Use cases and workflows
My approach page is the framework, and my projects are working software built from it. This page bridges the gap between my projects and my approach, showing what that looks like applied to a real business function.
Across functions
For each business function below, the first question shows how AI is often used to make existing work faster. The second asks how prediction could change the workflow itself, whether that means preventing a problem earlier, improving the decision, or changing when the work happens. Each function applies the idea differently because each one is solving a different business problem.
Finance
Can we process invoices faster?
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Can we prevent bad invoices from entering the workflow at all?
The check moves from after the fact to before the payment happens.
Customer service
Can we answer tickets faster?
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Can we predict and prevent the issue before the customer has to call?
The goal changes from answering customers faster to preventing avoidable calls in the first place.
HR
Can we screen resumes faster?
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Can we predict role fit while reducing bias?
The focus moves from filtering résumés faster to improving the quality and fairness of the hiring decision.
Supply chain
Can we forecast demand better?
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Can we redesign ordering and inventory around live prediction?
Forecasting becomes a continuous input instead of a quarterly exercise.
Sales
Can we write outreach faster?
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Which customer needs which message at which moment?
The goal moves from producing more outreach to reaching the right customer with the right message at the right time.
Operations
Can we inspect defects faster?
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Can we predict defects upstream before they happen?
The focus moves upstream from catching defects to preventing the conditions that cause them.
Knowing when not to use AI
The instinct in most companies right now is to reach for AI first and ask what problem it solves second. The more useful skill, and the harder one, is knowing when the right answer isn't AI at all.
Question 1 of 8
Can you state the business problem clearly?
For example, a company may say that too many customers are calling and immediately assume it needs a chatbot, but call volume is a symptom rather than a diagnosis. The first question should be why customers are calling. If three questions account for most of the volume, a better FAQ or clearer confirmation email may solve most of the problem before AI is needed.
Building the chatbot first could leave customers with the same unanswered questions while adding another system the company has to pay for and maintain.
Before I'd recommend AI for something like this, I want to know:
- ·What's actually driving the behavior, not just its volume
- ·Whether a simpler, non-AI fix already gets most of the benefit
- ·What's left over once the simple fix is in place, and whether that remainder is actually where prediction or personalization would help
The same decision pattern applies across AI systems
Fraud detection, demand forecasting, predictive maintenance, computer vision, dynamic pricing, and recommendation systems all begin with the same basic structure. They use available data to predict something the business does not know yet, and then the business decides what action should follow. The examples above apply that structure to six different functions, but the value comes from the decision and workflow around the prediction rather than the technology alone.