The Future of Product Management is Looking Bright
“As coding becomes more efficient, teams will need more product management work (as well as design work) as a fraction of the total workforce. Perhaps engineers will step in to do some of this work, but if it remains the purview of specialized Product Managers, then the demand for these roles will grow.”
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Previously, Ng emphasized the importance of prioritizing AI product strategy upfront, as early as possible. Failing to consider the strategic benefits of AI could lead to missed opportunities in a business that's rapidly changing.
Every AI product roadmap should be shaped by an effective strategy that consists of 6 key steps:
Formulate the Product Vision: Define a clear, long-term vision that considers current and emerging trends. This vision should reflect the desired future state of the business, guiding the growth trajectory of AI products.
Assess the Current State: Evaluate current capabilities, technology, processes, data availability, customer pain points, skillsets, and whether the culture promotes AI adoption. A realistic assessment is essential to ensuring that the vision is achievable.
Define Objectives and Key Results (OKRs): Identify specific, measurable, achievable, relevant, and time-bound (SMART) goals that align with the overall vision.
Develop an Action Plan: Break down the OKRs into manageable sub-goals related to data preparation, model development, user experience, and system integration. Detail the steps required to achieve each sub-goal in the product roadmap.
Start with a POC: Begin with a proof of concept (POC) to test the AI capability on a smaller cohort of users, gather learnings, and refine the plan before proceeding with a GA launch.
Monitor Outcomes and Adjust the Strategy: Periodically review progress against established OKRs. AI products often face unexpected challenges with data quality, model drift, and technical limitations. Be prepared to adapt the plan based on the results and keep stakeholders abreast of progress.
Successful AI products don't end with deployment; they require end-user adoption and continuous monitoring. Fostering an AI culture where stakeholders understand the benefits that AI brings to the business is absolutely essential for adoption. It's about intelligently applying AI where it makes the most sense and adds the most value.
