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Practical AI

AI for Business: Separating What Works From the Noise

April 20268 min read

There is no shortage of AI promises. Every week brings a new tool, a new model, a new claim about what is now possible. For business leaders trying to make real decisions, the noise is exhausting.

This piece is not about what AI might do someday. It is about the patterns that consistently create genuine business value right now — and the ones that rarely do.

What Actually Works

The AI use cases with the strongest track record share a common trait: they are narrow, well-defined, and connected to a specific operational problem.

A few examples that deliver reliably:

  • Internal knowledge assistants that help employees find answers across documents, policies, and past decisions. The ROI is in time saved per query multiplied by query volume.
  • Document processing that extracts structured data from unstructured inputs — invoices, contracts, intake forms. Reduces manual effort and error rates simultaneously.
  • Customer-facing support tools that handle high-volume, low-complexity inquiries. Works best when there is a clear escalation path for anything the AI cannot confidently resolve.
  • Content drafting workflows that give teams a starting point rather than a finished product. The human edits; the AI handles the blank page.

What Rarely Works

Broad implementations with vague goals. "We want to use AI to improve our operations" is not a project — it is a wish. Without a specific problem, a measurable outcome, and a defined scope, these initiatives tend to produce demos that never reach production.

Also worth treating with skepticism: AI as a replacement for fixing a broken process. Automating a bad workflow produces a faster bad workflow. The process improvement has to come first.

The Right Starting Point

The best question to ask is not "how can we use AI?" It is "where are we spending time on work that follows a predictable pattern?"

Predictability is what AI is good at. If you can describe the inputs, the desired outputs, and the rules that connect them — there is likely an AI application worth exploring.

If the work requires nuanced judgment, relationship context, or creative problem-solving, AI may assist but should not lead.

A Note on Vendors

Almost every software vendor is now describing their product as AI-powered. Many of these claims are marketing. When evaluating tools, ask what specifically the AI does, what data it was trained on, and what happens when it is wrong. The answers will tell you a great deal about whether the capability is real.