Technical evaluators move when the benefit is demonstrated.

Total cost and reliability also appear more often than time to value among the decision criteria, suggesting that the initial performance claim is only part of the assessment. For a supplier, that creates a reason to explain the conditions under which the tool would be operated and supported as carefully as the improvement shown in a demonstration.

A representative evaluation could explore what happens when an integration fails and which team would take responsibility for maintenance. Those are practical extensions of the criteria respondents selected, helping the evaluator examine the work adoption might create alongside the benefit the product promises.

Only two percentage points separate those three rejection reasons. There is little to distinguish them. The more useful reading is that an evaluation needs to address financial approval alongside reliable operation and integration, because each appears frequently in the responses.

The assessment also extends into ongoing work: maintenance burden is selected by 24%, and weak documentation or support by 22%. Including those conditions in a trial would help the evaluator consider what the team would need to do after adoption, when responsibility for keeping the tool useful becomes part of the decision.

The individual responses make that increase more nuanced than the aggregate result alone suggests. Thirty-one respondents move into the specialist option after verification, while eleven move away, leaving a net increase of twenty respondents even though the added evidence does not produce the same response from everyone.

For a vendor designing an evaluation, making the result inspectable means stating the starting conditions and the work used to measure the change, so colleagues who were absent from the test can judge whether it resembles the conditions in which their own team would use the tool.

For code generation, the shares choosing approval before each action and production within approved limits are equal at 38%. Merging and deployment shift toward explicit approval, suggesting that evaluators distinguish between producing an artifact and authorizing the step that carries it into the workflow.

A product demonstration can preserve that distinction by showing how a proposed change is reviewed, and what must happen before it is released. That would let the evaluator examine the points at which responsibility passes between the system and the team, rather than inferring the approval model from the presence of an AI feature.

The alternatives under consideration are also broad, spanning a new commercial purchase or supplier expansion, internal development, and open source. Half of the recalled decisions ended in a commercial purchase or an expansion or renewal, leaving a substantial share in which respondents pursued another route or continued with evaluation or the current approach.

Seventy-one percent report that the recalled decision took no more than three months, although those decisions cover different categories and outcomes. Preparing a clear account of the test while evaluation is underway would help colleagues assess the result without having to reconstruct how it was obtained.

A message about lower ongoing costs earns a closer look from 37%, leading the messages tested, although less manual effort and more predictable work also attract substantial shares. The spread gives suppliers a reason to test the opening emphasis with their own audience while keeping the underlying proposition consistent across those benefits.

The earlier evidence findings show how that initial interest can develop into an evaluation: analysts help respondents research their options, while trials in their own environment lead the evidence they found influential. Linking discovery material to a practical test would make it easier to examine whether the advertised benefit applies to the team’s work.