A general-purpose AI assistant can suit drafting, research and internal exploration. A business-integrated chatbot may be worth assessing when users need answers from approved company information or must complete a defined task in a website, service channel or business system. Neither option is suitable for every workflow. Set requirements, test representative questions and confirm data handling, integrations, escalation and operating costs before choosing.
Write down who will use the system, what they need to accomplish, which information it may use and what a successful outcome looks like. Separate low-risk information requests from actions such as changing a customer record, confirming an order, booking an appointment or giving regulated guidance. Keep a human review step where a mistake could affect a person's money, rights, safety or access to a service.
A general-purpose assistant may be sufficient for staff drafting, summarisation, brainstorming or research when users can review the result and no live business record needs to change. Before using one with company information, read the current service terms and data controls, check whether prompts may be retained or used to improve services, and follow your organisation's information security policy. Do not enter personal, confidential or regulated information unless the service has been approved for that use.
Assess an integrated chatbot when a defined user journey depends on approved business content, controlled access to current records, or a bounded action in another system. Integration needs to be confirmed for the particular environment and permission model; an API or connector existing in principle does not prove that a required workflow is available, reliable or included in a proposal.
| Requirement | What to specify and test |
|---|---|
| Answers | Approved source documents, update owner, citation or link behaviour, and handling when no answer is found. |
| Actions | Permitted operations, identity checks, confirmation before changes, audit events and rollback or correction routes. |
| Escalation | Triggers for handing the conversation to a person, transfer of context, expected response time and out-of-hours handling. |
| Data | Data categories, hosting and subprocessors, retention, deletion, access controls and incident process. |
| Measurement | Representative test set, accuracy and completion measures, failure review and a process for updating content. |
Build a test set from real, anonymised questions and include ambiguous requests, missing information, outdated content, attempts to obtain restricted information, and requests outside scope. Check both the response and any action taken. Record failures, severity and the corrective change. Test again after changing prompts, documents, models or integrations. A polished demonstration is not a substitute for testing the actual configured system.
Map what personal data is collected, why it is needed, where it is processed, who can access it, how long it is kept and how a person can exercise their rights. Establish controller and processor roles, supplier terms, security controls and any required impact assessment with the organisation's privacy adviser. Do not describe a system as compliant solely because it uses encryption or a particular model.
For consequential decisions or regulated advice, the chatbot should not make an unreviewed decision or present generated text as professional advice. Tell users when they are interacting with automation, provide a usable route to a person and make the system's limitations clear before the user relies on the output.
Give each shortlisted supplier the same written scenarios and ask them to demonstrate them using test data. Request the proposed architecture, data-flow description, integration assumptions, security responsibilities, test plan, support terms, service limits and an itemised cost covering setup and ongoing usage. Confirm which capabilities are included in the specific proposal and which depend on later development or a third-party service.
Compare the total cost over a period that reflects your expected use. Include implementation, content preparation, hosting, model or usage charges, monitoring, support, maintenance and staff time. Treat projected savings as assumptions to validate in a pilot, not guaranteed outcomes.
It can be configured to retrieve information from selected documents, but accuracy depends on source quality, permissions, retrieval design and testing. Confirm that the system points users to the relevant source and admits when it cannot find a reliable answer.
Some implementations can call approved services to perform bounded actions. Confirm the target system's interface, permissions, identity checks, confirmation steps, audit trail and failure handling before treating the action as available.
Make the decision workflow by workflow. Measure demand, error impact, escalation needs and service expectations. A chatbot may handle a defined portion of routine requests, but a named person should remain responsible for oversight and exceptions.
Set a baseline and success criteria before launch. Track resolution, completion, escalation, correction and user feedback across a representative period. Review failures and costs as well as successful conversations, then decide whether to expand, revise or stop the pilot.
Softomate Solutions can help scope a chatbot assessment around your workflows, information sources and integration requirements. The first step is to establish whether a process change, existing tool configuration or a new build is appropriate.
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