A nationwide organisation uses an AI planning solution internally and supports its external adoption through demonstrations and training in return for contractual compensation. However, lead handling for this activity has been informal and person-dependent without a consistent data-based method to judge potential and set priorities. This creates uncertainty about where to invest scarce time and effort and risks missing high-value opportunities. The thesis proposes a clear scoring method.

The research followed a qualitative exploratory design. Four expert interviews were conducted with representatives of the organisation, the software provider and an external association to capture context and success factors. Secondary sources from records and public websites added context. Using qualitative content analysis, the team derived seven key evaluation categories and translated them into a two-dimensional scoring framework. The approach ensured traceability and alignment with practical decision needs.

The resulting model brings together two perspectives. The financial score is grounded in activity volumes and the degree of the organisation’s involvement across lead generation demonstrations and training. The non-financial score reflects readiness, including process stability, digital maturity, staff affinity and the expected time to operational break-even. Each score can be applied on its own, or the two can be combined to form a single ranking. Validation through five scenario-based use cases showed that operational readiness can shift priorities and may outweigh purely financial indicators. The organisation’s direct involvement in demonstrations and training had a notable effect on the financial score. The model is simple to apply without specialised software, transparent in its logic and easy to update as new information becomes available or priorities evolve. It provides a practical decision aid for balanced prioritisation and offers an academic contribution by tailoring established lead scoring principles to a real-world context.