Dec 23, 2025
AI Readiness Checklist: 5 Things to Do Before You Build With AI
AI is easy to demo and surprisingly hard to operationalize. A lot of teams jump straight to model selection, prompt experiments, or vendor evaluations. Then the initiative stalls in “pilot purgatory,” where usage never scales and trust never solidifies.
This checklist is designed to prevent that. If you’re building an AI implementation roadmap, do these five things first.
The 5-step AI readiness checklist
Before you build with AI, confirm you can answer “yes” to each:
We have a business problem with measurable outcomes (not just an AI idea).
We understand our data reality: sources, quality, access, and ownership.
We’ve defined where AI should not be used (risk, trust, compliance).
We’ve designed the workflow end-to-end (inputs → outputs → human review → action).
We have governance for rollout and iteration (monitoring, feedback, versioning, escalation).
If you can’t confidently check these off, that’s not failure it’s a signal your roadmap should start earlier in the lifecycle.
1) Define the business problem
Most AI efforts start with “Where can we apply AI?” A better start is: What’s the operational pain we’re solving, and how will we measure improvement?
Write this down in one sentence:
Today: what’s slow, manual, error-prone, or inconsistent?
Target: what should be faster, cheaper, safer, or more accurate?
Metric: what number will change if this succeeds?
If you can’t name the metric, you’ll end up optimizing vibes (and debating models) instead of outcomes.
2) Assess data readiness
“Do we have the data?” is rarely the right question. The real question is:
Do we have the right data in the right shape, with reliable access and ownership?
Fast, practical data readiness audit: Where does the data live (systems, silos, PDFs, tickets, docs)? What percent is structured vs. unstructured? Who can grant access and how long does that take? How often is it wrong, missing, or outdated? What’s sensitive or regulated (and what must never leave boundary)?
Design to reality. Don’t design to the PowerPoint version of your data.
3) Decide where AI should not be used
Teams lose trust in AI when it’s inserted into the wrong parts of a workflow, especially where risk is high and accountability is unclear.
Before you build, define: What decisions must remain human-owned? Where AI can assist (draft, summarize, classify, route)? ?What requires explainability or audit logs? What happens when confidence is low (fallback paths)?
This is not anti-AI. It’s how you keep adoption healthy.
4) Design the workflow before choosing a model
A lot of “AI strategy” stops at model choice. But the model is only one part.
The workflow is the product: Where does AI enter the flow? What does it receive? What does it produce? Who approves or edits it? Where does it get routed next? How do you handle exceptions?
At ConvoPro, we’ve found teams succeed fastest when AI is routed into real work (with guardrails, approvals, and feedback), rather than living as a separate “chat tool.”
5) Plan governance and iteration from day one
AI systems change. Prompts change. Data changes. Models change. Users change.
So your roadmap needs “how it improves” built in: Who owns quality and updates? What do you monitor (accuracy, escalation rate, time saved, rework)? How do users give feedback in the flow of work? How do you version changes safely?
AI systems are living systems. Treating them that way from day one avoids rework later.
A simple AI readiness scorecard (use this in your roadmap)
Score each category 1–5:
Business problem clarity
Data readiness
Workflow definition
Risk + controls
Governance + iteration
If any category is a 1–2, don’t build yet, fix that category first. That’s how you avoid spending months in pilot purgatory.
Next step: turn this checklist into a roadmap
If you’re evaluating AI initiatives or trying to turn pilots into production, clarity is usually the missing ingredient.
A good AI roadmap starts before you write any prompts or pick a model. If you want help turning these five steps into a practical plan for your org, ConvoPro can help you map the workflow, guardrails, and rollout path.
FAQ: AI readiness and AI roadmaps
What is an AI readiness checklist?
An AI readiness checklist is a structured way to assess whether your organization has the problem definition, data readiness, workflow design, and governance needed to deploy AI successfully at scale.
What should be included in an AI implementation roadmap?
Prioritized use cases tied to outcomes, required data sources, workflow design, risk controls, governance, rollout phases, and monitoring/iteration.
How do we avoid “pilot purgatory” with AI?
Define success metrics early, design the end-to-end workflow, validate data constraints, assign clear ownership, and establish governance and feedback loops before scaling.