What happens when artificial intelligence moves beyond chatbots and starts making decisions, managing workflows, and delivering real business results?

In this episode of Functional Executives, hosts Jim Boswell and Veer Hossain sit down with Justin Rae, Founder & CEO of Cinch, to explore how AI is transforming the way businesses operate—and why the biggest opportunities may still be ahead.

Justin shares firsthand experiences implementing AI across his organization, from Claude Code and intelligent agents to the exciting concept of "self-driving marketing."

In this episode, we explore:

- Why AI adoption isn't necessarily about fear—and what really holds employees back

- How AI agents are transforming productivity, sales, and customer service

- The game-changing capabilities of Claude Code and other AI tools

- Why giving employees room to experiment with AI matters

- How businesses can use AI without losing human oversight

- The potential of self-driving marketing and autonomous AI systems

- What executives should be doing now to prepare for what's next

💡 The big takeaway: AI isn't simply another technology investment. It's changing how work gets done, how teams operate, and what's possible for businesses of every size.

If you're a CEO, CFO, entrepreneur, or business leader looking to move beyond AI hype and understand what's actually working, this conversation is for you.

🌐 Explore more episodes and insights: https://www.functionalexecutives.com

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Evan shares how his team used AI to accelerate a major modernization project while keeping experienced developers involved in the process.

We discuss:

+ How AI can dramatically speed up development

+ Why human oversight still matters

+ QA bottlenecks, security risks, and technical debt

+ Claude Code, AI agents, and developer productivity

+ The cost of scaling AI inside an organization

If you’re a CEO, CFO, CIO, CTO, private equity leader, or executive trying to understand how AI can create real business value without sacrificing quality or security, this episode offers a practical look at what’s working now.

In this episode of Functional Executives, Jim Boswell and Veer Hossain sit down with Ken Philbrick, Partner and Founder of Adam James International, an executive search firm that recruits CEOs, CFOs, and other senior leaders for private equity-backed middle-market companies.

Ken shares what he is seeing firsthand as AI begins reshaping finance, accounting, executive recruiting, and the talent market. The conversation explores automation of repeatable accounting functions, the growing connection between finance and technology, and why AI fluency and intellectual curiosity are becoming increasingly important for CFOs and other executives.

They also discuss what AI still can’t replace: judgment, relationships, behavioral insight, and the human nuance required when hiring and leading at the executive level.

And for executives preparing for their next opportunity, Ken offers an important insight: companies are already beginning to include AI capabilities in executive scorecards and expectations. For CEO and CFO candidates, demonstrating how AI fits into a 90-day plan may increasingly become part of the conversation.

In this episode:

• How AI is changing finance and accounting organizations

• What executive recruiters are seeing from CEOs and CFOs

• Why finance and technology are becoming more interconnected

• Where AI can automate work and where human judgment still matters

• AI fluency as an emerging executive leadership competency

• How companies can encourage adoption while managing risk

• What AI could mean for the future talent pipeline

• Why curiosity and adaptability may matter more than ever

If you’re a CFO, CEO, finance leader, private equity professional, or executive navigating how AI is changing your organization and your career, this conversation offers a practical look at what’s already happening in the market.

What we cover

It's not your database data. The AI-readiness conversation is really about the messy, unstructured stuff — SharePoint files, conversations, one-off projects — not the operational data warehouse you've had for 20 years.

Categorization is the real bottleneck. Before anything else works, you have to decide how to model and bucket your data, and make sure those buckets connect. Most teams get stuck right here.

Not everything in your files is authoritative. That folder your summer intern filled isn't a source of truth just because it's in SharePoint.

You can't shortcut the learning curve. Veer and Jim talk about why "getting the reps in" matters — skip the hands-on iteration and you lose the nuance that makes these tools actually useful.

Build or buy your "second brain"? The two dig into what it means to build (or buy) an AI system for a company — ownership, champions, "pioneers" inside the org, and why the right call depends heavily on where you are in your maturity curve and how close you are to an exit.

The maintenance nobody budgets for. Whichever path you choose, the system needs ongoing care — security, infrastructure, search tooling — as your data and scale grow.

A preview of what's next. Jim teases an upcoming deep dive on the AI maturity curve and power users, plus mentions of new guest interviews and more on "OpenBrain" in future episodes.

