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I started by trying to scope the problem before jumping to the build/buy framework, which I think was the right call.
Start by clarifying the business goal and customer need, then evaluate build/buy/partner/invest options against criteria like time-to-market, differentiation, cost, and strategic fit. Recommend a hybrid approach that leverages existing Qualtrics strengths while partnering or buying for commoditized components, and outline a phased roadmap with clear metrics.
Pro tip: Emphasize that the decision isn't binary—often the best path is to build the differentiated AI/ML layer that leverages Qualtrics' unique data, while buying or partnering for commodity infrastructure. Also, tie every option back to customer value and Qualtrics' strategic priorities.
Define the specific insights (e.g., correlations, operational metrics) and customer segments. Identify must-have capabilities, scalability needs, and integration points with existing Qualtrics products.
Evaluate Qualtrics' current AI/ML expertise, data assets, and infrastructure. Determine which components are core differentiators versus commoditized enablers.
For each component, compare options on criteria: time-to-market, cost, control, scalability, and risk. Consider build for unique IP, buy for mature solutions, partner for speed, and invest for long-term strategic bets.
Propose a mix: e.g., build the insight generation layer, buy a data processing engine, partner for niche ML models. Outline phases with milestones and success metrics.
Establish KPIs like customer adoption, insight accuracy, and ROI. Plan to reassess decisions as technology and market evolve.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.