AI for Businesses: Where It Actually Helps (and Where It Doesn't)

Every business is being told they need 'AI' right now, but very few of the pitches explain what problem it's actually solving. Before adding AI to a product, it's worth being specific about what task is currently manual, slow, or error-prone — because that's the only place AI reliably adds value.

The strongest use cases we've seen are narrow and well-defined: automatically categorizing support tickets, summarizing long documents, extracting structured data from unstructured text, or answering FAQ-style customer questions from a known knowledge base. These work well because the input and expected output are both fairly constrained.

The weakest use cases are the opposite — open-ended chatbots with no clear scope, AI features added because a competitor has one, or automation applied to a process nobody has actually measured or documented yet. If you don't know how long a task takes a human today, you can't tell whether AI is actually saving time.

There's also a reliability question. AI models can be wrong confidently, so any AI feature that makes decisions (approving refunds, filtering applications, generating content that goes out under your brand) needs a human review step somewhere, at least until you've built enough confidence in its accuracy for your specific use case.

Our approach is to treat AI as a tool scoped to a specific, measurable problem — not a feature we add because it's trendy. If a workflow is manual and repetitive today, we'll tell you honestly whether AI is a good fit for it, and what accuracy tradeoffs come with it, before writing any code.