AI or Conventional Automation: Which Should You Choose?

Distinguish exact rules from interpretation

Conventional automation follows explicit rules: when a defined event occurs, check known conditions and perform a specified action. It can transfer an order, calculate a total or send a reminder after an agreed period. You can normally explain the expected result from the inputs and the rules, then test whether the implementation follows them.

Generative AI can handle interpretation of varied language, retrieval-assisted answers and drafting where specifying every possible input as a rule would be difficult. The output may vary or be incorrect. A commissioning brief therefore needs to define acceptable error rates, evaluation cases, review requirements and permitted actions alongside the functional requirement.

Choose rules when the answer is already defined

If the business can express the required decision precisely, start by considering a rule-based approach. A paid invoice has a known status. A record either has a mandatory field or it does not. A system integration can copy an approved address from the source of truth without asking a model to reinterpret it.

Conventional automation still needs duplicate-event handling, retries, validation and ownership of exceptions. Adding a model does not remove those requirements. Where the inputs and required behaviour are already defined, compare the additional dependency, evaluation and operating cost against a rule-based implementation before including AI in the architecture.

Consider AI where the inputs vary

An enquiry may describe the same underlying problem in many different ways. A supplier may change the layout of a document. An internal question may use different wording from the relevant policy. AI can be evaluated for interpreting those inputs, proposing a classification, extracting candidate information or presenting a draft answer with supporting references.

Define the quality threshold for each part of the output. A category suggestion might be acceptable with staff confirmation, while an extracted account reference must match a valid record before it can be used. Establish how missing evidence, conflicting sources and unsupported requests are handled. Evaluate against labelled examples; the model's own confidence statement should not determine whether an action is authorised.

Separate interpretation from validation and transactions

Compare approaches against the whole workflow

Use the same representative cases to compare the manual process, rule-based automation and an AI-assisted implementation. Record false classifications, missed fields, correction time and exceptions alongside completion time. For a retrieval feature, check whether cited passages support the answer and whether access restrictions are preserved. Define acceptance thresholds before reviewing which approach performed best.

Compare integration access, software and model usage, monitoring, changing business rules and support ownership. Consider whether the client can rerun evaluations or change provider without rebuilding the surrounding workflow. An existing product may meet these requirements; bespoke development needs a reason grounded in the required integration, user experience or control over the feature.

Use consequences to set the boundaries

A search suggestion that a colleague can ignore has a different risk from an automated decision about a person, a financial commitment or a customer record. Define the consequences of failure and the actions that remain with an authorised staff member. The solution can gather information or prepare work without being allowed to make the final decision.

Specify a fallback for every important step. That might mean leaving a document in a review queue, marking a draft as incomplete or notifying the owner when an external service is unavailable. A process that continues safely when AI is unavailable is easier for a team to trust and support than one that silently stops or guesses.

Document the architecture decision

Record the approach selected, the alternatives considered and the evidence supporting the decision. Where output quality remains uncertain, commission a defined evaluation. Where the requirement and behaviour are already established, proceed with the implementation and acceptance plan. An assessment can resolve competing priorities or unknown feasibility, but does not need to precede every automation or AI integration.

In MP Software's First Steps Enterprises project, candidate details were already available as structured website submissions. We mapped and validated those fields, created candidate records through the JobAdder API and added failure notifications. Conventional integration addressed that requirement. An AI component would need a separate justification, such as interpreting an unstructured source that the existing flow could not process.

Useful resources and next steps

• AI consultancy to compare your options
• Bespoke AI development