Notable moments

1:18 — Jim: "They're not talking about your operational data warehouse that you've been using for 20 years. They're talking about all the other stuff that adds the color to that financial or operational data."

2:45 — Jim on the real issue with data modeling: "You've got to really decide how to categorize all of this data... those buckets all have to be connected with a pipe."

3:45 — Veer: "The files that are in SharePoint that your summer intern uploaded are not necessarily authoritative documents, right?"

10:08 — Jim frames the core question: "If you were going to build a second brain... for your company... do you build this or do you go find [a vendor]?"

15:41 — Veer: "There's a durability with buy that you don't get with build, especially if it's not your competency."

18:33 — Jim's takeaway: as your data and scale grow, you'll have to keep rethinking both your maintenance and your search tooling.

Timestamps

0:00 — Categorizing and modeling data
4:00 — Reflecting on lessons learned
7:20 — Importance of hands-on experience
10:46 — Aligning investments with company goals (build vs. buy)
15:59 — Teaser: the AI maturity curve
18:33 — Scaling and data management, key takeaway

We'd love your feedback — what should we go deeper on? What could we cut? Drop a comment and let us know.

Episode Highlights

• The Reality Behind the Brochure: Jim Boswell and Veer Hossain share why their Open Brain prototypes—meant to be the digital second brains—work well… until they really don’t.
• The Raccoons Analogy: Turns out, today’s AI tools often feel like "three raccoons in a trench coat." Yes, it sounds ridiculous—but so is believing one click solves enterprise search and data nightmares.
• Personal Use Cases & Real Revenue: Veer Hossain discusses how his system actually saved time, made money, and pulled context for consulting proposals faster than a caffeine jolt.
• CRM Heartbeating: Jim Boswell’s OpenBrain combs through emails, preps his daily top 5 messages, and ensures no client thread is dropped—though, as always, human review is a must.
• Data Problems Don’t Magically Disappear: Still using that 1997 spreadsheet? So is your AI. Veer Hossain explains why bad data makes for bad answers, no matter how shiny the LLM.
• Surprise, You’re in IT: From rotating API keys to ongoing database maintenance, personal AIs generate their own tech debt. It’s not a “set and forget” world—welcome to your new hobby.
• Scaling Lessons for Enterprises: Every pain Jim Boswell and Veer Hossain felt building solo gets multiplied (and much more critical) at organizational scale. AI changes work dynamics, but not the laws of data and common sense.

Key Takeaways

• If you’re thinking AI will just “figure it out,” expect a lot of digital trash pandas rifling through fifteen copies of Final_Version_V2.xlsx.
• Maintenance, data curation, and being explicit about what you want are the new table stakes.
• LLMs are powerful, but they’re not mind readers—and handing off your work without review is still a career-limiting move.
• Building your own tools forces you to learn fast what “good” actually looks like—often by failing first.

Join Jim Boswell and Veer Hossain for this dry-witted, reality-check episode—ideal for digital execs, AI-curious leaders, and anyone tired of tech evangelists promising too much, too soon.

Subscribe for more no-nonsense talk on building practical, functional digital tools—minus the hype.

The conversation focused on the widening gap between AI hype and actionable adoption in mid-market companies. A key theme that emerged was the persistent focus on the latest AI models and industry arms races, while overlooking the pragmatic ways AI can transform less glamorous—but critically important—business workflows. The discussion explored why the typical headlines about “game-changing” models miss the real concerns of most companies and how functional leaders should reframe their approach.

Key Topics & Highlights

The AI Barbell: Frontier Labs vs. Tiny Startups

• Several points were raised, including the tendency for media to spotlight either mega-corporations burning mountains of cash or trendy micro-startups, while the economic engine—the midmarket operators—gets ignored

Beyond AI Hype: What’s Actually Useful?

• One concept discussed was the difference between talking about technology and extracting real business value from it.
• Instead of asking, “What’s the latest model?” a more valuable question is, “How does this help my team operate faster, with less grind, and more accuracy?”

