How Should Non-Functional Requirements Be Defined for an AI DA Workflow?

Functional requirements describe what an AI-supported delegated authority workflow should do, such as classify a bordereau, map fields or identify exceptions.

Non-functional requirements describe how the whole service must behave while doing it. They cover matters such as availability, throughput, response time, capacity, resilience, recovery, security and operating cost.

These requirements turn broad expectations such as “the service must be reliable” into testable targets. They should be derived from the business process and its consequences, not copied unchanged from another system or expressed only as a model response-time target.

Functional success does not prove service readiness

A workflow may produce accurate results in a controlled test and still be unsuitable for production. It may slow sharply when several large bordereaux arrive together, depend on an external model with an incompatible service window, or leave no practical way to recover partially processed records.

Non-functional requirements expose these conditions before go-live. Common dimensions include service availability, files or records processed per hour, maximum queue age, end-to-end completion time, concurrent users, peak file size, recovery time, acceptable data loss, audit retention and cost per transaction.

The requirement should describe the business service, not merely one technical component. A model response in two seconds has limited value if extraction, validation and downstream loading take hours. Similarly, an available application has not met its requirement if records are accumulating unseen in a failed queue.

Business demand determines meaningful targets

Start with the operating pattern. Identify reporting deadlines, month-end peaks, claims events, time-zone coverage and dependencies on underwriting, finance or regulatory processes. Use actual volume ranges where available and state uncertainty where they are not.

Then assess impact. A delayed low-priority enrichment step may be tolerable, while a stopped premium-processing queue close to reporting cut-off may need rapid recovery. Different parts of the same workflow can therefore have different targets.

Requirements should cover normal, peak and degraded operation. A degraded mode might route low-confidence records to a queue, temporarily use a rules-based mapping or defer non-essential enrichment. Manual fallback needs a credible capacity and a trigger; simply stating that staff will process everything manually may not work at production volume.

Representative testing exposes production limits

Tests should use realistic file sizes, formats, concurrency and dependency behaviour. Include bursts of submissions, malformed data, timeouts and unavailable downstream services. Measure the complete journey from receipt to accepted output, including human review where it is part of the design.

AI-specific testing should examine variable response times, rate limits and the behaviour of confidence thresholds under load. It should also confirm that retries do not create duplicate records or uncontrolled cost. Where a third-party service is involved, contractual commitments and observed performance should both inform the test.

Results need agreed evidence: throughput percentiles rather than a single average, queue age over time, recovery outcomes and the conditions under which the test ran. AI can help analyse logs or identify bottlenecks, but owners must decide whether the observed service meets the business tolerance.

Trade-offs and tolerances need named owners

Requirements interact. Higher availability may increase cost. A very low latency target can restrict validation or model choices. Retaining extensive evidence may support audit but increase storage and privacy obligations. The right answer is a documented balance, not the maximum target in every category.

Business owners should set impact and timeliness tolerances. Architecture, engineering, security and service teams should confirm feasibility and controls. Operations should confirm that review and fallback assumptions are workable. Decisions and exceptions belong in the production acceptance record.

After go-live, compare actual performance with the targets and review them when volumes, integrations or business criticality change. Non-functional requirements remain service controls, rather than documents completed once for a project gate.

Example

A hypothetical premium-bordereaux workflow passes functional tests on ten files. In production it must handle a concentrated month-end peak from many coverholders before a finance deadline.

The DA operations owner works with architecture and service teams to define maximum queue age, end-to-end throughput, availability, recovery time and a workable fallback. Tests use representative large files, concurrent arrivals and a simulated dependency outage.

The results show where capacity must increase and confirm when the service should enter degraded mode, giving owners evidence against the actual reporting window rather than a generic availability percentage.

FAQs

• Are non-functional requirements the same as an SLA?

  No. Non-functional requirements define the service qualities and test targets needed by the workflow. An SLA is an agreed service commitment and may contain only some of those measures.

• Should AI response time be measured on its own?

  It can be a useful diagnostic measure, but the governing target should normally cover the end-to-end business process, including validation, queues, integrations and required review.

• How should targets be set when volumes are uncertain?

  Use available ranges, scenarios and explicit assumptions. Test beyond the expected case, monitor actual demand after release and assign an owner to revise capacity targets when evidence improves.