How agents are actually used in these scenariosSee what peers are doing, then decide where to start

Business and risk teams depended on the data team’s schedule for data, and researchers spent much of their time searching material and compiling reports, squeezing the time left for judgement.

Built a data Q&A assistant and a research report agent, connected to the internal data warehouse, producing structure and first drafts in the established templates.

Data requests moved from waiting in a queue to self-service; research went from writing whole reports to revising a first draft, and analysts put their time back into judgement and conclusions. Data and conclusions are used after confirmation by business staff.

Underwriting and claims material comes in many types and formats; manual checking is slow, and reviewers focus on different things.

Document understanding agents check completeness and extract key points, flag missing items and fields to verify, and hand the result to human review.

Reviewers moved from paging through files to checking the points the agent flagged, with more consistent criteria. Approval remains the reviewer’s decision; the agent takes no part in it.

When equipment stopped and the veteran engineer was not there, maintenance staff could only wait, and the same fault was retried from scratch each time; experience left when people did.

Consolidated years of maintenance records and process documents into a knowledge base agent searchable by machine model and fault symptom, so frontline staff can pull up past handling records on the spot.

New staff can look up past solutions instead of waiting for someone; veteran experience is fixed as a reusable company asset rather than staying with individuals.

Quality records, process change orders and non-conformance reports were filled in by hand, with many field and format requirements, and each report took considerable time.

Drafts are generated from the company’s existing templates, key measured data is entered and confirmed by quality inspectors, and the report enters the existing approval flow.

The filling burden moves to the system, with people only entering and confirming data; once templates are unified, inconsistent report formats also decline.

Campaign inquiry volume is several times the usual level, temporary outsourced staff fill the gap, a single wrong answer on campaign rules becomes a complaint, and training costs are repeated.

Built a customer service agent with product information, campaign rules and return policies as its knowledge boundary; it handles high-frequency standard questions first and transfers anything beyond that to a human.

Standard questions are answered by the agent first, and people handle only complex cases that need judgement; campaign rules come from one knowledge base, reducing complaints caused by inconsistent wording.

Many SKUs and frequent launches mean titles, selling points and detail pages must be rewritten per channel, and manual output cannot keep pace with launches.

Multiple versions of copy are generated from the product parameter and selling point library, published after operations review; content involving efficacy or absolute claims is blocked first by the rules layer.

Launch copy moved from writing item by item to bulk drafts plus human selection, and operations put their time into selling point strategy rather than moving words around.

Medical record standards have many items, quality control relies mainly on manual sampling, and follow-up reminders and rehabilitation guidance must be tracked patient by patient, with heavy duplicate entry.

Document understanding agents flag record completeness and standards issues and generate first drafts of follow-up scripts, all confirmed by clinicians before archiving.

Quality control moved from sampling to full-coverage prompts, archived after clinician confirmation; follow-up drafts reduced duplicate entry.

Student and parent questions concentrate on course selection, fees, schedules and examinations, and teachers and form tutors answer the same questions repeatedly.

Built a Q&A agent on school policies, course schedules and academic rules as its knowledge base; questions it cannot answer or that involve individual cases go to a person.

Repeated enquiries are handled by the agent first, freeing form tutors to deal with matters that need individual communication.

The same service question is answered dozens of times a day at the counter, wording depends on individual memory, and policy updates reach the frontline late.

The latest policy documents and service guides are the knowledge source; the agent gives guidance from them with sources attached, introduces no external content, and counter staff confirm before replying.

Answering is unified to one knowledge source; after a policy update the knowledge base is synchronised and there is no need to notify people one by one.

From land acquisition to settlement, drawings, contracts and site instructions went through a dozen versions; at settlement the figures did not reconcile, and finding an old version meant scrolling through chat history.

Document understanding agents compare contract clauses and mark version differences, and historical project material is made into a searchable knowledge base.

Contract review moved from reading every clause to checking the flagged differences and risk clauses; finding material no longer depends on scrolling records, reducing repeat work for legal and cost roles.

Shipment status, in-transit exceptions and settlement differences have to be checked across systems; customer service and operations go back and forth with dispatch, and exceptions are often known only when the client complains.

Connected shipment data for data Q&A, with exceptions alerted under preset rules and pushed to the responsible person.

Checking a shipment moved from asking someone to self-service; exceptions are found before the client complains, leaving more time to handle them.

Past contracts, due diligence material, case law and internal templates are scattered; finding references for a new engagement takes a lot of time.

Historical documents and templates were made into a searchable knowledge base agent, searchable by clause type, risk point and client industry; new contracts get difference comparison and key point prompts.

Finding references moved from digging through archives to searching by question; first drafts and comparison results are used after professional confirmation and do not replace professional judgement.