The AI Placement Paradox
Most enterprise AI strategies fail not because of poor models, but because of poor placement. We treat AI as a feature—a chatbot bolted onto a dashboard, a summarizer tacked onto a report. But real leverage comes from architecture, not features. The decision of *where* you place AI in your stack determines whether it becomes a compounding asset or a technical debt liability.
The Framework: Three Altitudes of Intelligence
To create a "wow" factor that scales, you need to stop thinking about use cases and start thinking about topography. There are three distinct layers where AI can live.
# Type 1: The Embedded Core (AI for You)
*The Apple Intelligence Model.* Here, AI sits shallow in the user experience but deep in the infrastructure. It dissolves complexity. It’s not a "tool" the user visits; it’s the invisible hand that sorts the notifications, optimizes the query, or predicts the failure. Quick Win: Identify the highest friction internal workflow (e.g., ticket triage, data cleaning). Embed a small, specialized model to act as a permanent "intern" in that loop to collapse the default manual steps.
# Type 2: The Horizontal Layer (AI for Us)
*The Platform Model.* This is the sweet spot for scale. You build AI as a shared service layer that both your internal teams and your external partners can call. You aren't just selling a product; you are providing the intelligence infrastructure for others to build upon. Quick Win: Expose your internal metadata and context via consistent APIs (like MCP). Allow your BI tools and internal apps to "ask" your platform questions, turning your data stack into a semantic engine.
# Type 3: The External Utility (AI for Them)
*The Bedrock Model.* You provide the pipes, they bring the intelligence. This is great for speed—letting customers bring their own models to your data—but it costs you strategic control. Action: Use this for edge cases where you can't compete on context, but never make it your core strategy.
The Methodology: Moving the Constraint Upstream
To implement this for scale, follow this 3 step action plan: 1. Audit the Friction: Don't ask "Where can we use AI?" Ask "Where is the human middleware?" Where are people manually bridging gaps between tools? 2. Select the Layer: Choose your placement. Do you need to dissolve internal complexity (Type 1) or enable an ecosystem of builders (Type 2)? 3. Build the Feedback Loop: If your AI implementation doesn't return data that makes your core platform smarter, you are just renting intelligence. Don't just build AI features. Build an Architecture of Intelligence.