01
Bring approved knowledge together
Give the workflow a maintained source of answers, policies, and escalation boundaries.
Research-informed case study · Customer support
A clearer support queue, with people still in control.
What an AI-assisted support workflow can look like when approved knowledge, human review, escalation, and quality checks are designed together.
Starting point
Support teams handle repetitive questions, uneven case complexity, and pressure to give a useful answer quickly without losing quality.
Work delivered
A guided support workflow that brings approved knowledge, issue summaries, suggested next steps, escalation rules, and human review into the same operating path.
Useful result
One field study reported a 14% average productivity improvement, with larger gains for newer and lower-skilled agents. The result varied by experience and task, so it is evidence to test, not a promise to copy.
The workflow
Put assistance next to the decision, not in front of it.
A useful support workflow reduces searching and summarising while leaving the final answer, sensitive judgement, and difficult escalation with a person.
What changed
14%
more issues resolved per hour in one field study
02
Summarise the issue
Turn the incoming conversation into a concise context block so the agent can see what matters.
03
Suggest the next step
Offer a draft or recommended action that the agent can edit, accept, or reject.
04
Review and learn
Track quality, escalations, and failure cases so the workflow improves without hiding its limits.
The practical difference
Productivity is only useful when quality stays visible.
The practical lesson is not that every agent or ticket will improve equally. It is that the workflow should make good assistance easy to review and make poor suggestions easy to catch.
01
Faster orientation
The agent spends less time hunting through scattered information.
02
Safer suggestions
Approved sources and clear escalation rules set boundaries around the assistant.
03
Measurable quality
Resolution, rework, escalations, and feedback show where assistance helps.
What to carry forward
A useful next test is specific.
Use the pattern as a starting point. Choose one workflow, one owner, and one measure that will tell you whether the change is worth keeping.
- 01 Start with a narrow, repeatable support queue rather than every customer interaction.
- 02 Use maintained business knowledge and a clear owner for changes.
- 03 Keep sensitive decisions and unusual cases on a human path.
- 04 Measure quality and rework alongside speed.
Evidence and limits
Useful context, clearly labelled.
The 14% figure is from the field study “Generative AI at Work,” based on 5,179 customer-support agents. It describes that study's context and should not be treated as a general business outcome.
Read the NBER working paperHave a similar workflow?
Let’s work out what is worth testing first.
Bring the repeated work, uncertain handoff, or daily bottleneck. We will help you decide what to improve and how to keep a person accountable for the result.