Functional Executives

Veer and Jim Boswell talk through a practical AI reality check: the tools are getting more capable, but they still require hands-on reps, clear problem definition, and human oversight. They also explore what it looks like to use AI inside a business, from experimenting with local models to building a case for where AI can actually save time and reduce friction.

Key Topics:

• In this episode, Veer shares a hands-on experiment building a local AI assistant that watches the screen and offers contextual nudges, using a Gemma 12B model on his own Mac.
• Jim and Veer discuss the end of the five-hour usage limit in one of the major AI tools and how vendors are lowering friction to encourage more use.
• They compare fast and wrong versus slow and wrong, agreeing that speed alone does not solve a weak workflow or a poor model.
• Jim explains how AI tools have become more complex, with deeper settings and hidden features like Claude skills that many users never enable.
• Veer describes using a newer model setup with an Ultra mode that spins up subagents, letting him watch orchestration and agent handoffs in real time.
• Both hosts emphasize that meaningful AI adoption comes from reps, fluency, and iterating through failures, not from expecting a perfect first build.
• They discuss Uber’s approach of embedding strong AI engineers into other departments in short sprints to automate real workflows and seed internal capability.
• Veer connects that approach to manufacturing methods like Kaizen Blitz and diagonal slice, where cross-functional pods are given authority to fix specific problems quickly.
• Jim argues that smaller and mid-sized companies usually do not have the bandwidth to dedicate staff for long internal AI experiments, so borrowing expertise may be more practical.
• They both stress the importance of choosing one broken process, building a business case, and focusing on measurable outcomes instead of trying to fix everything at once.

Timestamps:

00:00 - Summer catch-up and the state of AI conversations
01:19 - AI vendors lower barriers as new models roll out
02:42 - Building a screen-aware AI assistant on a local model
03:40 - Three days of compute and what came out of it
05:03 - Local model limits, speed, and usefulness
06:17 - Why AI tools still require a learning curve
08:06 - Hidden settings and skills inside Claude
09:05 - Iteration, errors, and the human in the loop
10:11 - Watching Ultra mode spin up subagents
11:38 - Why reps are required to get value from the tools
12:20 - Training and fluency as the foundation for adoption
13:12 - Uber’s agentic pods and embedded AI experts
14:51 - Cross-functional fix-it pods and Kaizen-style thinking
16:58 - Why small and mid-sized firms need a different approach
17:55 - Borrowing a chainsaw instead of buying a landscaping crew
18:55 - Speeding up finance and accounting workflows
20:40 - Seeding AI through a company with an intentional role
22:03 - Start with the business problem, not the tool
24:01 - Why the beginning of a project can be much faster with the right mindset
25:23 - Using AI for structured discovery and problem narrowing
27:00 - Consultant help versus doing the reps yourself
28:23 - Coaching, correction, and muscle memory
29:07 - Build a light governance layer and a simple business case
30:09 - Helping clients get fluency, reps, and a clear direction
31:23 - Wrapping up and previewing a future guest

Notable Quotes:

"Fast and wrong is just as bad as slow and wrong."

"You need to go borrow the chainsaw if you're serious about it."

"We're helping folks do this and get a couple of reps in."

Can you really trust an AI with your spreadsheets? Should you? Jim Boswell and Veer Hossain pour a strong cup of skepticism over the myth that AI is about to replace all junior employees—and why most people are using these tools all wrong. From the dangers of treating AI as a magical genie to the value of grilling your own digital intern, the hosts dissect Anthropic’s “4D Framework” for AI fluency and how it applies to both tech aficionados and the AI-skeptical.

• Anthropic’s 4D Framework:
  • Delegation: Define exactly what you’re offloading to AI. Is it even worth it?
  • Description: The better you describe your problem, the better the AI delivers
  • Diligence: Don’t skip the human auditor step—review everything as if AI is your new intern.
  • Discernment: Final decisions always need a human. Don’t let AI steer the ship unsupervised
• Why Gen Z (and Boomers) Distrust AI:
  • Suspicion and resentment show up at both ends of the experience spectrum
  • Much of the distrust comes from misunderstanding and time pressure, not laziness or fear
• Digital Apprenticeship Isn’t Dead—It’s Just Higher Stakes:
  • Entry-level work isn’t vanishing; it’s evolving. New hires need to think critically about AI output rather than just generating busywork
• Managers: Don’t Abdicate Mentorship:
  • AI should free up more time for mentoring, not less
  • The most rewarding work isn’t productivity hacks—it’s helping people develop both hard and soft skills
• AI as a Tool, Not a Threat:
  • Treat AI like a junior analyst—not a genie and not a replacement for human judgment
  • Success comes from understanding value creation, not cutting costs to the bone

