Why Most AI Training Fails at Work | Elise Masurel de Laval

How to Actually Implement AI in Business — Michael LaVista

Most companies are buying AI, then quietly failing at the last mile. Michael LaVista has spent 25 years shipping the hard final stretch — here's how he gets AI into production. Michael LaVista, founder of Caxy (a Chicago software firm that's spent 25+ years staying a few steps ahead of the market), breaks down "last-mile AI" — why most AI projects run out of steam right at the end, and how to actually get them into production. We cover finding use cases worth building, the AI stack you can realistically hire for, driving adoption against real resistance, value-stream mapping to find the work that matters, guardrails for autonomous agents, and the rule that saves the most pain: solve the problem first, pick AI second. Real AI in production, not theory. In this episode: • What "last-mile AI" is — and why most AI projects run out of steam right at the end • How to find AI use cases worth building (let 100 ideas compete, keep the home runs) • Why automating tasks you already do is a weak AI strategy — and what to do instead • How to pick an AI stack you can actually staff and maintain • Driving adoption when part of your team is quietly resisting AI • Value-stream mapping, guardrails for autonomous agents, and when NOT to build it yourself Chapters: 0:00 Cold open: pick the problem, not the hype 1:20 Last-mile AI: where AI projects quietly die 2:56 Why automating tasks is a losing bet 3:53 "The board said use AI" — $100K/mo, zero accountability 5:45 Finding use cases: let 100 ideas compete 6:54 Idea to working software in 6 minutes 8:07 The AI stack: pick tech you can actually hire for 9:52 Adoption & the people who resist AI 11:25 Value creation beats automation (proposals, IKEA) 15:43 Right tool for the job: when NOT to use AI 18:47 Value stream mapping: find the red-pen process 21:28 Coding vampires & the autonomous-agent holy grail 23:25 Guardrails: prompt injection & Bedrock 24:58 Three steps ahead: a world where everyone builds 27:46 Final advice: problem first, then go get help Guest: Michael LaVista, Founder & CEO @ Caxy. Michael LaVista is the founder and CEO of Caxy, a Chicago-based software and AI transformation firm. For 25+ years he's helped mid-sized companies turn AI from a board mandate into something that actually ships — what he calls last-mile AI. Connect with Michael: https://www.linkedin.com/in/michaellavista/ Connect with Vlad: • LinkedIn: https://www.linkedin.com/in/vladeziegler/ • YouTube: https://www.youtube.com/@aiwithvlad • Work with Vlad (Elements Agents): https://www.elementsagents.com/ • Come on the show: https://cal.com/vladimirelements/podcast-intro-call — Business AI Explained is a podcast for founders and GTM teams on how AI creates real business impact — real examples, real constraints, lessons from AI in production. Hosted by Vlad de Ziegler, founder of Elements Agents. ━━━━━━━━━━━━━━━━━━━━━ #BusinessAI #AIForBusiness #AIimplementation #AIstrategy #AIadoption

$200M Selling Chicken In Under 3 Years — Leander Gruss, How Lanch Built a $200M Business with AI-native operations — Leander Gruss

Lanch went from zero to $200M in under three years selling chicken. Leander Gruss, Head of Growth at Lanch, will explain us how. In this episode: • Why you should never "vibe-code" your source of truth — and how to decide build vs buy • How a non-technical growth lead actually ships internal tools (the exact stack) • The 2-day prototype rule that stops AI builds from sprawling • Running shared AI agents for SQL, customer support and procurement across the org • Why clean, structured data — not bigger prompts — makes agents accurate • The buy-vs-build math that's wiping out their monthly software bills Chapters: 0:00 Cold open: own the transaction 1:08 Meet Leander & Lanch (0 to $200M) 3:41 The data warehouse that runs the business 6:09 Writing SQL in minutes, not weeks 7:22 Build vs buy: what NOT to vibe-code 8:22 Building the route-optimization tool 10:26 The build stack: Replit, Lovable, Claude Code, Vercel, Supabase 11:55 Driving AI adoption + PRDs + cost limits 15:39 Langdock & sharing agents across the org 18:38 Context engineering & RAG for retrieval 24:25 Benchmarking & evals 26:33 Buy to build economics & maintenance 32:45 Lovable + git + skills = 3-4x dev cycle 37:40 Where to find Leander Guest: Leander Gruss, Head of Growth @ Lanch. Leander Gruss leads growth at Lanch, the creator-backed food-tech company behind Loco Chicken and Happy Slice. He joined as one of the first employees and helped scale it from zero to $200M. Connect with Leander: https://www.linkedin.com/in/leander-gruss-aa1280147/ Connect with Max: https://constructlabs.com/ Connect with Vlad: • LinkedIn: https://www.linkedin.com/in/vladeziegler/ • YouTube: https://www.youtube.com/@aiwithvlad • Work with Vlad (Elements Agents): https://www.elementsagents.com/ • Come on the show: https://cal.com/vladimirelements/podcast-intro-call — Business AI Explained is a podcast for founders and GTM teams on how AI creates real business impact — real examples, real constraints, lessons from AI in production. Hosted by Vlad de Ziegler, founder of Elements Agents.

