A workflow improvement needs a clear definition of success. For an invoice approval, that might mean less waiting without more corrections. For an AI support assistant, it might mean useful suggestions that reviewers can verify. Start with the business decision the measurement will support.
Define the workflow and its baseline
Choose one process with a recognizable start and finish. For example, measure from a complete purchase request arriving to its approval or rejection. Record what counts as complete, which cases are excluded, and who owns the definition. Otherwise, changes in reporting rules can look like operational improvements.
Collect a baseline across ordinary work and exceptions. Keep queue waiting time separate from hands-on processing time. A faster form may make little difference if requests still wait for an unavailable approver. Segment results by process type or team when their workloads differ.
Build a balanced scorecard
- Flow: completed cases, time to completion and age of unfinished work.
- Quality: corrections, reopened cases and records missing required information.
- Effort: handling time, review time and exception investigation.
- Reliability: failed handoffs, duplicate actions and recovery time.
- Cost: operating spend divided by successfully completed cases.
Define the denominator for every rate. Report both the number of exceptions and the total number of eligible cases. Compare typical completion times with slower cases so that an average does not hide a persistent backlog.
"In the past, we managed software; today, we design autonomous processes that AI executes for us. The future belongs to those who automate not just the tasks, but the decision-making itself."
Connect business outcomes to technical signals
Google’s monitoring guidance identifies latency, traffic, errors and saturation as useful service signals. Apply these alongside business measures: a successful API response alone does not establish that the correct invoice reached the correct approver.
For AI-assisted work, label the result before counting it as successful. Track suggestions accepted unchanged, suggestions corrected, and cases escalated to a person. Include review effort when estimating saved work. Keep the evaluation examples stable when comparing a changed model, prompt or workflow.
Give each measure a decision owner
Review the scorecard with the people who operate the process. For each concerning trend, record the likely cause, the next change to test and a review date. Avoid collecting extra fields that nobody uses to make a decision.
Use these definitions when planning analytics and reporting or document approval workflows. The GreatWork.ai module catalogue lists Smart Dashboard and Smart Reports; evaluate a proposed configuration against your agreed measures before relying on its reports.
I look forward to seeing how these developments will improve service levels and customer satisfaction in the freight industry!