Seven questions to ask before AI takes on the work.

Imagine a demonstration with ten successful customer enquiries. The agent understands each request and gives a clear answer. These illustrative examples establish something useful about those interactions. They leave open what happens when information conflicts, a system is unavailable or the requested action exceeds its authority.

ellio connects knowledge and predictive intelligence to judgement about the action, timing and channel, followed by digital-first delivery with human expertise. A buying decision should examine that complete service. The following questions help a buyer assess what informs the interaction, how it proceeds and who takes responsibility for the result.

Start with a specific customer need and the work required to meet it. Answering a question, submitting a request and completing an account change are different responsibilities. Establish the starting point, the actions in scope and the point at which the service may regard the task as complete.

Ask to follow one example from the customer's request through the underlying system action and subsequent outcome. In an illustrative delivery enquiry, an accurate estimated date may resolve the question. If the customer requested a change of address, repeating that date leaves the task open. Agree the measure that distinguishes a useful interaction from successful delivery, including what should happen when completion depends on another team.

Ask how customer records, conversation knowledge, service events and approved guidance inform the response. Establish which information is current and permitted for the purpose. Where predictive intelligence contributes, ask what the estimate means. Propensity to buy describes likelihood; judgement must still establish whether the offer, timing and channel fit the customer.

Follow a recommendation through a change in circumstances. Does the service use the customer's correction or persist with an outdated assumption? Ask to see how a conflict or gap becomes visible and how human expertise helps resolve it. The useful evidence is a recommendation that can be explained in the customer's context and revised when that context changes.

Separate access to information from authority to act. Ask which actions are permitted, under what conditions and within which limits. The answer should identify the client decisions that establish those permissions, the people who approve changes and the situations that require specialist judgement.

Test the boundary with a plausible customer request. If an agent can update an address, what prevents it from changing an unrelated account detail? If a requested adjustment falls outside the agreed scope, what does it do next? Include failed and uncertain actions: a timeout should not be treated as proof that a change succeeded. Look for a service design that can establish the actual state before repeating consequential work.

In ellio's service, a digital agent can query a human expert while retaining the customer interaction. When a person must take over, the agent transfers the context and continues assisting the human with knowledge and guidance. Ask to see both situations. Clarifying a question and transferring responsibility serve different purposes.

Check who provides the expertise, which decisions require a handover and what happens when the appropriate person is unavailable. The receiving human needs the circumstances, prior advice and outstanding work, with authority to question the recommendation. Capacity matters for expert consultation as well as transferred cases. The customer needs continuity and an accurate account of what happens next.

Ask how the evaluation covers the recommendation, the conversation and the completed action. Include digital voice where it is in scope, expert consultation within the interaction and required handovers. Useful examples include customer corrections, missing information and failed actions. Check that continued digital guidance helps the human exercise judgement rather than merely repeat an earlier recommendation.

Keep development examples separate from evaluation where practical, and review performance in the situations that matter to your service. An overall score can conceal a weak result on consequential exceptions. Assess predictive performance and the intervention's additional benefit separately. Ask what remains uncertain, and agree the evidence needed to support the scope being purchased.

The operating team needs a usable record of the information presented, guidance applied, actions attempted and resulting state. Ask who can inspect an incident, change an instruction, restrict a capability and stop affected work. Establish the fallback and who can authorise a restart after checking the correction.

Walk through a change to a connected component or a newly discovered wrong answer. Who notices? Which cases can be identified? How are customers with unfinished work helped? Evidence and controls should remain useful across the provider and client boundary. A list of technical features does not establish whether the people running the service can respond effectively.

Compare the cost of achieving the customer outcome with a credible baseline. Include connected knowledge, predictive intelligence, integration, digital delivery and the human expertise used during and after the interaction. Establish how demand, expert consultation, transfers and repeated attempts affect the complete service cost. A low channel cost can conceal work elsewhere.

Keep the benefits distinct. Released capacity, avoided expenditure, additional contribution and improved customer outcomes require different evidence. Ask what must happen operationally for each proposed benefit to become real, who owns that step and when it can be measured. A low interaction price can be useful, but it cannot establish the value of a service whose unfinished work returns elsewhere.

Take one realistic use case to the next meeting. Ask to see the intelligence behind the recommendation, a digital interaction with expert consultation, and a case that requires a human to take over. Follow the outcome back into the knowledge used for later decisions. Keep assumptions and responsibilities visible.

Use the result to define a scope the evidence can support. Some gaps can be closed through configuration or additional testing; others require client decisions, better data or more operational capacity. The next commitment should reflect what is established and what remains to be demonstrated.

Record the outcome as a short decision note: the task, intended benefit, permitted actions, evidence reviewed, unresolved conditions and accountable owners. Include the cost of closing those conditions and the point at which the decision will be revisited. This gives procurement, operations and technology colleagues a common basis for assessing the proposal, and a way to recognise if the proposition changes during implementation. Specific contractual and regulatory requirements need the appropriate specialist review.