Model Size, Quantization, and the Geeky Details

• There was a breakdown of "quantization," which basically means compressing large language models to run on smaller, local devices—trading off some precision for speed and privacy
• The group compared this to giving street-by-street directions instead of a pinpoint GPS—less accurate, but often good enough

Local Models vs. Cloud: The Loneliness of the Desktop AI

• Small models keep things private, but if only one analyst can use it, we’re right back where we started with locked-up spreadsheets. A key theme that emerged was the need for good architecture: how will your company balance privacy, collaboration, and resilience?

The Real Value: Automate the Boring, Focus on the Game-Changing

• The discussion explored real-world examples, like automating a 13-week cash flow forecast or report generation. The focus was on freeing up smart humans to actually interpret and act on the data—not grind through compiling it
• Using AI to build durable, repeatable scripts (think of it as your own private IT team without the ticketing system) was highlighted as a difference-maker

Adoption Frustrations & Advice for Action

• Despite the buzz, adoption remains sluggish among even tech-savvy organizations—a surprising revelation for all involved
• The bottom-line advice: Stop waiting for the “perfect” model. Pick a use case, get reps, and build momentum. Budget a few bucks for professional development—skip the overpriced conference and just play with the tools already

Get Involved

• Which workflows have you quietly revolutionized? How is AI actually showing up in your organization—beyond the hype? The team wants your feedback!
  • Drop a comment or shoot a message on LinkedIn.

Next time: Expect more hands-on stories, and maybe a few guests who’ve stopped talking and started doing. Until then—close the newsletter tab and start building.

In this episode, Veer Hossain and Jim Boswell are joined by Justin Rae, CEO of Cinch, to geek out about AI agent orchestration, local inference on SparkBoxes, and the future of "self-driving" marketing. The crew dig into what’s actually working at scale (spoiler: tinkering leads to full-blown ops), how to cut the fat from marketing workflows, and why handing the mundane off to bots is your new superpower.

Highlights

• From At-Bats to Galactic Swings: Justin Rae breaks down why getting reps with AI tools matters more than obsessing over which local model to run.
• SparkBoxes Galore: What workloads actually make sense to run locally? Justin Rae has three (and counting) running monitoring, code analysis, and more.
• Kill the Token Tax: Running continuous AI agents in the cloud is expensive. Local inference saves serious cash…and gives you ultimate control.
• Workflow Fixes, Not Just Flags: It’s not just monitoring—Cinch’s agents actually queue up and propose fixes, with humans as the final gate.
• Human-in-the-Loop Done Right: Build trust in your agents, level up the team, and become the AI orchestrator they’ll fight to hire in three years.
• Self-driving Marketing Roadmap: Like self-driving cars, there are levels. Justin Rae lays out where Cinch sits today and where it’s headed.
• Level Up, Don’t Fear Up: This isn’t about killing jobs—it’s about empowering people to punch above their weight and own the new tools of the trade.

Where to Find Cinch

• Events:
  • Pestworld
  • SEMA
  • Apex (November)
  • Various regional shows in home and automotive services throughout the summer
• Website: cinch.io
  Want to put their AI voice agent to the test? Sign up for a demo or check out the self-serve option right from their homepage.

Notable Quotes

“The future is you managing your own agents. I want you to leave here the most AI-versed marketer anywhere.” – Justin Rae

“Why don’t we have self-driving marketing yet? You’re not going to kill someone if you botch an email, right?” – Justin Rae

For the exec who wants to game harder and manage smarter: get your hands dirty, reap the at-bats, and show your agents who’s boss—before someone else does.

Like what you heard?

• Subscribe on your favorite podcast platform.
• Give us a shout if you automate something over the weekend instead of mowing the lawn.
• Connect with Justin Rae and Cinch at cinch.io or at any of the events above.

What happens when you stop seeing AI as just another chatbot and start wielding it like a pneumatic nail gun? In this episode, the team sits down with Justin Rae, CEO of Cinch, to dissect what it really takes to integrate AI into ops, products, and even the back office. From orchestrating multi-agent AI stacks to hacking the digital equivalent of "knowing your customer," this conversation is loaded with dry wit, real-world stumbles, and the relentless pursuit of 10x gains.

