When should we use AI in prospect assessments?

Generative AI is worth considering when prospects need help connecting a broad body of expertise to their individual challenges. Fixed-output assessments can be helpful, but their recommendations are limited by predefined data and logic. For UST, a global digital transformation consultancy, the challenge was that prospects struggled to identify relevant resources within an extensive library of AI expertise. NavigatorAI used generative AI to identify user-specific challenges, guide prospects through tailored questions and generate personalised recommendations informed by UST's AI playbook. The practical test is whether predefined results can provide sufficiently relevant guidance. When prospects' circumstances require more adaptable connections between their answers and available expertise, NavigatorAI illustrates a use for generative AI beyond scripted recommendations.

Adaptable recommendations need trusted content, structured rules and human oversight, not flexibility alone. NavigatorAI combined publicly available business and audience data with UST's proprietary AI expertise. Public data made the experience relevant to individual circumstances, while proprietary expertise kept recommendations aligned with UST's services. Carefully structured rules and human oversight supported accuracy and relevance throughout the experience. For an assessment project, the practical requirement is to establish the expertise recommendations should draw on, the rules that shape the answers and the human oversight that maintains their quality. A 2026 finding from Gartner adds a buyer perspective: 69% of B2B buyers prefer to validate AI-generated insights with sales representatives.