Reframing AI: From Buzzword to Business Precision

The term AI has become a catch-all phrase that obscures more than it clarifies. This framework cuts through the noise by categorising AI based on function rather than technique, which is a vital shift for any leader serious about strategy. The core argument here is that AI is not one thing but six distinct capabilities: Analytical, Semantic, Generative, Agentic, Perceptive, and Physical. Each plays a different role—from deciding and remembering to creating and acting. This clarity is critical because it forces us to ask the right questions when someone says "AI". Without specificity, strategy remains vague and execution risks misalignment.

Commercially, this framework is a game changer. It moves the conversation from vague claims to precise value propositions. For marketing and product positioning, it means leading with what the AI actually does—how it decides, remembers, creates, or acts—instead of hiding behind the generic "AI-powered" label. It also highlights that competitive advantage lies not in isolated AI capabilities but in how these functions are orchestrated together in a coherent architecture. This multi-category stacking is where real differentiation emerges, especially in complex products like customer value management systems.

From a leadership perspective, this taxonomy demands a more disciplined approach to vendor evaluation and partnership. It encourages mapping offerings against these six categories to identify gaps and overlaps, ensuring the technology stack aligns with business goals. It also surfaces the need to understand interaction patterns—whether AI operates invisibly, assistively, conversationally, or through generative interfaces—and how these modes affect user experience and adoption.

What stands out is the emphasis on infrastructure concepts like the Model Context Protocol and GraphRAG. These are not buzzwords but foundational elements enabling interoperability and trustworthy AI. The mention of edge deployment reflects a pragmatic nod to privacy, latency, and cost considerations that too often get lost in AI hype.

There is a clear tension between the seductive simplicity of "AI" as a marketing term and the operational complexity required to deliver real impact. This framework challenges leaders and marketers to move beyond superficial claims and articulate a precise, functional narrative. It also signals that the future of AI in business is less about single flashy capabilities and more about sophisticated orchestration and integration.

In sum, this taxonomy is a call for precision and maturity in AI discourse. It offers a practical lens to dissect AI's multifaceted nature and align it with strategic priorities. For any senior operator, this is a valuable tool to cut through hype, guide investment, and sharpen how we talk about AI internally and externally.