Return On Intelligence: Implement AI Without Wasting Time & Money

About Return On Intelligence: Implement AI Without Wasting Time & Money

Stop chasing the AI hype and start seeing actual results. Welcome to Return on Intelligence: Implement AI Without Wasting Time & Money, the podcast dedicated to dismantling AI myths and delivering actionable strategies for enterprise leaders. Powered by NineTwoThree Studio (https://hubs.li/Q03Z0k770), this show moves beyond "vibe coding" and "magic wand" promises to focus on what actually drives sustainable business value. Hosted by AI expert and NineTwoThree CEO Andrew Amann (https://www.linkedin.com/in/andrewamann/), along with Growth Marketing Lead Nay Cook-Nelson (https://www.linkedin.com/in/naycook/), each episode draws from the studio’s real-world experience building over 50+ custom AI solutions for billion-dollar brands and funded startups. We dive deep into: The 4-Week Discovery Playbook: How to validate ROI before writing a single line of code and avoid the common traps that cause 95% of enterprise AI projects to fail. Human-in-the-Loop Engineering: Why developers remain the ultimate decision-makers and how to use AI to empower your team rather than replace them. Enterprise-Grade Scalability: Moving from "cool prototypes" to robust, secure, and profitable applications that solve high-stakes business problems. ROI-First Implementation: Discover the frameworks that allow 90% of our projects (https://hubs.li/Q03-4q7t0) to achieve measurable returns within just three months. Whether you're a CEO looking for a competitive edge or a technical leader navigating the latest models like Gemini and Claude, we help you identify the right problems to solve—and the ones to ignore. Watch and Listen: Watch on YouTube: Return on Intelligence Channel (https://www.youtube.com/channel/UCvuGGpBCc1QC79KNK0zUreg) Join 3,000+ AI Leaders: NineTwoThree Studio Newsletter (https://hubs.li/Q03KQ_q60) for weekly insights on the latest adoption curves. Ready to see your own Return on Intelligence? Apply for a Free AI Project Evaluation (https://hubs.li/Q03KQ_q60) to see how we can turn your data into ROI.

Publisher

NineTwoThree AI Studio

Category

business · technology

Language

en

Explicit

No

First episode

8 Jan 2026

Latest episode

26 Oct 2026

Latest episodes

28 episodes in the feed.

1. 

   26 Oct 2026

   The Sad Truth About Personal AI Agents (Dots, Muse, Grok)

   Giving everyone Claude can make one employee faster while leaving the rest of the company buried in more work. How do you roll out AI so the whole team benefits? On this episode of Return on Intelligence, NineTwoThree CEO Andrew Amann and Head of Marketing Nahshon Cook-Nelson explain how to connect AI adoption to team workflows, company knowledge, and measurable business results. We walk through the four-step process we use with clients: align leadership, validate the problem, build the solution, and monitor what happens after launch. That includes setting rules for data access, choosing which problems to solve first, and checking whether AI spending produces useful outcomes. Take the AI readiness quiz: https://www.ninetwothree.co/tool/find-your-ai-readiness-score (https://www.ninetwothree.co/tool/find-your-ai-readiness-score) In this conversation: • Why individual productivity gains can create bottlenecks for other teams • How an AI constitution defines approved tools, data access, and security boundaries • Why your busiest senior employees can fall behind during an AI rollout • How to connect token costs to business outcomes • Andrew’s prediction for Anthropic’s expansion into cloud infrastructure • What “owned intelligence” means for your company’s knowledge and experience CHAPTERS 00:00 Why leaders should look past AI tool hype 00:42 Welcome to Return on Intelligence 01:27 Claude Dashboards, Motion, and the unified experience 02:09 Individual AI vs. team-wide results 04:54 Why traditional AI rollouts leave people behind 08:37 Our four-step AI implementation process 11:18 Moving beyond a personal AI assistant 12:50 Connecting token spending to revenue 13:58 Andrew’s predictions for Claude and cloud infrastructure 17:38 OpenAI’s consumer focus 19:06 Dots, GrokBot, Muse, and the distraction of new tools 20:20 Company knowledge and owned intelligence 22:51 Next steps and the weekly newsletter Where does work get stuck after someone on your team uses AI to finish their part? Tell us in the comments. Subscribe for more conversations about implementing AI across your business. #AIImplementation #EnterpriseAI #ReturnOnIntelligence

2. 

