November 19, 2025

Artificially Intelligent, Actually Useful.

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AI That Scales With You

This week's newsletter explores the critical connection between operations and successful AI automation. The theme came together at Tony Robbins' AI Advantage Summit. Rachel Woods shared her perspective on successful AI integration which reinforced something I think everyone knows: garbage in, garbage out. The businesses that succeed with AI aren't just the ones deploying it fastest. They're the ones that take the time to evaluate and strengthen their operational foundation before building AI automations.

The insight across all the lead articles is timing. Whether you're transitioning from startup to established business or scaling into the millions or billions, the key is refining and future-proofing your operations before growth demands it, not after. A perfect example is the CDC's 527% return on AI automations which simply came from identifying clear operational bottlenecks and then using AI to systematically eliminate them.

Ad hoc ChatGPT use increases productivity about 30%, but systematizing AI and having the right people in place unlocks gains of 300% to 3000% and codifies expertise into scalable processes

Operational inefficiency costs the average business 25% of annual revenue, yet most companies prioritize sales over the operational foundation that makes everything possible

Refining processes before automating them prevents AI from scaling operational flaws, especially before inflection points so your team drives revenue not infrastructure

From 30% to 3000%: The Right AI Team Wins Out

Most teams using ChatGPT daily are seeing about 30% productivity gains, but this is nowhere near AI's full potential. In this interview Rachel Woods makes a compelling case that the real unlock happens when you move from "random acts of AI" to systematized AI operations. Her research shows that businesses implementing AI systems see productivity improvements of 300% to 3000%. The difference isn't what AI can do; it's how you deploy it.

Woods identifies three critical roles that successful AI-first companies establish. The AI Visionary sets strategic direction and identifies high-impact opportunities. The AI Operator is a process-oriented manager who bridges business processes and AI capabilities, documenting workflows enough to become AI instructions. The AI Implementer builds, tests, and maintains the systems.

Key Insight: Success with AI comes down to whether you have a team translating "this could work with AI" into "this consistently works with AI." And it's the AI Operator who connects business needs to technical execution.

Watch Key Segment →

View full interview

The Hidden Cost of Operational Debt

Operational inefficiency quietly drains 25% of annual revenue from the average business, yet companies consistently prioritize revenue-generating activities over operational foundations. The critical inflection points happen during scaling transitions. Informal processes that work perfectly at five people break completely at fifty. Only 4% of U.S. businesses reach $1 million in revenue, and just 0.4% surpass $10 million. Why? Because operational systems aren't updated before growth exposes system weaknesses.

Strategic Take-away: AI and automation deliver maximum value when you improve operational systems before growth hits, connecting tissue between systems, not just the headline-grabbing enterprise platforms.

Read Full Article →

Refine First, Automate Second

The "Lean Automation Framework" targets the right operational work: repetitive back-office tasks that drain capacity without adding strategic value. And while this article and interview focus on startup transitions, the same principle applies at every inflection point: refine processes before automating them. AI learns from the patterns you feed it, and will happily replicate your operational flaws at scale if you haven't cleaned them up in advance.

Key Insight: Automate operational support before growth demands it. When systems are already in place, your team drives growth instead of scrambling to build infrastructure.

Read Article / Interview →

Quick Hits

Build AI Operations for the Future

Automating repetitive operational tasks with AI requires rigorous risk assessment before deployment. Consider "red teaming" where the AI team intentionally pushes AI to behave badly in controlled environments to identify and correct risks before release.

Why Most AI Pilots Fail

AI pilots fail from limited governance, messy data, and unclear ROI. Strengthen your operational systems first, then systematically automate them. Governance frameworks and success metrics provide the structure that turns AI pilots into sustainable automations.

Massive ROI From Operational AI Automation

The CDC achieved 527% ROI by deploying a GenAI chatbot handling administrative tasks, an AI-powered report analysis saving 5,500 labor hours, and adding AI computer vision to cooling tower identification reducing staff workload from 4 hours to 5 minutes.

Industry Developments

Gemini 3.0 Arrives as Google Accelerates AI Integration

Google released its newest version of Gemini with immediate availability in Search, the Gemini app, and developer platforms. The model achieves record scores on industry benchmarks and introduces Gemini Agent, which can execute multi-step tasks like organizing inboxes and booking travel, rather than just providing guidance.

Healthcare Startup Automates Admin Workflows with AI

Sandy Health raised pre-seed funding to automate hospital operational workflows including prior authorizations, insurance verification, and scheduling. The platform aims to free clinicians from administrative tasks, allowing them to reclaim hours daily for patient care while improving reimbursement timelines.

Alright, that's a wrap for the week!

The common thread here isn't about AI capabilities; it's about operational readiness. The businesses struggling with AI aren't typically struggling with technology. They're struggling with operations that were never designed to scale.

The "Don't Automate Chaos" principle is more real than most people realize. When you automate unvetted processes, you don't just scale your problems, you multiply them. That's why the timing of operational investment matters so much and the sweet spot is during transitions. The question isn't whether your team has access to AI; it's whether your operations are structured well enough that AI will help.

Thoughts?

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Refine First, Automate Second

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Got a project in mind?Tell us about it.

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Tell us about it.