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It's easy to get caught up in the AI hype, but excitement can stop us from seeing the practical steps needed to make AI truly work. At Appian, we recognize that AI is at its most powerful within a process. Before you get to embedding AI in process, however, you must determine if AI is what you need.

You turned on an AI feature in your analytics tool. It surfaced an insight about your pipeline. You looked at it, paused, and closed the tab because you weren’t sure the number was right. AI-ready data would have made you forward it instead. It’s data that is clean, structured, and governed consistently enough that an AI model can reason about your metrics without a human translating or reconciling them first.

Every analytics vendor claims AI. Few can prove their AI is doing real analytical work. Here is what executives need to verify before committing budget to an AI-powered analytics tool.

The challenge of any aerospace company is to deliver new capabilities without compromising safety, reliability, or precision. At our current juncture, legacy technology runs into conflict with modern tool stacks. Artificial intelligence (AI) creates fissures in compliance and auditability, and innovation and productivity gains come at a cost of greater complexity. Despite these seismic shifts, the central question remains the same.

“Garbage in, garbage out.” We are not the ones who said this, George Fuechsel did. But when we are talking about AI today, it is hard not to repeat it. We spend a lot of time discussing what AI can do, the outputs, the predictions, the impact it can create. Much less attention goes to what is actually going into these systems.

A strategic framework for using AI-powered quality engineering to accelerate delivery without sacrificing regulatory requirements.

A guide to learning how MCP registries help govern AI agent-to-tool connectivity AI agents are only as capable as the tools they can reach. When an agent needs to query a database, file a support ticket, or pull data from a CRM, it has to find the right tool, authenticate, and invoke it — all at runtime. The Model Context Protocol (MCP) standardizes how agents communicate with these tools. But MCP alone does not answer a fundamental question: how does the agent know which tools exist?

A VP of Marketing presents an AI-generated performance review on a Monday morning. The CAC numbers are clean. The trend lines are directional. The exec summary recommends a $200K budget reallocation from paid search to organic content. The CFO nods. The budget shift is approved before lunch. Two weeks later, an analyst spot-checks one figure against the source system. The number doesn’t exist anywhere in the connected data.

Most AI analytics tools added a chatbot to a dashboard and called it intelligence. These eight actually change how fast your team goes from question to decision.

For years, credit underwriting was pretty straightforward. Lenders looked at a few fixed factors like credit scores and income, to decide who was worthy of a loan. If you didn’t fit the criteria, you were simply rejected. It worked, but only to a point. This approach left out many people who were actually creditworthy and often missed subtle shifts in market stability.