Why Most AI Agent Projects Die at the Last Mile — Idan Raman

Most AI agent projects stall at the last mile: the legacy browser workflows no API can reach. Idan Raman, founder of Anchor Browser, joins Vlad to explain why browser agents are the missing piece in enterprise AI, how the Cloudflare partnership is changing the economics of automation, and why the OpenClaude security fallout was a wake-up call for anyone running computer-use agents in production. If you're evaluating AI agents for your business, this is the layer of the stack nobody's explaining clearly. In this episode: - Why 90% automated still isn't automated — the KYC last-mile problem - Computer use vs browser use, and when each one makes sense - The real security story behind the OpenClaude virality (leaked credit cards, stolen passwords) - How enterprises are pricing AI: the voice AI framework ($3/call → $0.30) applied to back-office work - Why open source LLMs are exploding inside large enterprises - The Cloudflare "web bot protocol" and why it's a win for everyone - Building a moat in AI infrastructure: "if it's hard, it's good" Chapters: 00:00 Intro 00:52 Computer use vs browser use 03:34 KYC as the poster child 06:08 The Cloudflare partnership 09:01 When to use browser agents vs Playwright 10:56 Security and the OpenClaude fallout 14:05 Open source LLMs in the enterprise 17:08 Pricing AI tools for enterprise 21:19 Building a real moat in AI 25:24 Dogfooding Anchor to grow Anchor 29:45 What's next Guest: Idan Raman, Founder @ Anchor Browser. Idan built Anchor Browser to solve the last-mile automation problem for enterprise AI agents. Connect with Idan: https://www.linkedin.com/in/idan-raman/ Anchor Browser: https://anchorbrowser.io/ Connect with Vlad: - LinkedIn: https://www.linkedin.com/in/vladeziegler/ - YouTube: https://www.youtube.com/@aiwithvlad - Work with Vlad (Elements Agents): https://www.elementsagents.com/ - Come on the show: https://cal.com/vladimirelements/podcast-intro-call — Business AI Explained is a podcast for founders and GTM teams who want to understand how AI creates real business impact. Hosted by Vlad de Ziegler.

Why Most AI Training Fails at Work | Elise Masurel de Laval

AI training usually fails for a simple reason: it is too generic. In this episode of Business AI Explained, I sit down with Elise Masurel de Laval, co-founder of Catalyst.ai Academy, to break down what makes AI training actually work inside companies. Elise and her team focus on practical AI training for non-technical roles, with a strong emphasis on role-based use cases and learning by doing rather than abstract demos.  They discuss why AI adoption needs to be tied to real business workflows, why peer-led learning often beats formal training, and how companies should think about shadow AI, governance, and long-term capability building. They also explore why AI agents create excitement early on but often become difficult to maintain unless internal teams can truly own them. They cover: *  Why most AI training programs fail  *  The case for role-specific AI enablement  *  Why peer practitioners are often the best teachers  *  How to build relevance through real company use cases  *  Why AI communities can outperform static courses  *  How to think about shadow AI without overreacting  *  Balancing experimentation with governance  *  The hidden maintenance cost of AI agents  *  Why practical adoption matters more than tool knowledge  *  How non-technical teams can build AI confidence quickly  This episode is for founders, operators, enablement leaders, consultants, and anyone trying to move from AI awareness to real adoption across teams. About the guest Elise Masurel de Laval is co-founder of Catalyst.ai Academy. Catalyst describes its approach as practical AI training for non-tech roles, and the company’s about page highlights Elise’s background in marketing, digital, sales, innovation, and executive leadership in education.  Where to find Elise: → LinkedIn: https://www.linkedin.com/in/elise-masurel-de-laval-2579b999/?skipRedirect=true → Company: https://catalystacademy.ai/ Work with Vlad: If you’re implementing AI in your operations and want hands-on help building real workflows: → https://www.elementsagents.com/ Subscribe / follow Vlad: → LinkedIn: https://www.linkedin.com/in/vladeziegler/ → AI with Vlad: https://www.youtube.com/@aiwithvlad

From Bankruptcy to Building Booxkeeping: Max Emma on Franchising, Bookkeeping and AI in Operations

Max Emma started in landscaping and construction, then got hit hard during the 2008 recession, a period that eventually led to bankruptcy and some hard-earned lessons about risk, financial management, and resilience. He later co-founded Booxkeeping, a bookkeeping business built around fixed pricing, operational efficiency, and standardized financial reporting. That business went on to scale through franchising, becoming a standout model in the bookkeeping industry. In this episode of Business AI Explained, Vlad sits down with Max to unpack that journey and explore how AI is being used inside real business operations today. They discuss where AI genuinely saves time, why custom AI builds often disappoint, and how off-the-shelf tools like ChatGPT and QuickBooks can create real leverage when applied to the right workflows. They cover: *  Lessons from the construction industry and the 2008 recession  *  The path from bankruptcy to building a new business  *  Why bookkeeping was the opportunity Max chose to pursue  *  How Booxkeeping scaled through franchising  *  Where AI works well in finance and ops  *  Why custom AI tools can be costly and impractical  *  How AI helps evaluate franchise territories faster  *  Using AI to automate notes, emails, and internal workflows  *  Why efficiency gains matter more than hype  *  How lower operating costs can become a competitive advantage  This episode is for founders, operators, franchise builders, finance leaders, and anyone trying to understand what practical AI adoption looks like inside a real business. Where to find Max Emma:  → LinkedIn: https://www.linkedin.com/in/maxemma/ [https://www.linkedin.com/in/maxemma/] → Company: https://www.linkedin.com/company/booxkeeping/ [https://www.linkedin.com/company/booxkeeping/] Work with Vlad:  If you’re implementing AI in your operations and want hands-on help building real workflows:  → https://www.elementsagents.com/ [https://www.elementsagents.com/] Subscribe / follow Vlad: → LinkedIn: https://www.linkedin.com/in/vladeziegler/ [https://www.linkedin.com/in/vladeziegler/] → AI with Vlad: https://www.youtube.com/@aiwithvlad [https://www.youtube.com/@aiwithvlad]