   10 Sept 2026

   Why companies are canceling Claude (Custom AI Harness Explained)

   Join 5,000+ leaders rolling out AI to their team: https://www.ninetwothree.co/guides/newsletter (https://www.ninetwothree.co/guides/newsletter) ___ Should your company build its own AI harness instead of relying entirely on Claude, ChatGPT, or another model provider? Michelle C., AI Product Manager at NineTwoThree, explains what a custom AI harness is, when building one makes business sense, and why companies want more control over their AI infrastructure, workflows, data, and costs. In this episode, we cover: • What an AI harness does • When a custom AI system is worth the investment • The risks of depending on one model provider • Where AI skills, tools, and business logic should live • How proprietary data and workflows create a stronger system • A real enterprise AI example, including the challenge, solution, and impact • Why cheaper AI models may still produce more expensive agent workflows • How security, infrastructure, and human oversight affect AI performance If your company is evaluating AI agents, Claude, ChatGPT, or a custom enterprise AI system, this conversation will help you decide what to own and what to buy. ABOUT THE GUEST Michelle C. is an AI Product Manager at NineTwoThree. She works with teams to plan and build AI products grounded in real business workflows. ABOUT NINETWOTHREE NineTwoThree helps companies plan, build, and launch custom AI, web, and mobile products. Learn more: https://ninetwothree.co (https://ninetwothree.co) SUBSCRIBE Subscribe for practical conversations about AI product development, AI agents, custom software, and the systems companies need to put AI into production. What part of your AI system does your company need to own? Share your answer in the comments. #ArtificialIntelligence #AIAgents #EnterpriseAI

3. 

   27 Aug 2026

   ML Engineer: "You've been lied to about open source"

   Download our free AI Implementation guide non-technical leaders: https://www.ninetwothree.co/guides/ai-playbook (https://www.ninetwothree.co/guides/ai-playbook) ___ Self-hosting an LLM sounds like a way to stop paying for tokens forever. The token bill turns out to be a small part of the cost. Nay Cook-Nelson talks with Vitalijus, a machine learning engineer with a PhD in natural language processing and 7+ years in data science. He has deployed self-hosted models for tens of thousands of businesses, starting years before GPT existed, back when training a model from scratch on your own data was the only option in machine learning. ⚡ IN THIS EPISODE ▸ What "self-hosted" means in 2026, and how it differs from a serverless API call ▸ The 15 things you inherit the moment you leave a third-party provider ▸ Why an H100-class GPU at $25K to $40K only gets you to your first few concurrent users ▸ How a model priced 50% lower per token can cost more in production ▸ Hybrid routing, and how to evaluate before you swap models ▸ The team you need: ML engineer plus DevOps or MLOps, on duty for the life of the project ▸ Where self-hosting is the only option: sovereignty, edge, IoT, and zero-connectivity environments ▸ The token pricing trap, and what model deprecation does to a product built on prompts 🕒 CHAPTERS 00:00 What you get from this episode 00:53 Seven years in data science, a PhD in NLP, and self-hosted deployments before GPT 01:58 Self-hosting vs. serverless APIs 03:37 Why every ML model used to be trained from scratch, and what BERT changed in 2018 06:13 Model distillation, and how DeepSeek got competitive at a fraction of training cost 07:50 What you inherit: serving, scaling, load balancing, autoscaling, failover, monitoring, batching, caching, quantization, GPU utilization, rollbacks, upgrades, guardrails 10:35 A 120B open-weight model needs an H100-class GPU at $25K to $40K. Then a fifth user shows up 12:45 Verbose models eat your margin. Prompt caching cuts cost up to 90%, and you build it yourself 13:48 Hybrid routing by use case instead of one provider for everything 14:53 Three ways to evaluate an LLM system: humans in the loop, performance metrics, models as judges 17:10 Zero-retention agreements, and when proprietary or medical data rules out an API 19:07 Who you need to hire, and why it is a department rather than two people 20:38 A healthcare client self-hosted Langfuse, provisioned 7 to 8 resources for one dashboard, then moved to a paid zero-retention tier 23:26 When self-hosting is the only option: 100% uptime, aircraft, IoT, EV charging in garages with no signal 24:50 US-hosted open-weight inference, and how to get cheaper tokens without buying GPUs 26:17 Infrastructure carries about 95% of this decision 27:14 The token pricing trap. Under 5% separates the top model from the tenth, per Stanford's AI Index 30:27 A version bump does not mean a better model for your workload 📌 THE TAKEAWAY Open-weight models are good enough for most workloads, so picking one is the straightforward part. Infrastructure, evaluation, and staffing decide whether the move pays off. Vitalijus's rule: know your load, know your user count, build a baseline with real evaluation, then experiment. Swapping models without that baseline will cost you.

Contact Return On Intelligence: Implement AI Without Wasting Time & Money

Guest appearances

Books guests

Based on episode analysis; this does not confirm that the show is currently accepting guests.