Building the Data Lens for AI
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…
Data and AI-led business transformation
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…
Data Preparation in the AI Journey (Part 2)
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…
Matching the AI Data Lens to the Data Sources
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…
Data Preparation in the AI Journey (Part 1) – From Strategy to Operations
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…
AI Application Development – Building with Context Intelligence
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…
Context Intelligence – Project Definition – Establishing the Foundation
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…
From Idea to Impact: Navigating the AI Application Journey
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…
Data Readiness for AI
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…
Putting the Context into Context Engineering
Dan Wolfson Context engineering is one of the latest buzzwords across the AI community. The basic idea is to both improve the quality and manage the cost of a conversation with an LLM AI by…
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