Highlights

• AI Isn’t Just a Chatbot Anymore
  • Discussion centered on evolving AI from "just a chatbot" to orchestrating real-world tasks—answering phones, making data-driven decisions, and driving measurable business results.
  • Emphasis on the challenges of building, maintaining, and trusting AI systems. It’s about more than hype: when an AI fails, who fixes it?
• From Data is the New Oil to Actionable Customer Insights
  • "Data is the new oil" still rings true, but the power is now in execution—taking the raw data, matching it to customers, and replicating the kind of relationship the old-school shopkeeper had with regulars, just at digital scale.
  • AI is unlocking hyper-personalized marketing, like tailoring offers and tones in emails based on granular customer habits and preferences.
• Real-World Machine Learning Use Cases
  • At Cinch, the team uses machine learning to segment customers by value and price sensitivity, not just at launch but updated dynamically as habits and markets change.
  • This means customers aren’t treated equally—if a previously valuable client now lags the pack, the system catches it and adapts outreach accordingly.
• Iterate (and Fail) Your Way to Wins
  • The company culture is built around hands-on experimentation with AI, including regular “build something cool with AI” contests—gift cards for the winners, and bragging rights for days.
  • Not everything works: iterative “at-bats” are essential. The key is giving people space for a good first experience, which unlocks imagination and adoption.
• Change Management, Minus the Corporate Spin
  • You want to get knowledge workers, engineers, and everyone from sales to support using AI? Make it about reducing real pain—prioritizing tickets, finding faster answers, masking data for demos on the fly.
  • Incentives and space to experiment beat another round of PowerPoint-driven “AI transformation” meetings every time.

Quick Takeaways

• Try more, judge less. The path to AI ROI is paved with failed experiments and accidental discoveries—just like the training data.
• Make experimentation safe and worthwhile. Incentivize, don’t just assign.
• Hyper-personalization isn’t science fiction. Mapping data to behavior means marketing finally gets truly one-to-one.

Timestamps of Note

• [01:21] AI’s role beyond chatbot basics
• [05:30] Data-driven customer value segmentation
• [09:08] Customizing marketing tones and content
• [14:13] Building internal AI culture with contests
• [19:26] Overcoming mental and procedural barriers
• [24:07] Real examples of AI driving business efficiency
• [29:50] AI as the power tool of modern work

Functional Executives—yes, change is hard, but the 10x upside is real once you evolve past the “it’s just another chatbot” phase.

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."

Key Takeaways

• 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

Moments of Note

• “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

Listen & Connect

• Podcast: Available on Apple Podcasts, Spotify, YouTube
• Newsletters: Find us on LinkedIn
• 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.

Key Topics Discussed

• 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.

Gen Z AI Sentiment (Gallup Poll Data)

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%

Actionable Advice & Prompting Tips

• 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.

Top 5 Most Notable or Impactful Moments

1. Learning Mindset Over Tool Obsession

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"

2. The Champion for Change

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

3. Low-Friction AI Integration

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

4. Cost Focus vs. Real Value

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

5. Encourage Experimentation and Learning from Failure

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

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

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

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

Top 5 Most Notable or Impactful Moments

1. The Need for Authenticity in AI-Assisted Writing

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

2. Automation of Tedious Tasks Using AI

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

3. Emphasis on Cross-Functional Human Collaboration

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

4. Encouragement to Start Small and Iterate

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

5. The Value of Building Personal or Brand Voice Skills in AI

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!

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.

1. The Reality of AI Maturity in Mid-Sized Companies

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

2. The Shift Towards "AI by Default"

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

3. The Importance of Common Sense in AI Policy

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

4. The Limitations of Chasing Flashy AI Projects

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

5. Acknowledging the Gray Areas in Technology Adoption

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!

