AI Implementation Starter Kit
A practical framework for evaluating where AI genuinely adds value in your product - so you invest where it counts.
Step 1: Map your pain points
Before looking at any AI solution, get clear on what problem you are solving. Ask yourself:
- What task takes your team the most manual effort each week?
- Where do errors or inconsistencies creep in?
- What would 10× throughput look like for your team?
- Which decisions do you make repeatedly with incomplete data?
Pro tip: Talk to the people doing the work every day. The best AI opportunities often come from the team, not the leadership deck.
Step 2: Evaluate AI fit
Not every problem needs AI. Score each candidate against these criteria:
| Criterion | Strong fit | Skip |
|---|---|---|
| Data availability | You have 1,000+ labelled examples | No training data or it would cost a fortune to create |
| Error tolerance | 85%+ accuracy is useful | Needs to be 99.99% correct (auth, payments, safety) |
| Scale | Manual effort grows linearly with volume | Current process handles volume fine - AI adds latency |
| ROI clarity | Every hour saved maps to a dollar value | Can not measure the impact in concrete terms |
Step 3: Choose your AI pattern
Once you have confirmed AI is a good fit, pick the delivery pattern:
Assistant / Chat
Best for: Users need to ask questions about your data or get guided help.
Classification & Routing
Best for: You need to sort, tag, or route content at scale.
Content Generation
Best for: You need drafts, summaries, or personalised copy - reviewed by a human.
Retrieval-Augmented Generation (RAG)
Best for: Users need answers grounded in your specific documents or knowledge base.
Automation Pipeline
Best for: AI triggers actions or decisions in your existing workflow.
Step 4: Build vs. buy decision
Before writing any code, validate that building is the right move:
- Does an off-the-shelf tool solve 80% of this? (Buy it.)
- Can you start with an API (OpenAI, Anthropic, etc.) before building your own model?
- Do you have the team to maintain this long-term?
- What is the switching cost if a vendor launches a better solution next quarter?
Step 5: Plan your pilot
The best AI projects start small and prove value before scaling:
- Pick one use case from your shortlist
- Define the success metric (time saved, accuracy, revenue lift)
- Build a prototype with real data in 2–4 weeks
- Run it alongside your current process for 2 weeks
- Compare results - if it wins, scale; if not, kill it and move on
Remember: The goal is to learn fast and fail cheap. Most successful AI products started with a focused pilot.
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This guide is part of the AI Implementation Starter Kit by Triweb AI.
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