PMP Practice: Ensure Shared Vision with Stakeholders

This scenario requires balancing legitimate business constraints with technical uncertainty. The sponsor's need for predictability is real and tied to funding survival, while the technical complexity genuinely requires empirical learning. A hybrid approach allows the team to commit to high-level outcomes and timing for business planning purposes while preserving the flexibility needed for technical experimentation. Regular demonstrations create transparency and allow stakeholders to see actual progress rather than theoretical milestones. This builds a shared understanding that the vision includes both business goals and adaptive execution. The key differentiator is that the sponsor has explicitly stated funding depends on predictable progress, making a purely adaptive approach insufficient, while the technical uncertainty makes pure prediction unrealistic.

**Why not A:** Committing to a purely predictive 18-month roadmap with fixed deliverables ignores the genuine technical uncertainty in machine learning development. Forcing experimental AI work into predetermined milestones creates unrealistic expectations and risks major replanning when predictions inevitably prove wrong.

**Why not C:** Creating two separate plans—one predictive for investors and one agile for developers—is fundamentally dishonest. Managing stakeholder communications independently with divergent plans creates a trust risk; when the plans inevitably conflict, stakeholder confidence in the project will be severely damaged.

**Why not D:** Adopting a fully agile approach and telling the sponsor the team cannot commit to dates directly undermines the stated business requirement that Series B funding depends on predictable progress. Ignoring the sponsor's legitimate business constraint is not a viable path forward.