01
Define the outcome
Choose one service queue, one customer promise, and one owner. Start with “help an agent prepare a grounded response,” not “replace support.”
Measure: handling time, first-response quality, escalation rate.Business use cases
Find the value chain before you find the tool.
AI creates value when it improves a real activity: sensing demand, making a decision, serving a customer, moving work between teams, or learning from outcomes.
A use-case lens
Five ways AI changes a business process.
Most use cases combine more than one pattern. The right design usually starts small and increases autonomy after evidence accumulates.
01 / Sense
Find signals
Detect anomalies, classify requests, extract fields, summarize events, or forecast what may happen next.
02 / Understand
Make knowledge usable
Retrieve policy, research, customer history, or operational context so people can make better decisions faster.
03 / Create
Draft the next artifact
Generate an email, report, proposal, code change, plan, or explanation that a person can review.
04 / Decide
Recommend an action
Compare options under constraints, explain trade-offs, and route a recommendation to the decision owner.
05 / Act
Execute a workflow
Use tools to update records, schedule tasks, open tickets, or coordinate work with explicit permissions.
06 / Learn
Improve the operation
Capture feedback, corrections, exceptions, and outcomes so the process improves instead of repeating the same failure.
Industry examples
Where the patterns show up.
Manufacturing
Quality and maintenance
Vision models identify defects; predictive models flag equipment risk; agents assemble maintenance context for technicians.
Student question: What is the cost of a false positive versus a missed defect?
Healthcare
Documentation and navigation
Speech and language systems draft notes, retrieve guidance, and route cases while clinicians retain responsibility for care decisions.
Student question: Which decisions are assistive, and which are high-consequence?
Finance
Risk and exceptions
Models score anomalies; language systems explain policy; analysts review the cases that need judgment.
Student question: Can the organization explain why a case was escalated?
Retail
Merchandising and service
Forecast demand, personalize recommendations, summarize customer history, and support returns without hiding the rules.
Student question: What changes if the recommendation is wrong?
Software
Agentic development
Repository context, tests, and specialized agents can help teams plan, build, validate, and document software changes.
Student question: What evidence proves the change is safe to merge?
Logistics
Planning and disruption
Combine supplier, inventory, route, and weather signals to propose alternatives and route exceptions to planners.
Student question: Which constraints are hard rules and which are preferences?
Worked use case · step by step
Support-ticket resolution assistant.
Follow one complete example. The goal is not to automate support end to end; it is to reduce repetitive work while keeping the customer promise and sending authority with a person.
Business outcome: reduce median handling time and improve first-response quality without increasing incorrect promises, policy violations, or escalations.
02
Map today’s process
Observe how a ticket moves from intake to resolution. Record the systems, handoffs, repeated lookups, exception types, and points where an agent must use judgment.
Deliver: a current-state process map and a list of 20 realistic tickets.03
Assemble the context
Retrieve only the customer history, product facts, service policy, and approved response patterns needed for that ticket. Keep source links and permissions attached.
Design: a context policy for freshness, access, and source citations.04
Choose the model mix
Use embeddings or search to find relevant policy, a language model to draft and explain, and deterministic rules to check required fields, refunds, or prohibited claims.
Compare: a smaller fast model for routine tickets with a stronger model for exceptions.05
Design the human gate
The assistant drafts, cites its evidence, highlights uncertainty, and asks for missing information. The support agent edits and approves before anything is sent.
Control: no automatic external message, refund, or account change in the first pilot.06
Evaluate realistic cases
Test normal tickets, edge cases, outdated policy, missing customer data, prompt injection, and requests that should be refused or escalated.
Score: correctness, groundedness, time saved, cost per successful ticket, and safe escalation.07
Scale by evidence
Improve retrieval, prompts, routing, and policy checks from agent corrections. Increase autonomy only for low-risk actions with a clear rollback path.
Monitor: drift, overrides, complaints, cost, and changes in policy or product.TicketCustomer request enters the support queue with identity and permissions.
RetrieveSearch policy, product documentation, account history, and prior approved answers.
DraftGenerate a response with citations, missing-information prompts, and an uncertainty flag.
ReviewSupport agent checks evidence, edits the response, and approves or escalates.
LearnLog corrections and outcomes so the team can improve the system safely.
Use-case canvas
Write this down before building.
If the answers are vague, the idea is still a theme—not a business case.
CustomerWho experiences the improved outcome: employee, customer, supplier, or citizen?
TaskWhat specific decision or activity becomes faster, better, cheaper, or safer?
InputsWhich data, documents, systems, permissions, and people are required?
OutputWhat artifact, recommendation, classification, or action does the system produce?
MetricHow will you measure quality, time, cost, adoption, risk, and customer impact?