An AI demonstration can handle a few examples while leaving an important question open: what happens when incomplete data, ambiguous cases, or a provider failure arrives? Turning it into a reliable process requires defining how it is evaluated, who owns its outcomes, and what happens when it needs help.

This guide focuses on generative AI for business tasks such as classifying requests, extracting information, or preparing drafts. Progress depends on the complete process working within known boundaries, with verifiable outcomes and an alternative when AI cannot complete the task.

1. Define one task and the outcome the business needs

Start with a concrete unit of work. ‘Use AI in operations’ is too broad; ‘extract fields from a request so a person can prepare a new record’ identifies inputs, output, and an owner.

  • Input: which documents, messages, or data the system may receive.
  • Output: what it must produce and what makes the result usable.
  • Boundary: which cases are excluded and which actions it cannot perform.
  • Owner: who accepts the result and resolves exceptions.

Compare it with the current alternative: rules, search, a conventional integration, or manual work. AI should justify its place in the flow. Include the effort needed to review and correct its results.

2. Evaluate against representative work

Prepare an evaluation set separate from the examples used to tune the system. Include common cases, exceptions, missing information, and inputs that should be rejected. Use authorised data and minimise the personal information required.

  • Quality: are the fields, classifications, or claims correct for the task?
  • Relevant errors: which failure could cause the greatest harm, even if it is uncommon?
  • Escalation: does it send cases beyond its scope for review?
  • Operations: how long does a completed case take and cost, including review?

Agree acceptance criteria before seeing the results. Record differences by case type; a global average can hide failures in an important category. A model's self-reported confidence does not replace this evaluation.

The NIST AI RMF organises risk management around governing, mapping context, measuring, and managing. It provides a reference for assigning responsibilities and reviewing a system throughout its lifecycle; metrics must fit the specific use.

3. Decide what AI may propose and what it may execute

Preparing a draft, recommending a decision, and changing a system are different capabilities. Specify authorisation for every step. If AI interprets a request, the code applying the action must still check permissions, required data, and business rules.

  • Assistance: prepares an output for a person to review before use.
  • Bounded execution: acts on permitted, verifiable operations with explicit limits.
  • Exception: stops and escalates when information is missing or conditions are not met.

Human review needs context, time, and a real ability to reject or correct. Define who receives the task and what happens if nobody handles it. A review queue without an owner can become a bottleneck.

OWASP describes excessive agency as a risk associated with overly broad capabilities, permissions, or autonomy. Its advice to limit tools and privileges helps contain the impact of an incorrect output.

4. Protect the boundary between content and instructions

A document, page, or email can contain instructions intended to divert the model. The system should treat that material as input data and keep authorisation controls outside the model.

The OWASP prompt injection guidance covers direct and indirect attacks. Asking a model to ignore malicious instructions provides no sufficient guarantee: combine access restrictions, output validation, and controls over available actions.

Also check which information each user may access and which data is sent to the provider. When using document retrieval, filter access before retrieving content. Add tests with adversarial inputs and responses that attempt actions outside the allowed scope.

5. Move from a pilot to observable operations

Start with limited, supervised use. Expand scope once you have evidence about quality, review, and recovery. The decision should consider both correct outcomes and the work the team needs to sustain them.

  • Version the model, instructions, configuration, and sources so changes can be compared.
  • Record errors, corrections, escalations, and costs with the minimum information needed.
  • Define alerts, consumption limits, and a procedure for suspending the AI component.
  • Keep an alternative: manual review, a work queue, or a simpler operation.

Re-evaluate when the model, instructions, sources, or process changes. Avoid retries that duplicate actions: the system needs to distinguish a failed request from an operation that completed but whose confirmation never arrived.

An example: preparing supplier records

Illustrative example, not a client case: a company receives supplier forms and wants to prepare new records. During the pilot, AI extracts fields and flags missing data; a person checks the document before confirming the record.

The system checks formats and duplicates through rules. Ambiguous documents go to review. AI cannot change bank details or confirm a new supplier on its own. The team measures errors by field, review time, and pending cases before deciding whether to automate another step.

Checklist for deciding whether it is ready

  • The task, inputs, outputs, and exclusions are documented.
  • Evaluation covers representative cases, exceptions, and relevant attacks.
  • Acceptance criteria and unacceptable errors are defined.
  • Every action has permissions and controls independent of the model.
  • Human review and the operational alternative have an owner.
  • Quality, consumption, incidents, and version changes are monitored.

A reliable AI process is built through evidence and the ability to respond. The next investment should reflect what evaluation and operations have taught the team, with boundaries it can explain and maintain.

Codiloop can help you evaluate an applied AI use case and define a pilot with quality, oversight, and integration criteria before expanding its scope.