Pragmatic AI Maturity Model

In this interview, we sat down with Greg Smith (Head of Global Product and Solution Marketing, Certinia) to get his insights into the stages of data maturity within the AI adoption journey.

Greg advices that a key distinction in the nature of data handling between generative and predictive AI. Unlike predictive AI, which primarily analyzes existing data, generative AI creates new data from existing information. This fundamental shift necessitates a robust data strategy aligned with AI objectives to maximize the technology’s potential.

The maturity model outlines a progression from fragmented data usage to a sophisticated, integrated approach. Organizations initially leverage external data for efficiency gains, but internal data becomes crucial for deeper insights and influencing business metrics. As AI adoption matures, a focus on closed-loop systems emerges, where predictions are continuously refined based on real-world outcomes. This journey involves both technological and cultural transformations, with early stages emphasizing technology and later stages prioritizing cultural changes such as data governance and AI skill development.

The ultimate goal is to transition from efficiency gains to improved decision-making and scaled impact.

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Greg Smith, Head of Global Product and Solution Marketing, Certinia.
A primary focus of Greg’s is to help services organizations of any size run a more efficient, profitable, and data-driven services organization.
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Recorded at SuperAI Singapore, 6th June 2024, 2.30pm.
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