Business AI Explained

Nico Simko (Clair): $100M ARR with 70 people, and the plan for $1B with under 200

Nico Simko (Clair): $100M ARR with 70 people, and the plan for $1B with under 200 ### Description Nico Simko runs Clair, which lets US workers get paid early. In the last year they went from ~$30M to close to a $150M run rate, with a team of about 70. His own math: without AI, Clair would need 400 people to be where it is today. The plan is $1B in revenue with fewer than 200. We talked about: - scaling past $100M without a sales team - the agent that shipped a whole feature on its own, and still missed the context - why the bottleneck moved to their bank and payroll partners - open-source vs frontier models, and when the cheaper one wins - what hiring looks like for AI-native engineers and PMs Chapters (00:00) Intro (01:02) What Clair got right (04:30) $100M+ without a sales team (06:46) What stays human (08:35) AI across every team (11:49) The new bottleneck (14:30) New AI use cases vs. the mission (18:37) Moats in the AI era (20:57) Hallucinations and model costs (23:10) Hiring AI-native engineers and PMs (25:23) Strategy and go-to-market in 2026 (27:49) $1B with fewer than 200 people (29:15) Wrap-up Watch on YouTube: https://www.youtube.com/watch?v=v0eruAaxqaQ Book an AI operations audit call: https://cal.com/vladimir-de-ziegler-uyu7qb/ai-operations-audit-discovery-call Elements: https://www.elementsagents.com/ Vlad on LinkedIn: https://www.linkedin.com/in/vladeziegler/ Clair: https://getclair.com/ Nico on LinkedIn: https://www.linkedin.com/in/nsimko/ Business AI Explained: how entrepreneurs actually use AI to grow their businesses.

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I just hosted the largest AI event in history. What I learned about AI adoption - Igor Pogany

Igor Pogany just hosted the largest AI event in history — 600,000+ people — and grew a 400K+ channel teaching mid-career professionals how to actually use AI. So I asked him the obvious question: what did running it all teach you about using AI? His answer isn't about prompts or the newest tool. It's about context: stop treating AI like a vending machine, start feeding it your context, and use it to amplify the expertise you already have instead of trying to reinvent yourself. Igor Pogany — founder of The AI Advantage and now partnered with Tony Robbins and Dean Graziosi — teaches mid-career professionals (average age 50+) how to master AI instead of fearing it. We get into the practical playbook: why you should amplify your existing expertise instead of reinventing yourself, how to give AI enough context that it stops people-pleasing and hands you opinions you can trust, the two-rule test for avoiding AI slop, how to grow an audience by matching your viewer's worldview, and the "company brain" he uses to run a 50-person media company on AI. In this episode: • Why you should amplify your existing expertise with AI instead of reinventing yourself • How to give AI enough context that it stops people-pleasing and tells you the truth • The two rules for avoiding AI slop (never go 0 to 1, always seed it) • How to grow an audience by matching your viewer's worldview — and what a thumbnail really is • Why social media is networking at scale — and how Igor ended up working with Tony Robbins & Dean Graziosi • The "company brain" and Alfredo Hub setup Igor uses to run a 50-person company on AI Chapters: 0:00 The world's largest AI event (intro) 2:50 Who Igor helps: mid-career pros vs AI anxiety 4:10 Amplify what you do — don't reinvent yourself 6:18 The fastest first win: photograph your fridge 7:41 Using AI for decisions you can trust 8:36 Context: how to stop AI people-pleasing 13:25 Creating content with AI — without the slop 15:17 The real skill: turning chaos into content 17:30 Match your audience's worldview 19:07 Grow an audience: networking at scale 23:26 Compacted knowledge = time travel 25:47 The Alfredo Hub: one place for every AI tool 29:23 Company brain: a shared source of truth Guest: Igor Pogany, Founder @ The AI Advantage. Igor Pogany is the founder of The AI Advantage, one of the most-watched practical-AI channels on YouTube (400K+ subscribers), and Head of AI Education for the AI Advantage program he runs with Tony Robbins and Dean Graziosi. He teaches professionals how to use AI to save time and amplify the expertise they already have. Connect with Igor: https://www.linkedin.com/in/igorpogany/ 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.

