When should we use AI for prospect assessments?

Start by checking whether predefined data and fixed outputs can give prospects recommendations relevant to their circumstances. Traditional assessments can be helpful, but their recommendations are limited by the data and logic behind them. In the NavigatorAI project for UST, a global digital transformation consultancy, prospects struggled to identify the resources most relevant to their needs within an extensive library of AI expertise. The challenge was to connect users with tailored insights rather than leave them with generic guidance. Use that distinction when assessing the case for AI: identify where different prospect needs receive effectively the same recommendation, and whether a fixed assessment can still provide a useful answer.

Look for a need to connect a prospect's business context with tailored questions and recommendations informed by your organisation's expertise. NavigatorAI was developed for UST to help prospects navigate its extensive AI knowledge through personalised recommendations. The experience uses generative AI to identify user-specific challenges, guide prospects through tailored questions and generate recommendations informed by UST's AI playbook and expertise. Publicly available business and audience data helped shape each journey. For a prospective assessment, the practical question is whether business context and individual answers need to shape the guidance together. UST's approach also kept human input central, including prompt design, optimisation and supporting content, rather than treating generative AI as a replacement for expertise.