lyr3: AI, PE and Change
Do We Need Embeddable UIs in MCP?
Chat boxes have become the face of AI. We type, the AI replies, and when tools are connected through MCP (Model Context Protocol), the outputs are poured back into text. Clean, minimal plumbing. But MCP-Universe, a benchmarking project, shows just how brittle that model is. Only about one-third of complex interactions succeed when the AI has to juggle multi-step orchestration entirely through chat. That raises a question: are we overloading the AI, and forcing the chat box to do too much?
MCPs: The Choose-Your-Own-Adventure Games of Enterprise AI
When people hear “Model Context Protocol (MCP),” it sounds abstract and technical—like plumbing for AI systems. But if you strip it down, MCPs are really just goal-oriented choose-your-own-adventure games written for AI.
MCP Universe and AI limitations
On LinkedIn I wrote a short piece on how the limitations of AI impact the design and architecture of AIUX, the process of designing interfaces for AI. We will need to pay close attention to how this develops.
Making Excel AI Friendly
MDN (Markdown Notation) project defining a simple, AI-readable spec for representing tabular and contextual data for AI systems
Strategic decision feedback
An application that uses AI to provide critical feedback in go-to-market decisions. Outcome: Startup concept, effective technology but could not resolve on GTM. Retired.
Don't Build AI Tools. Build With AI.
The real opportunity in the AI era isn't building AI tools, but creating AI-native companies that fundamentally rethink how value gets created and delivered.
AI in Regulated Contexts
Exploring how compliance, audit, and defensibility change when AI becomes part of the workflow. Outcome: Developed AI tool to write compliance training scripts and documents. Client Project
The Future of Proofreading: AI Consensus Systems
How combining multiple AI proofreaders with consensus-based decision making can achieve better results than either humans or AI alone. Client Project
The Three Layers of AI Data
Understanding the three critical data layers that form the backbone of reliable AI decision-making: Training Data, Proprietary Data, and External Data.
Streamlining External Data with Preprocessed Repositories
How preprocessed data repositories can streamline external data integration for corporate AI systems, reducing complexity and accelerating time-to-value.
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