• “Sometimes prompts take 20 or 30 minutes to write if you really want AI to do it right. A lot of people want this to be a genie where they talk for 2 seconds.” — Jim Boswell
• “We don’t take work from a junior team member and just throw it right at the client... People are doing that with AI.” — Veer Hossain (05:24)
• “You can intuit things that make you look like a wizard, but really all it is is just learned experience.” — Jim Boswell
• “If you lead with distrust, I don’t think we’re helping this next generation of leaders.” — Veer Hossain

• Podcast: Available on Apple Podcasts, Spotify, YouTube
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• Comments & Questions: Drop us a line, challenge our thinking, or share your own war stories about AI in the workplace.

In this episode, Jim and his co-host dive into the cultural moment surrounding Artificial Intelligence and its reception by the younger generation entering the workforce. From commencement speakers getting booed for praising AI to the deep-seated anxieties Gen Z feels about losing the "human experience," the hosts unpack the friction between technology and traditional skill-building. They also share practical advice on how to integrate AI into your business responsibly and how to prompt AI tools to avoid "hallucinations" and baked-in digital hubris.

• Commencement Controversies: The hosts discuss a cultural moment where a record executive giving a commencement speech at Middle Tennessee State University was booed by young graduates for stating that AI is changing the face of production. The executive lashed out at the students, demonstrating a lack of emotional intelligence (EQ) in handling the feedback.
• A Better Approach to Backlash: A similar situation occurred with a Google founder/executive speaking at either Arizona State or the University of Arizona. While he also faced boos, he handled the crowd with much better EQ, trying to explain how AI could elevate society.
• The 14-Year-Old Perspective: One of the hosts shares his 14-year-old son's begrudging use of NotebookLM to create Spanish flashcards, noting that the teen flat-out rejects using AI for creative pursuits like music, art, or making YouTube videos.
• The Threat to Foundational Skills: Young adults are deeply concerned that relying on AI will prevent them from learning core foundational skills—such as calculating Net Present Value (NPV) or the Time Value of Money.
• The Experience Equation: The hosts propose a formula for AI success: 25 years of professional experience plus AI is a great recipe. However, zero experience plus AI is a recipe for hubris and disaster, as users won't have the foundation to know when the AI is wrong.
• The Academic Curriculum Lag: Colleges are currently struggling to teach AI responsibly, with academia fighting over what an AI curriculum should even look like.

The hosts reviewed a recent Gallup poll detailing how Gen Z feels about Artificial Intelligence over the past year. The data reveals a heavy lean toward negative emotions:

EmotionPercentage of Gen Z

• Anxious42%
• Angry31%
• Excited22%
• Hopeful18%

• Start Small: For businesses looking to adopt AI, the advice is to start small, make consistent investments over time, and focus on building basic literacy and fluency.
• Train Your AI's Voice: You can vastly speed up your workflow by spending time cultivating a writing skill within the AI that sounds exactly like you, feeding it plenty of pre-AI examples of your own writing.
• Combat AI Hubris: Out-of-the-box AI systems are often overly confident and will "hallucinate" incorrect answers.
• The "Ultimate Skeptic" Prompt: To get better, more accurate results, write explicit instructions telling the AI to act as your "ultimate skeptic". Instruct it to sit alongside you and scrutinize everything you both say, and tell it that when it lacks context, it must stop and ask for more information rather than guessing.

In this episode, Jim and Veer explore what it takes for businesses to turn AI curiosity into real results. From low-friction workflows to stronger leadership and smarter investment, they share why the biggest opportunity isn’t simply using new tools—it’s solving meaningful problems with them.

The conversation focused on differentiating true value from simply using the newest AI tools, emphasizing that meaningful impact only comes when technology is actually applied to solve real business problems, not just checked off a list as "latest and greatest"

A key theme that emerged was the importance of having at least one person in an organization who is deeply curious and empowered to drive AI initiatives end-to-end. This champion mindset is illustrated by examples of companies that leverage someone’s curiosity to implement actual change rather than just experimenting superficially

One concept discussed was the clever use of AI embedded into everyday workflows, like introducing an email bot for automating repetitive tasks. This approach lowered the barrier to adoption and mapped AI into what people were already doing, making it seamless for users who weren’t interested in learning new interfaces

