AI Agent & Copilot Podcast: Michael Simms on Why AI Projects Need Data Readiness, Strong Governance

Key Takeaways

• AI investments: Simms explains that a top priority for organizations currently is determining how to get value out of AI investments. He says an estimated 80% of AI initiatives aren't seeing the value that was anticipated, which begs the question: Are you choosing the wrong initiatives? Are you using the correct data? Have you defined the processes correctly? "All the underlying pillars that are required for these things to actually work and actually provide value, those need to be revisited and looked at," he says.
• Data readiness: Data readiness is, in Simms' opinion, "the biggest single issue that's out there right now" as it relates to AI projects. He suggests that organizations need strong data governance and an "iterative approach" to ensure both structured and unstructured data are clean, organized, and ready to support AI systems and workflows at scale.
• Adoption: Simms says his approach to AI adoption is different because he focuses on building the right organizational and process foundations first, rather than rushing to implement technology. He compares it to building a house, noting that "you don’t put on the shingles and the shutters and the paint before you actually build the foundation and the frame."
• New roles defined by AI: Simms expects AI to create and reshape job titles and also create demand for expertise that helps organizations adopt, manage, and govern it. Specifically, he foresees roles focused on governance and engineering, as industries determine how to implement and maintain AI systems.

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