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| The Foundation First Principle In this week's newsletter, we're reinforcing a point we've been making for a bit now: successful AI implementation requires a stable operational foundation. A few weeks ago we shared an article on why operations matter, and this edition has real-world evidence of what happens when organizations skip that step. From companies discovering that AI amplifies chaos without proper SOPs to construction firms learning about liability the hard way, the pattern is consistent: AI magnifies the processes you have - so tighten up your operating procedures before tackling AI automation. Meanwhile, significant shifts are emerging in AI's competitive landscape. Meta's Chief AI Scientist left to pursue visual learning systems that challenge the industry's LLM consensus, and Google's benchmark-leading Gemini 3 was trained entirely on Google's TPUs instead of Nvidia chips -- both of which threaten to upend AI's current infrastructure economics, or at least, cause a little stir. | Workflow automation (not task automation) can deliver 10x returns, but only when you automate stable operations. Leverage the CRAFT framework to create real stable competitive advantage. | AI scales whatever process already exists. If you deploy it on chaotic workflows you amplify confusion; build SOPs first, and you will amplify standards and efficiency instead. | Artificial Intelligence literacy and data readiness, not just model quality, determine whether organizations achieve actionable intelligence or simply automate existing chaos. |
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| The competitive edge isn't task automation, it's workflow automation. The CRAFT framework provides the blueprint: Clear Picture (document the workflow), Realistic Design (define minimum viable automation), AI-ify (build the process), Feedback (track improvements), and Team rollout (drive adoption). Organizations should start with high-frequency, clearly defined workflows where success is measurable. Target processes that are currently stable and repeatable like onboarding sequences, data validation routines, or content review workflows. Look for automation opportunities that either unlock new possibilities, leveraging AI's generative capabilities, or improve existing time-consuming and repetitive processes. Strategic Insight: The highest ROI often comes from unlocking possibilities previously thought to be impossible. However, transforming high-frequency workflows will deliver immediate, and measurable impact. Either way, revisit "failed" use cases every six months. |
AI doesn't create order, it scales whatever already exists. An organization deployed an AI-powered customer intake system without documenting their existing process and operations slowed and satisfaction dropped. The solution: Map (document how workflows actually happen), Measure (define success metrics), and Automate (apply AI to stable processes first). Organizations that build standard operating procedures before automating see smoother operations and expanded capacity. Strategic Take-away: Focus on processes which drive results. Start with one pilot area, learn from it, then expand. Without structure, automation amplifies confusion; with structure, it amplifies capacity. Read Full Forbes Article → AI readiness isn't an IT project. Rather, it's a company-wide transformation requiring coordinated progress. It starts with foundational work: enterprise alignment, governance frameworks, AI literacy, and data readiness. With that foundation, organizations can then move through implementation, optimization, and scaling. Common pitfalls include treating AI as plug-and-play technology, siloing initiatives within IT, and skipping governance entirely. The Practical Angle: Rushing to deploy AI tools leads to failed implementations. Achieving AI success means following the roadmap sequentially before deploying AI at scale. Review the Full Roadmap → | Quick HitsWhen AI Hallucinations Become Legal Liability Construction companies adopting AI for proposals and designs are facing liability issues when the AI hallucinates from incomplete data. The lesson applies across industries: companies need compliance frameworks and documentation trails for legal discovery. | Call of Duty's AI-Generated Art Triggers User Backlash Black Ops 7's use of AI-generated calling cards sparked intense criticism, contributing to the lowest user Metacritic score in franchise history. Users clearly rejected the AI content, prompting US Congressman Ro Khanna to call for regulations preventing companies from using AI to eliminate jobs for profit. | AI Agents Threaten to Commoditize Platform Giants Amazon is suing Perplexity after its AI shopping agent violated terms of service, highlighting how agentic AI threatens to commoditize platforms by stripping away ads and loyalty programs.
** This is a story worth following as AI agents will absolutely disrupt platform economics. ** |
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| Industry DevelopmentsMeta's Chief AI Scientist Says LLM Approach is Insufficient Yann LeCun is leaving Meta to launch a firm pursuing "advanced machine intelligence" by mimicking how babies learn through observation. He believes LLMs are insufficient for achieving human-level AI, contradicting the trillion-dollar industry consensus backing OpenAI, Google, and Meta itself. | Google's Gemini 3 Trained Entirely on TPUs, Not Nvidia Chips Google achieved state-of-the-art results with Gemini 3 using only their proprietary TPUs instead of Nvidia GPUs. If Google makes TPUs commercially available at scale and competitive prices, it could end Nvidia's dominance, triggering price wars, and commoditizing AI compute infrastructure. |
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