AI Strategy & Implementation Readiness Checklist

Use this to identify a worthwhile AI opportunity, expose readiness gaps, and define a responsible path from idea to implementation.

Project Snapshot

Check an item only when it is confirmed or documented. Leave unknown or unresolved items blank.

Organization Prepared by Priority workflow or use case Date

Current stage

Exploring Use case prioritized Pilot underway Preparing to scale

1. Outcome & Use Case

A specific operational or business problem is defined; AI is not the goal itself.
An accountable sponsor, workflow owner, and target users are identified.
Current time, cost, quality, backlog, or risk has a usable baseline.
The use case is prioritized for value, feasibility, effort, and risk.

2. Workflow & Human Oversight

The current workflow, handoffs, decisions, exceptions, and approvals are mapped.
AI's role—retrieval, drafting, decision support, or automation—is explicit.
Scientific, safety, regulatory, and business judgment remains with qualified people.
Human review, escalation, exception handling, and manual fallback are defined.

3. Data & Knowledge

Authoritative systems, documents, SOPs, and subject-matter experts are identified.
Required data is accessible, current, sufficiently complete, and owned.
Rights and restrictions are confirmed for proprietary, licensed, client, and regulated content.
Outputs can be traced to current sources when citations or defensibility matter.

4. Risk & Governance

The use case is risk-tiered based on consequences of incorrect or unavailable output.
Operations, quality, EH&S, IT/security, privacy, and legal are engaged as needed.
Permitted uses, prohibited uses, approvals, and final accountability are documented.
Testing, records, auditability, and validation expectations match the risk.

5. Technology & Security

Build, buy, and configure options are compared against the actual workflow.
The tool is approved for the data, with acceptable retention and model-training terms.
Identity, least privilege, confidential-data separation, and access review are designed in.
Required LIMS, ELN, QMS, document, reporting, or database integrations are understood.

6. Pilot & Evidence

The pilot is narrow, valuable, representative, and safe enough to learn quickly.
Acceptance criteria and unacceptable failure modes are agreed in advance.
Test cases cover routine, edge, poor-input, and expected failure conditions.
AI-assisted performance is compared with the current process for quality, time, and risk.

7. Adoption & Operations

Business, process, technical, and support owners have clear decision rights.
Users help shape the workflow and understand how their responsibilities change.
Training, work instructions, change control, support, and communications are planned.
Monitoring, incident response, rollback, and controlled updates are assigned.

8. Value & Scale

Outcome measures and guardrails connect directly to the original problem.
Total cost includes data, integration, testing, training, support, and maintenance.
A decision gate defines when to scale, revise, pause, or stop.
The roadmap balances quick wins with governance, infrastructure, and organizational capacity.

Using Your Results

Unchecked items are roadmap inputs, not failures. Resolve gaps in ownership, data, oversight, security, and testing before increasing scope or autonomy.

Ready to turn the gaps into a practical AI roadmap?

I help life science and research teams select useful workflows, establish guardrails, design pilots, and implement responsibly.

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