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

Timestamps & Key Topics

• 00:00:00 – Introduction and goal-setting: Why building your second brain matters and who can do it
• 00:00:56 – Taking a measured approach: Avoiding the urge to “Leroy Jenkins” your project
• 00:02:29 – The real value of chat histories and how to decide what’s worth saving
• 00:03:15 – Tech stack high overview: Supabase as the foundational database
• 00:04:00 – Supabase edge functions: Cost, flexibility, and efficiency explained
• 00:05:15 – Adding intelligence: OpenAI embeddings, PGVector, and semantic search
• 00:10:08 – Input surfaces: Telegram, Slack, and building your own data endpoints
• 00:12:03 – Automating data ingestion: Scripts, chunking, and making use of documents
• 00:16:01 – The importance of memory items: Keeping only what’s truly valuable
• 00:20:09 – The future vision: Agentic AI, automated prep, and your AI-powered executive assistant
• 00:22:34 – Closing thoughts: Staying competitive and getting started now

Episode Highlights

• Learn why Jim Boswell and Veer Hossain think building your second brain is the next edge for knowledge workers
• Step-by-step tips on setting up Supabase, using PGVector for semantic search, and automating knowledge capture
• Real-world examples: From consulting decks to chat histories and daily briefings
• Honest talk on over-optimization, staying curious, and evolving with the tech

Key Topics:

• Introduction to the "second brain" concept
• Semantic understanding and vector databases
• Importance of primitives: documents, meetings, tasks
• Tools for implementation: Superbase, PG Vector, embedding models
• Strategies for capturing valuable data
• The humorous Leroy Jenkins analogy

Timestamps:

08:46 - Introduction: Veer and Jim introduce themselves and the podcast

09:00 - Introducing the personalized "second brain"

11:00 - Building a contextual memory system for LLMs

14:00 - Data efficiency and prompt optimization

17:00 - Architecting a decentralized knowledge system

20:00 - Semantic understanding and vector embeddings

25:00 - Integrating multiple models and ecosystems

31:00 - Building primitives: documents, meetings, tasks

35:00 - Capture surfaces and input methods

40:00 - The Leroy Jenkins analogy

Resources & Links

• Superbase
• PG Vector Extension
• OpenAI Embedding Models

Connect with Jim and Veer:

• Veer Hossain - LinkedIn
• Jim Boswell - LinkedIn

Key Topics:

• Global AI Adoption
  Veer Hossain and Jim Boswell open with a discussion around the statistic that 84% of the world hasn’t meaningfully interacted with large language models, exploring access issues and digital entitlement (01:18).
• Fluency: Curiosity vs. Grit
  The hosts debate whether curiosity or grit is more fundamental for mastering new tools, concluding that curiosity comes first, setting the stage for deeper engagement with rapidly evolving technologies (05:18).
• Enterprise AI Challenges
  The episode features a compelling story about a major automotive manufacturer’s struggle with customer data deduplication and how new AI tools can address legacy problems that even billion-dollar firms face (07:19, 12:21).
• Small Biz vs. Big Biz: Speed and Risk
  The pair contrasts the nimbleness of smaller organizations with the risk-aversion of larger ones, wrestling with how budgeting and token usage factor into widespread AI tool deployment (14:12).
• AI in Practice: Claude & ChatGPT for Power Excel Users
  Jim Boswell shares detailed, practical workflows for using language models to streamline Excel model building—highlighting the need for precise, context-rich prompts to control costs and get quality output (24:36, 28:25).
• Fluency & Taste: Developing Good Judgement
  Veer Hossain brings in the notion of 'taste' in AI—a sense for when outputs are truly high quality, not just technically correct. They discuss the necessity of explicit feedback for AI agents, not just relying on one tool but “cross-examining” results for best outcomes (35:06, 38:38).
• Experience + AI = Superpowers
  The hosts agree that years of experience in a field dramatically improves the quality of AI interactions, making prompting more effective and results more meaningful (41:26).
• Looking Forward: Personal Skills and Taste Curation
  The episode ends with a teaser for future discussions on “skills”—the next evolution in personalizing your interaction with AI, and how to encode your own judgement and taste into automated systems (42:01).

Quotable Moments:

• “Curiosity comes first, because the grit doesn’t matter if you’re not curious.” — Veer Hossain (05:18)
• “We’re still coding. The language is just English.” — Jim Boswell (29:23)
• “Taste is going to be the next differentiator in the use of AI.” — Veer Hossain (35:50)

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Have questions or feedback? Drop us a line—we’d love to hear from you!

Key themes include overcoming traditional skill barriers, practical software and agent-building journeys, product-market fit, context management with AI, and the shifting economics of small investments in technology.

