What to Check Before Implementing AI
An AI tool depends on the process around it. A readiness review helps establish whether the required information, responsibilities, and operating conditions are in place before implementation begins.
Review one workflow at a time
Choose a specific process such as document intake, sales quoting, or weekly reporting. Identify the people who perform the work and those who rely on the output. A company-wide statement that the business is ready for AI is too broad to guide this decision.
Check the process, data, owner, and measure
First, compare how the team describes the workflow with how recent cases were handled. Note any important differences. Second, locate the information the solution needs and confirm that access and use are permitted.
Third, name the person responsible after launch. Fourth, define how improvement will be measured. A useful baseline could include handling time, correction rate, or completed requests per week. These checks provide evidence for the scope and implementation plan.
Investigate uncertain answers
“The data is in the CRM” does not establish that the relevant fields are complete or accessible. Inspect a sample and check the integration options. Similarly, an owner needs the time and authority to make decisions, not just a name on a project document.
Where staff handle a task differently, establish whether the variation is necessary. You may need process standardization before building an automation.
Turn findings into a delivery plan
Record the proposed use cases, prerequisites, estimated effort, responsible people, and success measures. Distinguish work that can begin from work waiting on access, a policy decision, or a process change.
Keep assumptions visible. An estimate based on a small sample should be labeled accordingly and revisited during implementation. The plan should also identify situations where the expected benefit does not support proceeding.
Use the findings to set the next step
Resolve gaps that would prevent safe, useful testing. You do not need to settle every future requirement before beginning a limited project, but its scope and boundaries should be clear.
A detailed AI Opportunity Assessment is one way to conduct this review. At Rock Creek, that work is separately scoped and priced after the free introductory call.
Questions and answers
Does a readiness review guarantee success?
No. It identifies dependencies and risks before implementation. The solution still needs appropriate design, testing, adoption, and ongoing maintenance.
Do we need perfect data before starting?
You need data suitable for the proposed task and a way to handle missing or unreliable information. Review quality and access for that use case rather than waiting for every company dataset to be complete.