An AI pilot is ready for a production decision when you can explain its intended users, measured quality, permitted actions, failure behaviour, operating owner and cost boundary. A successful demonstration is useful evidence, but it does not by itself establish reliable performance on real work or permission to process production data.
This is an advisory checklist for one workflow, not a certification or a report of completed customer delivery. For teams in India supporting users internationally, include the actual user locations, data-access constraints and service hours in the decision rather than assuming one operating model fits every market.
Review six decisions before expanding access
- Purpose and boundary. State the task, intended users and actions the system must never take. Name the person accountable for accepting the workflow, including any required human review.
- Evidence quality. Evaluate representative normal, difficult and out-of-scope cases. Preserve a test set that was not used to tune the system. Record errors, abstentions and unsupported answers, not just appealing examples.
- Data and access. Identify each source, who may retrieve it, retention expectations and how access changes propagate. Test whether a user can obtain information outside their role.
- Failure handling. Exercise unavailable dependencies, malformed input and ambiguous results. Define what the user sees, where exceptions go and when the system stops or falls back.
- Operating responsibility. Assign ownership of alerts, incidents, evaluation updates, model changes and rollback. A dashboard without a responsible person is not an operating arrangement.
- Release decision. Agree measurable acceptance criteria and unresolved risks. Choose limited rollout, further work or a stop decision based on evidence rather than a demonstration date.
Make the evaluation meaningful
Match measures to the actual task. A retrieval assistant may need answer support and correct refusal checks. A document workflow may need field-level accuracy plus review time. An action-taking agent also needs evidence that tool permissions and approval boundaries behave as intended.
Illustrative measurement: in a fictional set of 50 test questions, 40 produce supported answers, six appropriately defer and four contain unsupported claims. Report all three categories. Calling the 46 non-unsupported cases “accurate answers” would hide the distinction between answering and deferring.
No pass threshold is implied by this example. The business owner must agree what failures are tolerable for the specific use. Test results from a small sample do not establish performance across every language, document type or future model version.
Consider simpler alternatives
If the task follows stable rules, a deterministic workflow may be easier to evaluate and operate. If the problem is inconsistent source material, fixing the knowledge base may help more than changing the model. A limited assistant that drafts for review may be a more appropriate first scope than an agent allowed to change systems.
Google's engineering guidance emphasizes dependable pipelines, useful metrics and simple initial approaches. Microsoft's AI workload guidance considers the surrounding architecture and operational qualities. Use these principles to structure questions; neither framework proves that a particular pilot is ready.
Prepare the decision package
Bring a workflow description, redacted examples, current evaluation results, system/data diagram, access model and known failures. Record the unresolved decision and who can accept it. Initial enquiry forms should not contain credentials, confidential prompts or production records.
The DigiScience Solution Assessment can evaluate one defined readiness question and provide a recommendation and implementation brief. It does not include a working proof of concept or production deployment. Explore the existing governance controls and AI delivery practices for related scope.
Common questions
Does a good demo mean we can launch?
It supports further evaluation. You still need representative evidence, permitted access, a failure path and real operating ownership.
Must every pilot become a production service?
No. A useful assessment may recommend a simpler process, closing prerequisites or stopping an unsuitable use case.
How do we start a readiness discussion?
Describe the workflow and the decision blocking release. Agree the assessment scope and published terms before scheduling delivery.
Reference principles: Google Rules of Machine Learning and Microsoft AI workload guidance. Guidance checked 20 September 2026.