Author: Keith Baldwin

Prompts are useful. Prompts are not architecture. That distinction matters because many AI assistant projects begin with a prompt and never grow beyond it. Someone writes a clever instruction. The model responds well in a demo. The output looks impressive. A few people get excited. The organization starts thinking it has an AI assistant. It […]

Generic AI produces generic value. Business-specific AI produces business-specific value. That distinction matters because most organizations do not need a random chatbot bolted onto the side of the business. They need reusable AI assistant capabilities that understand their departments, workflows, documents, systems, rules, permissions, and approval processes. An IT department does not work like HR. […]

Most businesses do not need another AI demo. They need AI assistant capabilities that can survive real business use. That means the capability needs to be testable. It needs to be reusable. It needs to be secure. It needs to be maintainable. It needs to integrate with existing systems. It needs logging, error handling, permissions, […]

Most businesses make AI harder than it needs to be. One department wants a chatbot. Another wants a Microsoft Teams assistant. Another wants a Power App. Another wants AI inside an internal web application. Another wants workflow automation. Another wants an API. Another wants to talk about agents. Those may sound like different projects, but […]

Most businesses should not start their AI strategy by asking, “Should we build a chatbot?” That is the wrong starting point. A better question is: What reusable AI assistant capabilities should the business build, test, govern, and expose through the right interfaces? That question leads to a stronger architecture. Instead of building isolated chatbots, disconnected […]

Microsoft-based businesses are in a strong position to benefit from AI. Many already use Microsoft 365, Teams, SharePoint, SQL Server, Power Platform, Azure, .NET applications, and custom internal systems. They already have business data, documents, workflows, user permissions, identity management, and existing software infrastructure. That is a major advantage. But it also creates a strategic […]

AI terminology has become a mess. Businesses hear about AI assistants, chatbots, Microsoft Copilot, AI agents, copilots, automation, workflow AI, custom GPTs, retrieval-augmented generation, and enterprise AI platforms. The result is predictable. Executives, managers, IT leaders, and department heads often use different words to describe the same thing — or worse, use the same word […]

Many businesses are approaching AI from the wrong direction. They start with the visible interface. They ask: “Should we build a chatbot?” “Can we add AI chat to our website?” “Can employees ask questions through Teams?” “Can we connect this to SharePoint?” Those are reasonable questions, but they are not the most important questions. The […]

Choosing the right first Intelligent Document Processing project matters. A good first project builds confidence, proves business value, creates reusable architecture, and gives the organization a practical path for expanding IDP into other document-heavy workflows. A bad first project does the opposite. It creates delays, frustrates users, exposes weak assumptions, burns budget, and makes leadership […]

Document AI is powerful. It can read scanned documents, extract fields, identify layouts, classify forms, and turn unstructured information into structured candidate data. That is valuable. But many teams make a costly mistake: They use Document AI for parts of the workflow that do not require AI. That leads to higher costs, slower systems, harder […]

Intelligent Document Processing is often discussed as if it were one tool. That is the wrong way to think about it. In real enterprise environments, IDP is not just one AI service, one workflow tool, one database, or one application. It is a system that turns messy, unstructured documents into structured, validated, workflow-ready business data. […]

Intelligent Document Processing projects often get into trouble because teams confuse three very different things: Prototype.MVP.Production system. They are not the same. A prototype proves an idea might work. An MVP proves the idea can provide useful business value in a limited real-world scenario. A production system proves the organization can rely on the process […]

Most Intelligent Document Processing teams start with extraction. That makes sense. The first question is usually: Can the system read the document and extract the data? But that is not the question that determines whether an IDP system is production-ready. The harder and more important question is: Can the business trust the extracted data enough […]

Intelligent Document Processing demos are usually impressive. A clean invoice is uploaded.The AI finds the vendor name.The total is extracted.The date is captured.The result appears in a nice structured format. Everyone nods. The demo looks easy. Then the system gets tested against real business documents. That is where things change. In production, documents are not […]

Intelligent Document Processing, or IDP, is often presented as a simple automation story: upload a document, extract the data, and send the results to a business system. That is a useful demo. It is not a production system. In real enterprise environments, documents are messy. Forms change. Scans are blurry. Vendors use different formats. Employees […]