AI monitoring: what to check after a workflow goes live

An AI workflow is not finished when it goes live. Inputs change, staff use the process in unexpected ways, suppliers update their services and outputs can become less useful. Monitoring gives the business an early warning before a small issue becomes normal working practice.

The monitoring plan should be agreed during implementation. It needs a business owner, a review routine and a clear response when the workflow moves outside its approved limits.

Monitor business outcomes first

Return to the problem that justified the workflow. Is work moving more smoothly? Are handovers clearer? Are staff spending less time on repeated preparation? Has the quality of the final result stayed acceptable?

Choose a small number of measures that the team already understands. A technical score is not enough if the workflow creates extra checking or moves work to another department.

Track corrections and exceptions

Record how often staff correct the output, reject it or use the fallback process. Group recurring errors by type. A rising correction rate may point to changing inputs, weak instructions, outdated source material or a supplier change.

Do not hide manual work in the measurement. If people quietly repair every output, the workflow may look successful while producing no real benefit.

Review access and data use

• Who can start, change or approve the workflow?
• Are former staff and unused accounts removed?
• Is the workflow receiving any new type of information?
• Are logs available and reviewed?
• Do retention and deletion settings still match the approved design?

Use the ICO AI and data protection risk toolkit where personal data is involved, and check the live guidance for changes.

Watch supplier and model changes

Cloud services can change models, features, limits, terms and default settings. Record the version or service configuration used during approval where possible. Subscribe to supplier notices and repeat representative tests after a material change.

The UK AI Cyber Security Code of Practice provides baseline security principles for organisations developing and deploying AI systems.

Keep a test set

Maintain a small set of ordinary, difficult and unusual examples that reflect real work without exposing unnecessary personal information. Run them at agreed intervals. Compare the result with the accepted standard and keep a record of changes.

Define warning levels

Not every issue needs the workflow stopped. Define what triggers observation, investigation, restricted use or full suspension. State who can make each decision and how staff will continue the work while the issue is investigated.

Set a review schedule

Daily operational checks may suit a newly launched workflow, while a stable low-risk process may move to weekly or monthly review. Keep a separate date for the broader owner review. The schedule should reflect risk, usage and how quickly a bad output could affect people or customers.

Review whether the workflow is still needed

A workflow can remain technically functional after the business process has changed. Include an owner review of purpose, cost, value and user feedback. Retire the workflow when it no longer solves the original problem.

Build monitoring into implementation

Blue Canvas can design testing, logging, review points and fallbacks as part of AI implementation and automation.

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