AI Implementation Checklist
10 questions every team should answer before building AI systems. Also check your email — we've sent you a copy.
What specific business problem does AI solve?
Don't start with the technology. Define the problem in measurable terms: reduce response time, classify documents, automate a manual process. If you can't measure the impact, you can't justify the investment.
Do you have enough quality data?
AI is only as good as the data that feeds it. Evaluate: do you have access to the necessary data? Is it clean and structured? Do you have legal permission to use it? Without quality data, no model will perform well.
What happens when the model is wrong?
Every model makes mistakes. Define: what's the cost of an error? Do you need human review? Do you have a fallback plan? Robust systems assume the model will fail and design for it.
What is your evaluation strategy?
Before building, define how you'll measure success. Precision metrics, latency, cost per query, user satisfaction. Without clear metrics, you won't know if your system is improving or degrading.
Who will maintain the system in production?
A model in production needs continuous monitoring, updates, and cost management. Do you have an internal team prepared? Do you need external support? 80% of the cost of an AI system is maintenance.
Have you defined the limits of what AI can do?
Clearly delineate which decisions AI makes alone and which require human intervention. The most successful systems combine automation with intelligent escalation to people.
How will you manage API and token costs?
LLM costs can scale rapidly. Implement: token limits, response caching, model selection by task complexity, and cost alerts. A system without cost controls is a ticking time bomb.
Does your infrastructure support the latency requirements?
Define the response time requirements. Do you need streaming? Async processing? Job queues? The architecture must be designed around latency requirements, not the other way around.
Have you considered security and privacy?
What data do you send to external APIs? Do you comply with GDPR/local regulations? Do you have data retention policies? Have you implemented input sanitization to prevent prompt injection?
Do you have an iteration plan?
The first deploy won't be perfect. Plan improvement cycles based on real usage data. Implement logging, feedback loops, and A/B testing from the start.
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