Several points were raised, including an example where a team abandoned a high-value, time-saving AI-based report generator because it cost 30 cents per use. The discussion explored the irony of penny-pinching on technology that could add exponential value—contrasted with spending far more on trivial employee perks like bagels

The discussion explored the need for leaders to give teams room to experiment, fail, and learn—making sure those experiences are recognized as valuable learning moments instead of just "failures." The encouragement for stewardship balanced with risk-taking stood out as a modern leadership imperative

Resources & Links:

• ChatGPT
• Claude
• GPT models and harnessing context
• Management of AI workflows

This episode dives into the critical distinction between merely using AI tools and leveraging them for tangible business outcomes. Veer and Jim explore how organizations can move past vanity metrics like usage numbers to focus on meaningful problem-solving with AI.

In this episode:

• Why usage metrics like impressions and mentions are misleading indicators of success
• The importance of familiarization versus effective utilization in AI adoption
• How to assess whether AI-driven activities translate into value creation
• The misconception of AI tools being "new" and the real key is applying them thoughtfully
• Practical examples of integrating AI in daily workflows, including email automation and process improvements
• The role of leadership in fostering a learning culture that drives real outcomes
• The significance of addressing friction points and learning from failures
• Strategies to embed AI into core business processes rather than superficial use
• The importance of judgment, context, and critical thinking over mere tool operation

Resources:

• Book: AI Superpowers by Kai-Fu Lee
• Claude by Anthropic
• OpenAI API
• Cody - AI workflow management
• Westlaw with AI features

In this episode, Veer and Jim explore the nuanced landscape of AI integration, dispelling misconceptions and emphasizing strategic literacy. They highlight the pitfalls of superficial AI use, the importance of aligning AI initiatives with clear outcomes, and the critical role of government regulation and public perception. This conversation offers a pragmatic view on balancing innovation with responsibility in the age of AI.

Key Topics:

• The danger of AI being used as a blunt hammer — AI as a tool, not a magic fix
• How executives often chase "two pounds of AI" without a clear alignment to outcomes
• The misconception of “token maxing” and the importance of proper AI literacy
• Risks of cost overruns and lack of strategic planning in AI budgets
• Case studies on AI model bans and government regulation impacts
• The importance of building organizational fluency—"outcomes first" approach
• The threat of AI systems being "too agreeable" and the importance of responsible deployment
• The cultural and public perception of AI, especially among younger generations and social media influence
• Practical steps for organizations: education, experimentation, respecting risks, and establishing responsible policies

Timestamps:

• 00:00 Meta trying to unwind acquisition
• 03:28 OpenAI questioned by multiple states
• 07:11 Discussing AI model risks and ethics
• 10:32 Discussing AI model guardrails
• 15:16 Discussing AI literacy and challenges
• 18:25 Encouraging curiosity about AI tools
• 20:39 Middle school band comparison

Resources & Links

• Large Language Models (LLMs) explained
• OpenAI
• Anthropic
• Claude by Anthropic
• Meta's DeepSeek
• NVIDIA for AI hardware
• Regulatory and policy updates on AI
• The Impact of AI on Society

In this episode, Veer and Jim dive into the practicalities, challenges, and long-term implications of AI adoption in business. They explore how organizations can develop a responsible, strategic approach to AI that balances innovation with risk management, all while avoiding common pitfalls like token wastage and superficial "AI theater."

Key Insights:

• AI is often used as a hammer searching for a nail, leading to inefficient and superficial applications.
• The importance of establishing a clear AI strategy aligned with desired outcomes, not just adopting AI for its own sake.
• How token management and cost controls are often misused as tactics rather than part of a strategic design.
• The danger of overconfidence in AI understanding and the importance of developing true fluency.
• The evolving regulatory landscape and the necessity of risk mitigation, including local inference options.
• The cultural and societal shifts, including public perception and ethical concerns, influencing AI deployment.
• Recommendations for building AI literacy across executive teams to foster responsible, impactful use.

Resources & Links:

• The Practice of Strategy by Henry Mintzberg
• NVIDIA’s GPUs for AI
• Claude by Anthropic
• OpenAI's GPT models
• Meta’s Llama & Llama 2

The conversation focused on the challenges and opportunities facing executives as they integrate AI into their daily work. A key theme that emerged was the importance of starting small and embracing experimentation—even in the face of uncertainty or lack of technical expertise. The discussion explored the ways individuals can use AI tools to automate mundane tasks, improve writing, and develop a more authentic digital presence by training AI to write in their personal or brand voice. Several points were raised, including the need for cross-functional communication, knowledge sharing, and the value of quick wins and measured risk-taking. Listeners are encouraged to take actionable steps towards building AI fluency and to view AI not as someone else’s responsibility, but as a foundational aspect of today’s workplace.