Practical Takeaways

• You no longer need a coding background to build and deploy real, valuable products—leverage your domain expertise, curiosity, and modern AI tools.
• The key barrier now is understanding business problems deeply (product-market fit), not the mechanics of software development.
• Iterative, disciplined prompt writing, requirement gathering, and context management are critical in DIY AI projects.
• Small, targeted "bets" on software projects can have outsized ROI in today’s landscape.
• Security and governance must be revisited as more people in organizations can build and run software—embrace (don’t ignore!) the shift.

Notable Quotes

• “The skill of coding is not the constraint. It’s about figuring out what problem you’re trying to solve and if the market values it.” — Jim Boswell 00:07:04
• “You are going to get this… quantum jump, this giant leap of value—not just because the technologies exist, but because men and women with industry experience can now wield them.” — Veer Hossain 00:12:31
• “We don’t want to be a part of the hype. We want to figure out: how do I actually use this to do something meaningful to add value?” — Jim Boswell 00:11:39

Links & References

• Tools mentioned: ChatGPT, Cursor, Manus, Substack, Reddit, WSJ, YouTube
• Concepts: Agentic AI, Product-Market Fit, Context Rot, Microfinance Inspiration

Next Episode Teaser

Stay tuned for deeper dives into how functional executives should evaluate which AI projects to greenlight, and a candid conversation about security, risk, and the future of empowered employees in technology!

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Functional Executives is available on all major podcast platforms. Subscribe for real-life examples, actionable strategies, and the inside edge on blending technology and enterprise value.

Top 5 Most Notable or Impactful Moments

1. The Value of Building in Public

Veer Hossain and Jim Boswell discuss the concept of "building in public," referencing Nate B. and Peter’s move to OpenAI, highlighting the importance and impact of sharing projects transparently and collaboratively with the public.

2. Fast Prototyping and Leveraging AI Tools

Jim Boswell describes creating a working prototype in just three days using Cursor and GPT, shifting from theory to practical implementation for his wife’s business, enabling voice-based reporting.

3. The "Human Side" and Team Communication Challenges

Jim Boswell discusses the dynamic, idea-driven thinking of founders and how this rapid pace can risk burning out teams who may not naturally operate in the same way.

4. Bringing AI into Strategic Decision-Making

Veer Hossain elaborates on how AI is not just an operational tool but can act as a strategic partner, weighing in on whether new features or projects truly add value before building them.

5. The New Accessibility of Tech Creation

Jim Boswell emphasizes that even non-coders can now build powerful solutions by reflecting on his own experience of creating operational apps without being a professional programmer.

Connect with Us!

• Ready to cut through the hype and gain tangible AI workflow insights? Subscribe to Functional Executives on Apple Podcasts or Spotify.
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• Share your AI challenges and wins with us—DM Functional Executives and connect directly with Veer Hossain and Jim Boswell for your functional executive questions.
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Key Highlights

• The 60-Minute Engineer: Veer breaks down how he used autonomous agents to build a personalized job-hunting tool, proving that the "Gap" between idea and execution is shrinking.
• Prompting as Collaboration: Why "prompt engineering" is actually just high-level management. Veer explains why his 25 years of industry experience was the secret ingredient in getting the AI to perform like a senior dev.
• The Wisdom Shortcut: We discuss how AI allows seasoned professionals to compress years of technical friction into immediate outcomes.
• A Roadmap for the Next Gen: Advice for the 22-year-old professional. We debate how to maintain humility and critical thinking while using tools that accelerate the learning curve.
• The Cost of Dismissiveness: A look at the mindset shift required to stay relevant. Why curiosity and risk-taking are the only defenses against professional stagnation.

Key Highlights

• The Search for Structure: Why the real value of AI isn't in the "new," but in bringing clarity to complex, messy business systems.
• The Intuition Gap: As AI begins to analyze conversational nuance and power dynamics, we tackle the identity crisis of the modern leader: what remains uniquely human?
• The "Goldilocks Zone" of Tech: A deep dive into the danger of "skill rot" and how to use automation without becoming dangerously dependent on it.
• The "Owl on the Shoulder" Metaphor: Why AI should function as a helper for context, not a replacement for the emotional intelligence required to make final decisions.
• Closing the Gap: Reflections on the "creative gap"—how doing the volume of work today leads to the mastery required for tomorrow’s executive functions.