When should we use AI for prospect assessments?

AI becomes worth considering when a fixed set of recommendations cannot connect a broad body of expertise to the individual challenges prospects describe. Traditional assessment tools rely on predefined data and logic, which can limit how closely their outputs reflect a prospect's circumstances.

UST faced that problem with its extensive library of AI expertise: prospects struggled to identify the resources most relevant to their needs. NavigatorAI was developed for UST to deliver personalised recommendations rather than leave users to navigate the library alone. The practical decision is whether predefined outputs can provide sufficiently relevant guidance. Where prospects need a more individual connection between their challenges and available expertise, AI offers a way beyond scripted recommendations.

An AI assessment needs trusted content, structured rules and human oversight to turn flexible generation into relevant guidance. UST's NavigatorAI combined tailored questions with publicly available business and audience data, using UST's proprietary AI playbook and expertise to inform personalised recommendations. The approach kept recommendations aligned with UST's services rather than relying on unconstrained generation.

Human involvement included prompt design and optimisation, supporting content, refinement and testing. For a prospect assessment, the practical pattern is to define the expertise recommendations should draw on, structure how the interaction works and retain human oversight of accuracy and relevance. A 2026 finding from Gartner adds a reason to preserve access to people: 69% of B2B buyers prefer to validate AI-generated insights with sales representatives.