ApparelStyle-Level Demand Forecasting · Automated Fabric Inspection · Marker & Cut Plan Optimisation and 5 more.Read the deep dive →
Retail & E-commerceProduct Recommendation · Market Basket Analysis · Abandoned Cart Prediction and 5 more.Read the deep dive →
Logistics & Supply ChainDynamic Route Optimisation · Real-Time ETA Prediction · Freight Document Automation and 5 more.Read the deep dive →
HealthcareMedical Image Analysis · Clinical Note Summarisation · Readmission Risk Prediction and 5 more.Read the deep dive →
BFSITransaction Monitoring · Loan Underwriting Automation · KYC & Onboarding Automation and 5 more.Read the deep dive →
EducationPersonalised Learning Paths · Dropout Risk Prediction · Automated Essay Scoring and 5 more.Read the deep dive →
Real EstateAutomated Property Valuation · Lead Scoring & Routing · Contract & Title Processing and 5 more.Read the deep dive →
SaaS & TechnologyChurn Prediction & Prevention · AI Support Assistant · Usage-Based Expansion Scoring and 5 more.Read the deep dive →
Media & EntertainmentPersonalised Content Recommendation · AI Highlights & Auto-Summarisation · Automated Video Editing and 5 more.Read the deep dive →
Common Questions
Choosing and Sequencing
The one that scores best on the six tests above. It runs often, the data is already in hand, the number can be checked, a wrong answer is cheap, one person owns it, and it goes live in weeks. In our work the right first project is duller than the one people want. That is why it works.
You can, and we would advise against it on a first job. Taking one use case all the way to live shows you where your data, your systems and your own processes break. That lesson makes the next two much faster and much cheaper.
The list is what we see most often. It is not the limit. Most requests turn out to be one of these seven patterns in another trade's words. A number guessed ahead, a document read, a list ranked, a plan made better. Tell us the problem. We will name the pattern, and say whether it is worth doing.
Work it out on paper first. We price the problem as it stands today — hours lost, waste, sales missed — and set that against the build and running cost. If the payback is not clear on paper, it rarely gets better in practice. We would rather find that out in a workshop than in month four.
Recognise your problem in that list?
Tell us which one and we will tell you honestly whether it is worth building, what data it needs and roughly what it costs.
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