Reference
AI strategy glossary
The terms we use, defined plainly. Each is written to answer the question directly, whether you are a person or an answer engine reading it.
AI-nativeAI-native strategy consultingThe Production GapFractional Chief AI Officer (CAIO)Agentic AIAI technical due diligenceAI operating modelWrapper (AI)Generative engine optimization (GEO)AI use-case prioritization
- AI-native
- AI-native describes an organization or product where AI is part of how work happens, decisions are made, and things are built, by default. It contrasts with AI-assisted, where AI is added to processes that otherwise stay the same. Being AI-native is a stage of operating maturity, not a tool you buy.
- AI-native strategy consulting
- AI-native strategy consulting is advisory work that helps an organization set direction for AI and rebuild its operating model around it, delivered by people who both understand strategy and can ship AI systems. It goes beyond recommending AI to redesigning how the business works with it.
- The Production Gap
- The Production Gap is the distance between an AI demo that works once and a system that runs reliably in production. It has five parts: evaluation, integration, cost, trust, and ownership. Most generative AI pilots fail because they close none of them.
- Fractional Chief AI Officer (CAIO)
- A fractional Chief AI Officer is a senior AI leader engaged part-time or on an interim basis, typically one to three days a week. It gives a company executive-level AI judgment without the cost or delay of a full-time hire, and usually includes building toward the permanent role.
- Agentic AI
- Agentic AI refers to systems that pursue a goal over multiple steps by planning, calling tools, and acting, rather than answering a single prompt. Agentic systems raise the stakes on evaluation, guardrails, and ownership, because they take actions with real consequences.
- AI technical due diligence
- AI technical due diligence is an independent assessment of whether an AI company has a durable technical advantage. It examines architecture, proprietary data, dependency on model providers, unit economics at scale, and team capability, so an investor can tell a real moat from a wrapper.
- AI operating model
- An AI operating model is the way an organization structures people, processes, data, and technology so AI creates value repeatedly rather than in one-off pilots. Redesigning it is what turns AI from a set of experiments into a source of compounding advantage.
- Wrapper (AI)
- An AI wrapper is a product that adds a thin layer of interface or prompting over a third-party foundation model, without proprietary data, workflow depth, or a system a competitor could not quickly copy. The risk is that the next model release makes the wrapper redundant.
- Generative engine optimization (GEO)
- Generative engine optimization is the practice of making content easy for AI answer engines such as ChatGPT, Claude, Perplexity, and Google AI Mode to understand, trust, and cite. It extends search engine optimization to a world where the answer, not the link, is what reaches the user.
- AI use-case prioritization
- AI use-case prioritization is the practice of ranking candidate AI opportunities by value and feasibility so investment goes to the bets that will ship and compound. It replaces a scattered list of ideas with a sequenced roadmap.
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