Your AI pilot worked. What changes when it meets the rest of the business?

A successful AI pilot is a useful starting point. It shows what a solution can do within the conditions tested. Before expanding it across the business, I would treat the next stage as a fresh information test.

The next department may use different sources, formats and working practices. Evidence from a carefully selected pilot collection cannot answer every question about those new conditions.

The task sounds similar. The information has changed.

Consider an illustrative scenario: a proposal assistant performs well using a curated set of current product information. It then reaches other departments, where pricing sits in spreadsheets, specifications arrive as scans and older guidance remains in shared folders.

The request to prepare a proposal sounds familiar. But the assistant now needs to interpret tables, extract text from scans and distinguish approved guidance from superseded material. Client details or confidential commercial information may also need different handling.

These differences give enterprise leaders and delivery partners a practical agenda for the expansion decision.

Check five things before expanding

1. Coverage. Include the formats and repositories the next department actually uses. Ask the team to identify the sources needed for its everyday tasks, including information outside the original pilot. Record any important gaps.
2. Readability. Check whether the system can extract the information needed from tables, scanned documents and other formats. Test whether the meaning survives extraction, including headings, units and relationships between values.
3. Authority. Agree who decides which source takes precedence when versions conflict. A recent edit date alone does not establish approval. Ask the information owner to identify current guidance and explain how historical records should be handled.
4. Sensitivity. Decide which details are appropriate for the task and its intended audience. Consider access permissions alongside the relevance and sensitivity of the content. Agree what should be excluded, masked or referred for review.
5. Evidence. Test representative questions with users from the next department. Check the answers against agreed expectations and inspect the sources behind them. Record incorrect answers, missing context and unresolved conflicts.

Test the boundaries as well as the answers

Include questions the system should decline to answer or refer to a person. What should happen when pricing conflicts, approval is unclear or the available information does not support a reliable response?

A useful test set includes routine questions, difficult cases and requests outside the intended scope. Business owners should review the results and agree which issues must be resolved before wider use.

Start with a defined workflow in the next department. Record its sources, owners, preparation decisions and acceptance criteria. After addressing the issues, repeat the relevant questions using the intended users’ access. Keep that evidence with the expansion decision.

Make the next information set part of the decision

Preparing information can include making scans readable, resolving duplicate copies and removing personal or sensitive content that the task does not require. AIAssure supports these preparation tasks through its built-in Optimise and Cleanse capabilities. Business owners and delivery teams still need to agree authoritative sources, appropriate use and the tests that demonstrate readiness.

For enterprise leaders and AI implementation partners, the next department’s information deserves its own evidence. What would you need to see before trusting your pilot beyond its original boundaries?

Adapted from Stewart Connors’ LinkedIn post. The scenario is illustrative and the checks are a proposed practical approach. Read the original post ↗