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.

What if the real edge in AI isn’t bigger models or more GPUs, but leaders who can turn complexity into clear decisions? We sit down with analyst and “tech whisperer” Christopher Richardson to unpack the hard truths behind AI’s economics, why reliability still lags the hype, and how to build systems that actually pay off. From conference rooms where “the cloud” gets mistaken for the sky to board tables demanding ROI, we map the shift from curiosity to concrete roadmaps that stand up under pressure.

Christopher explains why large language models remain probabilistic and how that matters for accuracy, governance, and risk. We dig into the promise and pitfalls of agentic AI, where chaining models often compounds hallucinations and cost. The conversation tackles the “AI bubble” risk head-on: when adoption slows because outputs cannot be trusted for mission-critical work, the funding engine sputters. We also examine a fresh take on competitive advantage through the DeepSeek lens—achieving strong results with fewer parameters and lower GPU use—hinting that smart efficiency may outpace brute-force scaling.

Sustainability and equity thread through every topic. We talk plainly about the byproduct of electricity being heat, why water use and siting decisions matter, and how to measure trustworthiness beyond slogans. Then we turn to access: open models, practical training, and education pipelines that unlock talent in places like Baltimore, building a broader, more resilient workforce. The playbook is pragmatic and hopeful—smaller, domain-tuned models; clear governance; measurable utility; and real investment in people.

If you care about AI strategy, responsible innovation, and turning hype into durable outcomes, this conversation offers a grounded path forward. Subscribe, share with a colleague who’s wrestling with AI decisions, and leave a review with the one change you’d make first to improve trust and ROI.

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.

Artificial intelligence is rapidly transforming healthcare, but with great innovation comes the responsibility to implement these technologies ethically and effectively. Senior Research Associate Sahaj Vaidya takes us on a fascinating journey through the evolving landscape of digital health, offering invaluable insights from her work at Koita Centre for Digital Health at Ashoka University.

Sahaj's path to becoming an AI governance specialist began during the pandemic when she witnessed how automated systems could spread misinformation, inspiring her to champion responsible AI deployment on a population scale. She challenges common misconceptions about digital health, explaining that it extends far beyond digitizing medical records to encompass preparing for future technologies and helping people adapt to AI-enhanced healthcare as a normal part of life.

We explore the tangible benefits already emerging from AI integration in healthcare—from automated transcription services that free clinicians from administrative burdens to telemedicine platforms that optimize scheduling for both patients and providers. The conversation delves into sensitive issues surrounding medical data collection and privacy, including innovative approaches like using AI to create synthetic data for clinical trials without compromising patient confidentiality.

The discussion turns to AI governance, which Sahaj likens to the rules of a football game—essential for preventing chaos and ensuring fair play. She highlights the gap between organizations claiming to have AI governance policies and those implementing them substantively, noting the growing demand for specialized roles like AI ethics officers. The European Union AI Act serves as a case study of regulatory frameworks with global implications, affecting any organization with European customers or operations.

For aspiring professionals in digital health and responsible AI, Sahaj emphasizes understanding core principles before implementation and evaluating whether AI is truly the appropriate solution for each challenge. She envisions healthcare's future as human-machine collaboration rather than replacement, with AI complementing human decision-making in sensitive contexts.

Join us to hear why AI literacy matters for everyone, regardless of technical background, and discover why Sahaj believes AI isn't here to take our jobs but to handle routine tasks while creating new opportunities requiring distinctly human skills. This episode offers essential guidance for navigating the exciting yet complex intersection of healthcare and artificial intelligence.

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.