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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

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$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.

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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.

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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

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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/ → Company: 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/ Subscribe / follow Vlad: → LinkedIn: https://www.linkedin.com/in/vladeziegler/ → AI with Vlad: https://www.youtube.com/@aiwithvlad

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AI Adoption Playbook for Sales, CS and Marketing | Charlotte Lucas, ScorePlay

AI adoption usually breaks when companies try to force it top-down or keep it stuck in experimentation. Charlotte Lucas is Head of Strategy at ScorePlay, where she’s driving AI adoption across the business to help teams in sales, customer success, marketing, and operations move faster and work smarter. As ScorePlay competes in sports broadcasting with a lean team, Charlotte is building practical AI workflows that save time, improve execution, and help teams scale without losing the human touch. In this episode of Business AI Explained, we break down what AI adoption actually looks like when it works: how to get buy-in, where to start, which workflows to prioritize, and how to combine executive sponsorship with team-level ownership. We also cover how AI can support relationship-driven work, why shared learning matters, and how to build trust in AI outputs across the company. We cover: Why AI adoption works best when leadership sponsors it, but teams own the use cases How to start with small, practical workflows that build trust quickly How ScorePlay is using AI across lead generation, deal desk ops, customer success, and marketing Why peer learning, internal sessions, and shared channels accelerate adoption How to match the right AI tools to the right teams What strong governance looks like: access, review loops, and exception handling How AI can strengthen relationship-driven work instead of replacing it Why automating high-impact, low-complexity workflows creates leverage across the business How unstructured data can become a strategic advantage with the right AI workflows What it takes to build an AI culture that works both top-down and bottom-up This conversation is for founders, operators, GTM leaders, strategy teams, and department heads trying to move from AI experimentation to real adoption across the company. Episode length: ~45 minutes 👤 ABOUT THE GUEST Charlotte Lucas Head of Strategy, ScorePlay Charlotte Lucas leads strategy at ScorePlay and is driving AI adoption across the company to help teams save time, improve execution, and support sales, customer success, and marketing at scale. → LinkedIn: https://www.linkedin.com/in/charlotte-lucas-ba479125/ → Company: https://www.scoreplay.io/ 🔗 WORK WITH VLAD If you’re implementing AI in your operations and want hands-on help building real workflows: 👉 https://www.elementsagents.com/ 🔔 SUBSCRIBE → Linkedin: https://www.linkedin.com/in/vladeziegler/ → AI with Vlad: https://www.youtube.com/@aiwithvlad Watch the full video version on YouTube: https://www.youtube.com/@aiwithvlad New episodes every Tuesday.

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AI in Sales: How Enterprises Use Chatbots, Data & Automation to Close More Deals

AI is no longer just a buzzword in sales, it’s becoming the backbone of how companies engage customers, qualify leads, and scale go-to-market. In this episode, I’m joined by Adis Ceman, Regional Enterprise Sales Director at Cequens, a company building AI-powered communication systems across channels like WhatsApp, SMS, and voice. We go deep into how enterprises are actually using AI today, beyond the hype. We cover: How AI chatbots are replacing missed opportunities in customer support Why most companies fail at AI How to structure your data and prompts to get real results How AI is used in enterprise sales (lead generation, qualification, demos) The role of human touch in high-value deals (and why it still matters) How to use AI internally to train teams, build proposals, and move faster What signals actually matter when selling to enterprise clients Why speed + personalization are the real competitive advantages in 2026 One of the most valuable insights: AI doesn’t replace sales teams, it makes them faster, sharper, and more responsive. If you're building a product, running sales, or thinking about implementing AI in your company, this episode will give you a clear, practical view of what works. Episode length: ~24 minutes 👤 ABOUT THE GUEST Adis Ceman Regional Sales Director at Cequens - UAE → LinkedIn: https://www.linkedin.com/in/adisceman/ → Company: https://www.cequens.com/ 🔗 WORK WITH VLAD If you’re implementing AI in your operations and want hands-on help building real workflows: 👉 https://www.elementsagents.com/ 🔔 SUBSCRIBE → Linkedin: https://www.linkedin.com/in/vladeziegler/ → AI with Vlad: https://www.youtube.com/@aiwithvlad Watch the full video version on YouTube: https://www.youtube.com/@aiwithvlad New episodes every Tuesday.