The conversation focused on the proliferation of AI-generated content that is easily recognizable by its formulaic style, excessive emoji use, and lack of personal voice, which often leads readers to disengage

A key theme that emerged was the practical demonstration of automating the creation and upload of product descriptions for an e-commerce site using AI, dramatically reducing repetitive manual work

The discussion explored whether technology risks are truly technical problems or human problems, ultimately suggesting that increased cross-functional communication and knowledge sharing are the main solutions to leveraging new AI tools effectively

One concept discussed was the recommendation to identify a recurring, time-consuming task and experiment with AI to improve or automate part of the process, stressing that even a small jump in quality or efficiency is a meaningful win

Several points were raised, including the practical advice to develop a “skill” in AI tools that reflects an individual or brand’s authentic voice and to use examples from one’s own writing or brand history to train these skills.

If you enjoyed this conversation with Jim Boswell and Veer Hossain, make sure to follow us on LinkedIn, YouTube, and all your favorite social platforms to keep up with the latest insights and updates. Don’t miss out on practical tips and new episodes—subscribe to our newsletter today so you’re always in the loop. Your feedback and questions are always welcome, so join the conversation and stay connected with us!

Welcome to Functional Executives, where Veer and Jim Boswell bring pragmatic, real-world insights from the front lines of technology, finance, and strategy for mid-market companies. In this episode, Veer and Jim Boswell dive into the world of AI in business—from the early hype of ChatGPT, to the challenges of AI maturity, to the practical pitfalls and opportunities facing organizations today.

They discuss why most mid-sized businesses are still at the starting gate with AI, explore the growing need for organizational literacy and certifications, and highlight the importance of moving beyond flashy AI projects to make the technology a seamless part of daily operations. Along the way, they share personal experiences, debate risk and regulation, and challenge the fear-driven binary thinking that often holds companies back. Whether you’re a senior executive or hands-on operator, you’ll get actionable advice and thought-provoking commentary on how to leverage AI while navigating the messy realities—and opportunities—of digital transformation.

Veer and Jim Boswell discuss how most mid-sized companies are at the very low end of the AI maturity curve, despite leadership often believing they are much further along

Jim Boswell introduces the idea that companies must move beyond viewing AI as a novelty, integrating it as a fundamental aspect of daily work—much like past shifts to digital and mobile-first mindsets

Veer emphasizes that early AI policies should rely on existing principles (like email or data sharing policies) and common sense, rather than creating fear-driven, overly restrictive rules

Jim Boswell argues that focusing on high-profile AI initiatives is less valuable than embedding AI into everyday workflows and making it almost "invisible" through practical adoption and proficiency

Veer points out that many companies already operate in gray technical areas (like non-compliant Excel spreadsheets driving key decisions) and that similar flexibility should be considered with AI

Connect With Us

• Reach out if you’re interested in deeper education, workshops, or one-on-one strategy sessions tailored to your business’s AI journey. Our contact info is in the show notes!

In this episode, Veer and Jim delve into how AI is revolutionizing executive workflows. They discuss building AI assistants like Cyrano for daily briefings, the evolution of productivity tools, and the balance between automation and human oversight.

Key Topics:

• Personalized AI assistants for decision support
• Evolution from basic tools to integrated AI systems
• Importance of human judgment in AI use
• Balancing automation with oversight
• Systematizing business operations with AI
• Ethical considerations in data sharing
• AI's impact on workforce skills and agility

Timestamps:

• 00:00 - Introduction to AI in executive workflows
• 01:23 - Cyrano: AI-powered mornings
• 02:09 - Designing relatable AI personas
• 03:14 - Privacy concerns with AI
• 05:07 - Preparing for an AI-dominated future
• 07:30 - Strategic use of AI
• 09:21 - Balancing AI input with human discernment
• 12:00 - Veer's "Balcony" app for task management
• 15:23 - Managing AI’s coaching roles
• 20:13 - Risk of dependency on AI
• 23:14 - AI as a business operating layer
• 30:48 - Authentic communication in the digital age
• 33:05 - Enhancing human judgment with AI
• 40:45 - Scalable business systems with AI
• 52:45 - AI as a volume amplifier

Resources & Links:

• Prediction Machines by Ajay Agrawal
• Veer Hossain - LinkedIn
• Jim Boswell - LinkedIn