The AI Product Framework™: Build & Scale With Confidence

The AI Product Framework™ is a unique, comprehensive methodology designed to bridge the gap between AI capabilities and tangible business outcomes. For enterprises striving to move AI from pilot to production, it offers a repeatable approach that guarantees business value by unifying strategy, data science, engineering and product teams, unlike ad-hoc methods that yield inconsistent results. Our framework is built to help you lead AI transformation and deliver measurable outcomes with confidence.

MEET OUR FOUNDER

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Eric Dabols

I'm Eric Dabols, the creator of the AI Product Framework™. My passion lies in transforming complex AI potential into real-world business impact. Having spent years in product leadership roles within leading tech companies, I developed this framework to address the common challenges organizations face in scaling AI initiatives. My mission is to empower teams and leaders to consistently build, deploy, and manage impactful, scalable AI products that drive significant business results.

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The AI Product Framework™:
Our Pillars of Success

The AI Product Framework™ is structured around a strong Foundation and three interconnected core phases: Strategy, Discovery, and Delivery. These pillars work in harmony to ensure your AI products are not just built, but built right.

By systematically integrating these pillars, the AI Product Framework™ provides a clear path to navigate the complexities of AI, ensuring your initiatives consistently generate measurable value and build competitive advantage.

  • The Blueprint for Collaboration:

    • AI Operating Model™ (AOM™): A clear structure defining roles like AI Leadership (Data Science & Analytics, Engineering), Data Scientist, Tech Lead and Product Manager to foster seamless cross-functional collaboration.

    • AI & ML Capabilities: Ensures a shared understanding of core AI/ML concepts across all teams, enabling informed decision-making and effective communication.

  • Charting Your Course with Purpose

    • Core Principles: Guided by Focus, Data-Driven Insights, Transparency, and Placing Bets, we align AI capabilities with critical business objectives.

    • AI Leadership's Role: Pivotal in defining vision, prioritizing methodologies, and balancing innovation with inherent risks to ensure strategic direction.

  • Validating Ideas, Mitigating Risks:

    • Comprehensive Risk Assessment: Rigorous evaluation against Value, Usability, Feasibility, and Viability to ensure solutions are impactful and buildable.

    • Rapid Experimentation: Utilizes prototyping, A/B testing, and simulations for quick validation, minimizing waste and accelerating learning.

  • Operationalizing for Continuous Value:

    • ML Ops (Machine Learning Operations): Central to robust deployment, monitoring, and continuous improvement through CI/CD, instrumentation, and strategic model retraining.

    • Agile Practices: Emphasizes small, frequent releases to ensure scalability, reliability, and continuous adaptation of your AI products in production.