AI Automation Readiness: What a Business Needs Before Implementation

Begin with a specific operating responsibility: classify incoming requests, extract fields from documents, draft a response, summarize an interaction, or help staff retrieve approved information. A defined job can be tested; a broad request to 'add AI' cannot.

The workflow should identify the input, expected output, acceptable variability, prohibited actions, and the person responsible for the result.

AI systems depend on access to relevant, current, and appropriately governed information. Scattered files, conflicting policies, poor data quality, or missing ownership will limit reliability regardless of the model selected.

• Identify the authoritative source for each type of information.
• Remove obsolete or conflicting documents from the approved knowledge set.
• Define which data may be processed and where it may be stored.
• Assign responsibility for maintaining source information.

AI output should enter a controlled workflow. Depending on the use case, this can include confidence thresholds, required citations, review queues, approval steps, action limits, logs, and a clear way to correct the result.

The higher the impact of an action, the stronger the human review and permission boundaries should be.

A useful pilot has a baseline and a practical measure: time to classify a request, percentage of documents requiring manual entry, response delay, completion rate, or number of escalations. The goal is an operational improvement, not the presence of AI itself.

Limit the first implementation to one workflow, a controlled group of users, and a defined review period. Use real examples, including difficult and incomplete inputs. The results should determine whether to improve, expand, redesign, or stop the initiative.