Integrating artificial intelligence does not mean adding chat to every product. The first step is to distinguish two goals: using AI to assist the development team or embedding it in a user-facing feature. In both cases, value depends on a clearly scoped problem and verifiable quality criteria.
Start with the decision, not the model
An internal draft that will always be reviewed has a different risk profile from a response sent to a customer or an action performed in a management system. Describe the inputs, outputs and consequences of error, then define what “good enough” means: accuracy of extracted fields, presence of sources, escalation rate, time saved or number of human corrections.
Prepare representative test cases—including incomplete, ambiguous and hostile inputs—before choosing a model and provider. Models, prompts and sources change, so evaluations must be repeated and versioned to compare quality, latency and cost, and to block a release that makes important cases worse.
Treat inputs and outputs as untrusted data
A model can receive malicious instructions directly from a user or indirectly from documents, emails and pages it must analyse. This is prompt injection. Instructions in the prompt alone do not form a security boundary.
Available actions must use structured parameters, server-side validation and authorisation independent of generated text. Output must not be passed directly to a shell, browser, file system or database; it must be validated and encoded for its context, while queries remain parameterised.
Limit privileges and consequences
Tools and agents should access only the data and operations they need. Irreversible or high-impact activities require explicit approval, and there must be a safe way to reverse or contain the action.
Human oversight is not a generic formula: define who reviews, which information they see, how quickly they can intervene and what happens when the result is incomplete.
Data, providers and responsibilities
Before the pilot, classify the data that may enter the system. Minimise what you send, remove secrets and credentials, and review the contract, subprocessors, retention, processing location and the provider’s data-use settings. “We use an enterprise service” does not automatically mean compliance.
Assign a system owner, an escalation owner and a procedure for disabling the system. The NIST framework recommends governing, mapping, measuring and managing risks throughout the lifecycle. In the European Union, the AI Act applies different obligations according to role and risk, so classification must be assessed for the specific case.
A pilot that produces evidence
A good first project runs long enough to encounter real cases but remains limited to one process, one user group, one authorised data set and one primary metric. Compare the outcome with the current process, record errors and manual interventions, then decide whether to extend, correct or stop.
AI becomes useful when embedded in an observable process with understandable limits and defined human responsibilities. Only after testing, security and governance does it make sense to consider extending it into production.