In my first post on Data Readiness, I introduced the notion of a Data Readiness activity to provide the AI development team with the political power to make the resources (typically data and subject matter…
A Data Lens is a specification of the data needed to support an AI development project. It reflects the scope of the business problem/opportunity laid out by the sponsors, but has sufficient detail to act…
An AI project often starts in a back room, as a series of experiments. Its operation at that time can be low key and informal. At the other end of the spectrum, an AI project…
In my previous post, I outlined five key pillars: Scope, Compliance, Trustworthiness, Understandability, and Cost. While these pillars provide a design framework, moving an AI application from an interesting experiment to a production-grade tool is not a…
Scientific data includes context information (metadata) to make its creation reusable in downstream analysis. This metadata comes with a vocabulary that is helpful in understanding how business data – that is data from the systems…
It’s widely recognized that data preparation is critical for AI projects. But where does it actually fit in the development process? In my previous post, AI Application Development – Building with Context Intelligence, we explored…
From Planning to Building In our last installment, we discussed project definition and the context and clarity it can provide. In this installment, we’re going to jump into the part that most techies hanker for—the…
In our previous blog, we introduced the AI Application Journey—a framework showing how business context flows through five connected phases. Project Definition is the first and perhaps most critical phase, establishing the foundation that guides…
Organizations are investing heavily in AI—deploying the latest LLMs, implementing sophisticated RAG systems, building agents with MCP tools. Yet a surprising number of AI projects struggle to reach production or fail outright. The issue is…
AI technology is rapidly evolving, making it difficult to tie down the best practices and lifecycle flow for an AI development project. The AI development team must grapple with new concepts, technologies and complex development…