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Organizations should prepare their data, governance, processes, and people before scaling AI projects. In the October 6, 2026 episode of Cloud Wars’ AI Agent & Copilot Podcast, host John Siefert speaks with Michael Simms, Principal at Data Dojo, about why those foundations matter to AI adoption and value.
Why Simms puts data readiness first
Simms calls data readiness “the biggest single issue that’s out there right now” for AI projects. His point is practical: AI initiatives depend on underlying data that is prepared and managed well enough to support the intended work. He argues that organizations need strong governance and iterative preparation for both structured and unstructured data—not a one-time cleanup followed immediately by deployment.
That work belongs within a broader review of the conditions that allow AI to deliver value. “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,” Simms says.
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Build organizational and process foundations before implementation
Simms describes readiness through a house-building analogy: “you don’t put on the shingles and the shutters and the paint before you actually build the foundation and the frame.” Applied to AI, the sequence is to establish organizational and process foundations before moving into technology implementation. The episode presents this as a readiness principle, not as a comparison of specific tools or vendors.
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- Review organizational foundations. Identify the people, responsibilities, and governance needed to support the initiative.
- Prepare the data. Address structured and unstructured data iteratively, with governance in place.
- Establish workable processes. Make sure the organization can manage the initiative and its data before implementing technology.
- Implement with the foundations in view. Treat technology as part of the sequence, not a substitute for readiness.
How to interpret the episode’s 80% estimate
The episode page attributes to Simms an estimate that 80% of AI initiatives are not seeing the value anticipated. It does not identify the underlying study or provide its date, sample, methodology, or definition of “value.” Treat the figure as Simms’s reported estimate in the episode, not as an independently verified measure of AI project outcomes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the episode says about AI-related work
Simms expects AI to increase demand for governance and engineering expertise, while job titles may change. The episode page gives no quantitative forecast or timeline, so it supports a directional expectation rather than a specific prediction about roles or hiring.
Read the Cloud Wars episode page for the original discussion.
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