You've been assigned to market a new AI/ML-powered feature, but the engineering team is still iterating on the core algorithms, and the product team hasn't finalized the exact user experience or even the feature's name. As an Associate Product Marketing Manager, how would you begin to develop a go-to-market strategy under such high ambiguity, and what specific steps would you take to gather the necessary information and align stakeholders?

Employ a phased, iterative GTM strategy using the CIRCLES framework for discovery and the RICE framework for prioritization. Phase 1: Deep Dive & Alignment. Conduct stakeholder interviews (Eng, Prod, Sales, CX) to understand technical capabilities, user problems, and business objectives. Initiate competitive analysis for similar AI/ML features. Phase 2: Hypothesis & Validation. Develop preliminary value propositions and target audience segments. Create low-fidelity messaging concepts and test internally with sales/CX. Phase 3: Iterative Refinement. Based on product updates and internal feedback, refine messaging, naming conventions, and GTM channels. Prioritize information needs using RICE (Reach, Impact, Confidence, Effort) to guide engineering and product teams on critical marketing requirements. Establish a weekly sync with core stakeholders to manage ambiguity and ensure alignment.

Under high ambiguity, I'd initiate a phased GTM strategy, starting with a 'Discovery & Alignment' phase. I'd apply the CIRCLES framework to gather insights: Comprehend the situation by interviewing engineering to understand technical capabilities and limitations, and product for user problems and potential solutions. Identify the customer by analyzing existing user data and conducting preliminary market research for AI/ML trends. Report on competitive landscape to identify gaps and opportunities. I'd then move to 'Hypothesis & Validation', developing preliminary value propositions and target audience segments. Using the RICE framework, I'd prioritize information needs, focusing on high-impact, high-confidence data points from engineering and product to guide their iterations. For instance, understanding the core problem the AI solves (Impact) and the likelihood of its successful implementation (Confidence) would be paramount. I'd establish a weekly 'GTM Sync' with key stakeholders (Eng, Prod, Sales, CX) to share evolving insights, refine messaging concepts, and collaboratively define the feature's name and user experience. This iterative approach ensures marketing readiness evolves in lockstep with product development, minimizing last-minute scrambles and maximizing launch effectiveness.