In this episode of Rheminiscing, Dr. Tony Rhem breaks down what large language models (LLMs) are, how tools like ChatGPT, Gemini, and Claude actually work, and why the real value comes from how humans collaborate with them. You’ll learn the basics of token-by-token prediction, why LLMs feel intelligent even though they operate on probability, and how today’s models differ in strengths (reasoning, multimodal understanding, and contextual alignment). Dr. Rhem also demonstrates the power of well-structured prompting with a live example focused on patient engagement in healthcare, then closes with practical best practices: define intent, provide context, iterate, and verify outputs with trusted sources, keeping humans accountable and “AI-informed,” not AI-driven. #ArtificialIntelligence #AI #GenerativeAI #largelanguagemodels #llm #chatgpt #googlegemini #anthropicclaude #promptengineering #aiprompts #aitools #aiproductivity #ailiteracy #responsibleai #aigovernance #humanintheloop #aialignment

Key themes: AI and the future of work, AI Strategy, Generative AI, AI governance

This episode revisits a powerful conversation with Dr. Annie Green, exploring how artificial intelligence (AI), knowledge management, and digital transformation are reshaping the future of work. Building on those insights, Tony Rhem and his AI co-host Georgia unpack why a clear, well-defined AI strategy is no longer optional for organizations or individuals.

The discussion emphasizes that an AI strategy is a living framework, not a static document. As technologies evolve, so must the strategy, ensuring alignment between business objectives, workforce capabilities, data governance, and ethical AI practices. Without a plan, AI initiatives often lead to confusion, bias, duplicated efforts, and wasted investment.

A strong AI strategy is framed around four foundational pillars:

Purpose and alignment with measurable business or mission goals

People and skills, ensuring AI literacy from executives to practitioners

Data and infrastructure, grounded in strong data governance and security

Ethics and governance, embedding transparency, explainability, accountability, and oversight from day one

The episode also highlights why organizations frequently rush into AI tools without building a foundation—chasing innovation instead of solving the right problems. The solution is pairing AI strategy with AI policy and governance frameworks, creating clear rules of engagement that drive responsible, trustworthy, and sustainable AI adoption.

Importantly, the conversation extends beyond large enterprises. Small and mid-sized businesses, professionals, and individuals are encouraged to develop their own AI strategies—“pick a lane,” build depth in a specific AI discipline (such as generative AI, AI governance, or algorithmic assessments), and align learning with clear goals.

The episode closes with a direct message: AI without strategy is risk at scale. Strategy and accountability are inseparable, and ethics must be designed into AI systems—not bolted on later. AI strategy, done right, becomes ethics in action.

Bottom line: Technology moves fast, but purpose, governance, and strategy are what keep AI on course.

Key themes: AI and the future of work, AI Strategy, Generative AI, AI governance

In this episode, I speak with Dr. Annie Green about AI and how it has and will continue to influence how we work, live, and play.

Key themes: AI and the future of work, AI Strategy, Generative AI, AI governance