From Idea to Prototype
The biggest mistake in AI adoption is trying to do too much at once. A focused proof of concept (PoC) lets you validate an idea quickly and cheaply before committing to a full implementation.
Week 1: Define and Scope
Pick one use case
Not three, not five. One. Choose the use case that scores highest on: volume of tasks, consistency of the process, and tolerance for errors.
Define success criteria
What does "this works" look like? Be specific. "AI handles 60% of tier-1 support tickets with 90% accuracy" is testable. "AI improves our operations" is not.
Identify your data
What data does the AI need to do its job? Customer support logs, knowledge base articles, product documentation? Gather a representative sample.
Week 2: Build the Core
Choose your model
For most business use cases, start with a frontier model via API (Claude or GPT-4). Don't start with open-source models unless you have a specific reason.
Design the prompt
This is where most of the "AI engineering" happens. Write clear instructions, provide examples, and define the output format. Iterate based on test results.
Build the integration
Connect the model to your data source (usually via RAG). Keep the architecture simple — you can optimize later. An API call, a vector database, and a basic UI is enough.
Week 3: Test and Refine
Test with real data
Use actual historical examples. Compare AI output to what a human would have done. Track accuracy, edge cases, and failure modes.
Iterate on the prompt
Based on testing, refine your instructions. Add examples for edge cases. Adjust the retrieval strategy if the AI isn't finding the right context.
Get user feedback
Put the prototype in front of the people who will actually use it. Their feedback is more valuable than any benchmark.
Week 4: Evaluate and Decide
Measure against success criteria
Did you hit your targets? Where did the AI exceed expectations? Where did it fall short?
Calculate ROI
Time saved per task times number of tasks per month. Compare to the ongoing API and infrastructure costs.
Make the go/no-go decision
If the PoC met your criteria, plan the production implementation. If it didn't, you've learned something valuable at a fraction of the cost of a full build.
The Key Principle
A PoC should cost you 2-4 weeks and a few hundred dollars in API costs. If someone is proposing a 6-month AI project with no intermediate validation, push back. Prove value incrementally.