AI projects are more likely to deliver value when organizations prepare their data, governance, processes, and people before scaling implementation. In the October 6, 2026 episode of AI Agent & Copilot Podcast, host John Siefert speaks with Michael Simms, Principal at Data Dojo, about why those foundations matter to AI adoption and investments.
Why AI readiness starts before the technology
Simms frames AI readiness as a foundation-building exercise, not a software-first project. His sequence puts organizational and process foundations ahead of technology implementation. He compares the work to building a house: “you don’t put on the shingles and the shutters and the paint before you actually build the foundation and the frame.”
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That analogy points to a practical risk: adopting AI tools before the surrounding organization is ready can leave the core problems untouched. Simms says, “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.”
Why data readiness is central
Simms calls data readiness “the biggest single issue that’s out there right now” for AI projects. His emphasis is on preparing both structured and unstructured data, supported by strong governance and an iterative approach. In practice, readiness is not a one-time cleanup before launch; organizations need to revisit data and its governance as projects develop.
The episode’s discussion does not set out a technical checklist or a particular data architecture. Its central point is organizational: AI investments depend on data that can be managed and used appropriately, and that work needs to be treated as part of the project rather than postponed until after implementation.
What the episode’s 80% estimate does—and does not—say
The episode page attributes to Simms an estimate that 80% of AI initiatives are not seeing the value anticipated. The page does not identify the underlying study, its date, sample, methodology, or how “anticipated value” is defined. Treat the figure as Simms’s reported estimate, not as an independently verified or fully specified industry statistic.
Even without those details, the estimate reinforces the episode’s argument: implementation alone does not guarantee results. Readiness, governance, and the processes around a system are part of the work of making an AI investment useful.
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Simms expects demand for governance and engineering expertise related to AI, and says job titles may change. He offers no quantitative forecast or timeline, so this is a directional expectation rather than a prediction about how many roles will be created or when. For organizations planning adoption, the implication is to account for the expertise needed to prepare and govern data alongside the technology itself.
The episode centers on a sequence: establish organizational and process foundations, address data readiness and governance, then implement and iterate. It presents this as Simms’s guidance, not as the outcome of a tested comparison between vendors or implementation methods.
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The episode, “AI Agent & Copilot Podcast: Michael Simms on Why AI Projects Need Data Readiness, Strong Governance,” was published by Cloud Wars on October 6, 2026. It features host John Siefert and guest Michael Simms, Principal at Data Dojo. The episode page describes their conversation as focused on maximizing value from AI investments and the role of readiness and governance in adoption.
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