Ep. 0027 - How to Evaluate AI Vendors Without Getting Burned

The market is flooded with AI tools making big promises, but 95% of AI projects failed last year. Learn the 5 essential questions every enterprise buyer must ask to evaluate vendors, avoid liability, and secure ROI.

Ep. 0026 - AI Won't Take Your Job — But Someone Using AI Will

The most overused line in AI has become a statistical reality. Learn how to leverage agentic AI, bridge the adoption gap, and future-proof your career against disruption before it's too late.

Ep. 0025 - How to Pitch an AI Project to Your Boss

You see the opportunity, but leadership needs convincing. Learn a proven 5-step framework to translate AI hype into clear ROI, mitigate risks, and actually get your AI project approved and funded.

Ep. 0024 - Building Your AI Career Roadmap

Whether you're in marketing, finance, operations, or any other field — AI fluency is the career multiplier of the decade. Discover how to build a 6-to-12-month AI career roadmap and boost your salary by up to 56%.

Ep. 0023 - How to Become the AI Person at Your Company

Every organization needs someone who bridges the gap between tech and business. Learn why 95% of AI pilots fail, how to become the indispensable AI Translator, and command a $221k salary — no CS degree required.

Ep. 0022 - Multimodal AI — Beyond Text

The newest AI models can process text, images, audio, and video simultaneously. Discover how multimodal AI is moving beyond text to true perception, the top 2026 models, real business use cases, and the ethical challenges ahead.

Episode 21: AI and Cybersecurity — Sword and Shield

AI is being used both to launch more sophisticated cyberattacks and to defend against them — creating the highest-stakes arms race in human history.

In this episode we dive into the $25 million deepfake heist, the rise of "vibe hacking" and autonomous AI hacking swarms, and how defenders are fighting fire with fire using Agentic Security Operations Centers. We also cover why shadow AI is your organization's biggest vulnerability and the concrete steps you need to take right now.

Key Stats: eCrime breakout time is down to 29 minutes. The fastest lateral movement: 27 seconds. AI is predicted to drive 50% of incident response by 2028.

In Episode 20 of *AI in 5*, the narrator challenges the prevailing narrative of the AI industry: the relentless pursuit of massive, trillion-parameter models. The episode begins by exploring the "digital polymath" nature of giants like Claude Opus and GPT-4. While it is fascinating that a single AI can write Python code, critique 14th-century French poetry, and analyze the socio-economic impact of the potato, the narrator poses a critical question: do you really need a polymath with four PhDs just to sort your morning emails or extract a legal clause?

The narrative then pivots to the rising obsession with Small Language Models (SLMs) like Microsoft's Phi-3, Meta's Llama 3 8B, and Mistral. The host breaks down the incredible benefits of shrinking the model size. Not only do SLMs drastically reduce the prohibitive costs and environmental impact associated with massive server farms, but they also introduce an "accuracy paradox." By restricting the AI's knowledge base to specific, highly curated data—akin to teaching a child with textbooks rather than letting them learn from strangers shouting on the street—these smaller models actually outperform their giant counterparts in specialized tasks. Furthermore, their restricted "imagination space" makes them far less likely to hallucinate.

In Episode 19 of *AI in 5*, we tackle one of the most pressing issues facing the modern corporate world: the massive AI talent gap. As businesses scramble to integrate artificial intelligence into their operations, they are hitting a severe bottleneck. There simply aren't enough AI specialists to go around, and the ones who do exist command exorbitant salaries, often being scooped up by tech giants before their resumes even hit the open market.

The episode explores the frustration of everyday companies trying to compete in this hyper-inflated job market, illustrating the problem through a mid-sized logistics firm that wasted six months trying to hire an AI lead. However, the narrative pivots to a powerful realization: we are thinking about AI adoption completely wrong.

Instead of engaging in bidding wars for "unicorns"—highly technical engineers who lack specific business domain expertise—companies should look inward. The episode argues that it is far easier to teach a brilliant, experienced employee how to use AI tools than it is to teach an AI engineer the intricate nuances of a specific business. By highlighting a successful customer service case study, the episode demonstrates how empowering existing staff with AI tools not only solves operational inefficiencies but also flips the script on workplace AI anxiety, transforming fearful workers into loyal, AI-augmented professionals.

In Episode 18 of *AI in 5*, the host tackles one of the most misunderstood concepts in the artificial intelligence space: model fine-tuning. The episode opens with a relatable analogy of a brilliant but generic intern who lacks a company's specific voice. This perfectly illustrates the limitations of base AI models, which are trained on the entire internet but lack specialized, localized context.

The narrative explores how companies, desperate to customize their AI, latch onto "fine-tuning" as a buzzword. The host breaks down the technical reality of fine-tuning, comparing base models to a "giant lump of clay" that has been pre-trained at the cost of tens of millions of dollars. Fine-tuning, then, is the delicate process of sculpting the edges of that clay to fit a highly specific mold, much like an actor going to method acting school to *become* a character.