E045 – AI or Not – Phil Hartman and Pamela Isom

Welcome to "AI or Not," the podcast where we explore the intersection of digital transformation and real-world wisdom, hosted by the accomplished Pamela Isom. With over 25 years of experience guiding leaders in corporate, public, and private sectors, Pamela, the CEO and Founder of IsAdvice & Consulting LLC, is a veteran in successfully navigating the complex realms of artificial intelligence, innovation, cyber issues, governance, data management, and ethical decision-making.

The shiny demo rarely survives contact with real data. We sit down with Phil Hartman—director, data architect, and seasoned integrator—to unpack what actually makes AI work in production: infrastructure, integration, and testing that respects non-determinism without abandoning reliability. Phil shares how embeddings finally “clicked,” why sending the same prompt to multiple models and merging results can improve quality, and what guardrails and adversarial tests reveal when policies get complex. His insurance parsing example—great up to ten vehicles, then chaos—shows how hidden limits surface only when you move beyond the happy path.

We discuss user experience that respects people’s time, including clear escalation to a human and predictable flows for employee training, as well as brand consistency. Then we dig into a big shift: AI search that visits hundreds of sites and never shows your branding. To stay discoverable, content must be structured and anticipatory—think FAQs on the landing page, schema markup, and concise, high-signal answers that retrieval systems can trust. Phil also makes the case that low-code tools struggle with hierarchical, many-to-many enterprise data, and why leaders should expect custom code, deeper testing, and realistic budgets that reflect the complexity of integration.

If you want AI to be an innovation accelerator, tie it to real outcomes: shorter cycles, cleaner data, higher throughput. Involve end users, measure beyond ROI buzzwords, and design governance that spans model selection, prompt management, privacy, and audit. On jobs, Phil argues for augmentation over replacement—let AI handle the routine tasks so people can focus on judgment-intensive work. The pace is fast and messy, but progress belongs to teams who experiment, test the edges, and build safety into the fabric. Subscribe, share with a colleague who owns AI delivery, and leave a review with your toughest integration challenge—we might feature it next.