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How AI Is Rewriting Hiring and Operations | Sean Griffith (Founder of Truffle)

Many companies are experimenting with AI. But very few are restructuring their operations around it. In this episode of Business AI Explained, Vladimir de Ziegler sits down with Sean Griffith, founder of Truffle, to discuss how AI is reshaping hiring, software workflows, and internal operations. Sean explains how his team is able to produce the same level of output with fewer than 10 people compared to what he previously experienced inside a publicly listed company doing over $300M in ARR. The conversation explores how startups and modern teams are beginning to replace traditional software tools with AI-driven workflows and how leaders can rethink operations to become fully AI-native. Sean also explains how Truffle is redesigning the hiring process using AI-assisted candidate screening, transcription, and evaluation to help HR teams move from hundreds of applicants to a shortlist much faster. The conversation covers: • How startups can control costs when rolling out AI features • Quick SEO opportunities to appear in LLM search results • How developers are replacing traditional software with Claude Code • How to structure operations for AI-native teams • Which tools and workflows companies should automate first • How AI is transforming hiring and recruitment processes If you're building products, leading a startup, or exploring how AI can reshape your internal operations, this episode provides practical insights into the next generation of AI-native companies. Episode length: ~30 minutes 👤 ABOUT THE GUEST Sean Griffith Founder of Truffle → LinkedIn: https://www.linkedin.com/in/griffithsean/ → Company: https://www.hiretruffle.com/ 🔗 WORK WITH VLAD If you’re implementing AI in your operations and want hands-on help building real workflows: 👉 https://www.elementsagents.com/ 🔔 SUBSCRIBE → Linkedin: https://www.linkedin.com/in/vladeziegler/ → AI with Vlad: https://www.youtube.com/@aiwithvlad Watch the full video version on YouTube: https://www.youtube.com/@aiwithvlad New episodes every Tuesday.

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AI Implementation in Sales & Product Teams | Alexis d'Eudeville (Lemlist)

Most companies talk about AI. Very few actually implement it in production. In this episode of Business AI Explained, Vladimir de Ziegler sits down with Alexis d'Eudeville, AI Product Manager at Lemlist, to discuss how AI is being used inside real companies. Alexis shares practical lessons from building AI products, launching startups, and working at Google. They explore how AI is transforming product management, sales automation, and go-to-market strategy and why the most important factor is still keeping humans in the loop. The conversation covers: • How AI is implemented inside modern sales teams • The role of generative AI in product management • Why data quality matters for AI adoption • How companies can move from experimentation to AI in production • Practical examples of AI improving marketing and customer success If you're a founder, operator, or builder trying to understand how AI is actually used inside businesses, this episode breaks down the real strategies behind AI implementation. Key Topics: AI implementation in business AI in product management Sales automation and RevOps Go-to-market strategy AI in production systems Chapters: 00:00 Introduction – Alexis d'Eudeville & AI at Lemlist 01:45 AI in Business: Why Implementation Matters 05:12 How Companies Are Using AI in Production 08:30 Generative AI for Content and Data Analysis 12:45 Human-in-the-Loop AI and Ethical Considerations 18:20 The Future of AI Tools in Business Workflows 22:15 Real AI Examples in Marketing and Customer Success 27:40 AI Adoption Challenges for Startups and SMEs 31:05 How Companies Can Successfully Implement AI 35:50 Key Takeaways: AI Impact on Business Growth 38:15 Final Thoughts on the Future of AI Episode length: ~40 minutes 👤 ABOUT THE GUEST Alexis d’Eudeville AI Product Manager at Lemlist → LinkedIn: https://www.linkedin.com/in/alexis-d-eudeville-348bb858/ → Company: https://www.lemlist.com/ 🔗 WORK WITH VLAD If you’re implementing AI in your operations and want hands-on help building real workflows: 👉 https://www.elementsagents.com/ 🔔 SUBSCRIBE → Linkedin: https://www.linkedin.com/in/vladeziegler/ → AI with Vlad: https://www.youtube.com/@aiwithvlad Watch the full video version on YouTube: https://www.youtube.com/@aiwithvlad New episodes every Tuesday.

