Day: April 21, 2025

Building Effective AI and GenAI Product Strategy

Having spent two decades building products with data and AI at their core, I’ve decided to share my perspective on how AI/GenAI product strategy development and execution differs from conventional product strategy approaches.

As organizations increasingly integrate these capabilities into their product portfolios, product leaders must adapt their strategic frameworks to address the unique challenges and opportunities that AI and GenAI present.

1. Problem-Centric Market Research

The most effective AI product strategies avoid the trap of pursuing AI/GenAI for its own sake. Instead, they focus relentlessly on customer and business value, using AI’s distinctive capabilities to solve problems that would otherwise remain intractable.

• Identify business problems that cannot be effectively solved without AI/GenAI,

• In larger companies, conduct cross-organizational needs assessment to discover high-value AI/GenAI use cases,

• Look beyond the obvious use cases to uncover areas where AI/GenAI can create distinctive value.

2. Organizational Alignment & Cross-Functional Collaboration

Organizational alignment is equally critical. AI/GenAI initiatives must support broader company objectives while accounting for internal capabilities and limitations.

• Secure executive sponsorship and stakeholder buy-in early in the development process,

• Align AI initiatives with broader company objectives and strategic goals,

• Establish cross-functional teams to navigate technical, ethical, and business challenges.

3. Data Strategy as Core Business Asset

When it comes to AI/GenAI product development, Data is the King. Weaving data availability and readiness into product strategy is essential.

• Develop deliberate data acquisition, governance, and enhancement strategies

• Make strategic decisions regarding data partnerships, feedback loops, annotations, and synthetic data creation

• Position AI product managers as key stakeholders in data readiness and availability

4. Technical Development Under Uncertainty

The value proposition for AI products demands particular attention. Unlike traditional products, AI solutions often deliver probabilistic rather than deterministic outcomes. This requires careful articulation of how AI/GenAI delivers better solutions than alternatives, with clear success metrics.

• Implement research-prototype-productize development patterns for AI/GenAI solutions. The most effective AI strategies balance innovation and research with practical customer needs, avoiding the trap of pursuing technical sophistication at the expense of user value.

• Establish probabilistic success metrics that account for AI’s experimental nature,

• Plan for ongoing model evaluation, including human-in-the-loop evaluations. Strategic planning must account for how model performance may change over time, including potential performance drift that requires ongoing monitoring.

• Build vs buy decisions need to be revisited over time, as home-grown solutions mature.

5. Trust-Building and Adoption Strategy

Ethical and regulatory considerations take on heightened importance in AI/GenAI product strategy, especially as regulatory landscapes evolve around AI transparency, fairness, and accountability. GenAI introduces new concerns around authenticity, copyright, and misuse.

1. Develop explicit frameworks for responsible and ethical AI development

2. Incorporate explainability features and trust-building elements into product design

3. Create educational components to support human-AI collaboration and overcome adoption barriers

Each of these points deserves its own article, and many are being published every day. I will share as I come across the ones that resonate with me and, in my opinion, will help my fellow AI product managers create awesome AI/GenAI products and features!