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AI in E-Commerce: Automation, Positioning & Trust (with Tim Masek, Storetasker)

AI in e-commerce is transforming automation, positioning, and trust for modern online brands. In Episode 4, I sit down with Tim Masek, CMO of Storetasker, to break down how AI is actually being used inside real e-commerce businesses. Not demos. Not theory. Real execution. We cover: Using AI for copywriting and site structure Rethinking positioning with AI Automating workflows without losing originality Why trust still matters more than tools How expert networks outperform random freelancers Where AI creates leverage and where it doesn’t Tim shares how AI has become indispensable in strategic work saving thousands in external consulting and accelerating execution. If you run an e-commerce brand or care about practical AI adoption, this episode gives you a grounded view of what’s actually working. Episode length: ~49 minutes 👤 ABOUT THE GUEST Tim Masek CMO of Storetasker → LinkedIn: https://www.linkedin.com/in/timmasek/ → Company: https://www.storetasker.com/ 🔗 WORK WITH VLAD If you’re implementing AI in your operations and want hands-on help building real workflows: 👉 https://www.elementsagents.com/ 🔔 SUBSCRIBE → Linkedin: https://www.linkedin.com/in/vladeziegler/ → AI with Vlad: https://www.youtube.com/@aiwithvlad Watch the full video version on YouTube: https://www.youtube.com/@aiwithvlad New episodes every Tuesday.

AI in Operations for Hardware Companies - Eliott Wertheimer, VanMoof CEO

AI in operations for hardware companies looks very different from AI in software, especially after a bankruptcy. Eliott Wertheimer is the CEO of VanMoof, the iconic European e-bike brand rebuilding after bankruptcy. In this episode of Business AI Explained, we explore AI in operations for hardware companies and what it takes to rebuild trust, reliability, and economics in a physical product business. We cover: Why VanMoof’s brand survived bankruptcy The economics that killed VanMoof 1.0 (low margins, returns, D2C) Rebuilding trust through over-delivery, not marketing promises Where AI actually fits in hardware companies (support, supply chain, maintenance) Why AI should be an operational tool, not a product feature Why brand and design matter more as hardware gets easier to build This episode is for founders, operators, and product leaders working on hardware, consumer brands, or complex operations and trying to apply AI without breaking trust. Episode length: ~40 minutes 👤 ABOUT THE GUEST Eliott Wertheimer CEO of VanMoof Former McLaren (Lavoie), founder of Fuero Systems Aerospace engineer turned hardware operator → LinkedIn: https://www.linkedin.com/in/eliott-wertheimer-6910a1b6/ → Company: https://www.vanmoof.com/ 🔗 WORK WITH VLAD If you’re implementing AI in your operations and want hands-on help building real workflows: 👉 https://www.elementsagents.com/ 🔔 SUBSCRIBE → Linkedin: https://www.linkedin.com/in/vladeziegler/ → AI with Vlad: https://www.youtube.com/@aiwithvlad Watch the full video version on YouTube: https://www.youtube.com/@aiwithvlad New episodes every Tuesday.

GTM AI Teams: The 3:1 Dev Ratio (And Why It Works) - David Arnoux

GTM AI teams break down the moment you try to scale beyond a few workflows into real operations. David Arnoux is a fractional GTM AI strategist who sits inside companies building AI implementation, not just advising. He runs Gen AI Circle (400+ heavy AI adopters) and has worked with scale-ups and enterprise companies transforming their go-to-market teams with AI. In this episode of Business AI Explained, we break down how AI-native GTM teams actually work, what the team structure looks like, and why the best ones now run a 3:1 ratio of developers to marketers. We cover: How to audit a company's AI readiness (and the red flags that mean they're not ready) The maturity levels of AI implementation: from basic prompting to autonomous loops Performance marketing as a self-learning loop (campaigns that optimize themselves) Why "repo ownership" is now strategic power, the person who controls context controls outcomes The 3:1 developer-to-marketer ratio in cutting-edge GTM teams (and why AI is literally the third team member) Buy vs. build framework: when to use off-the-shelf tools vs. building custom The two internets theory: one for humans (doom scrolling), one for agents (decision-making) Why SEO is becoming "programmatic listicles" to game LLMs The revenge of the 1950s: why authenticity, friction, and in-person events matter more than ever How to work remotely while transforming companies (90% remote, 10% in-person) This conversation is for CMOs, CROs, GTM leaders, and operators who need to figure out how AI actually fits into their team structure, not just tools, but people. Episode length: ~45 minutes 👤 ABOUT THE GUEST David Arnoux Fractional GTM AI Strategist | Founder, Gen AI Circle Works with scale-ups and enterprise companies on AI GTM transformation. Builds ventures through Humanoids studio (including ViralBrain.ai) → LinkedIn: https://www.linkedin.com/in/davidarnoux → Company: https://www.heyarnoux.com/ → Community: https://www.thegenaicircle.com/ 🔗 WORK WITH VLAD If you’re implementing AI in your operations and want hands-on help building real workflows: 👉 https://www.elementsagents.com/ 🔔 SUBSCRIBE → Linkedin: https://www.linkedin.com/in/vladeziegler/ → AI with Vlad: https://www.youtube.com/@aiwithvlad Watch the full video version on YouTube: https://www.youtube.com/@aiwithvlad New episodes every Tuesday.

Business AI Explained - How AI Actually Gets Implemented in Companies (Trailer)

Business AI Explained is a podcast about how AI actually gets implemented inside real companies. I’m Vlad Ziegler, founder of Elements Agents. For the past two years, I’ve helped companies move from “we want to use AI” to actual production systems that drive measurable ROI. Every conversation starts the same: “We want to use AI. We just don’t know where to begin.” The gap isn’t technical knowledge. It’s operational knowledge: → What actually works in production? → What breaks after launch? → What’s the realistic ROI timeline? → How do teams structure AI internally? → What mistakes cost companies months? This podcast is about real implementation stories. No hype. No theory. No futurism. Just operators sharing what they built, what failed, and what worked. Launching February 24th with three episodes: → Abraham Gomez (Google) on AI in GTM → Eliott Wertheimer (VanMoof CEO) on rebuilding after bankruptcy → David Arnoux on structuring AI GTM teams New episodes every Tuesday. Subscribe now so you don’t miss the launch. 🎥 Watch full episodes on YouTube: https://www.youtube.com/@aiwithvlad 🌐 Learn more about Elements Agents: https://www.elementsagents.com/ 🔗 Connect with Vlad on LinkedIn: https://www.linkedin.com/in/vladeziegler/

AI Implementation in Go-To-Market (GTM) - Abraham Gomez, Google

AI implementation in go-to-market (GTM) breaks down the moment it moves from demos into real teams. Abraham Gomez is a Strategic Startups Customer Engineer at Google, where he has advised 400+ founders on AI implementation in go-to-market (GTM) teams. In this episode of Business AI Explained, we break down how AI actually gets implemented inside sales, marketing, and GTM operations and why most teams struggle once AI moves beyond experimentation. We cover: How to choose your first AI implementation in GTM The 80/20 rule: which AI workflows actually deliver ROI Buy vs. build decisions for AI tools (including when not to use Google’s) Accuracy vs. precision in AI systems, and why domain expertise matters more than models Why AI adoption fails even when the tech works How to plan AI projects knowing models change every 6–12 months This conversation is for founders, COOs, RevOps leaders, and operators who need AI to work inside real GTM teams, not just look good in demos. Episode length: ~50 minutes 👤 ABOUT THE GUEST Abraham Gomez Strategic Startups Customer Engineer at Google Works with startups implementing AI in GTM, operations, and core workflows. → LinkedIn: https://www.linkedin.com/in/goabego/ → WhoInvitedAbe Podcast: https://www.youtube.com/@WhoInvitedAbe → Website: https://goabego.com/ 🔗 WORK WITH VLAD If you’re implementing AI in your operations and want hands-on help building real workflows: 👉 https://www.elementsagents.com/ 🔔 SUBSCRIBE → Linkedin: https://www.linkedin.com/in/vladeziegler/ → AI with Vlad: https://www.youtube.com/@aiwithvlad Watch the full video version on YouTube: https://www.youtube.com/@aiwithvlad New episodes every Tuesday.