Baifang Warns Enterprises: Don't Rebuild Every Process With AI

If the past two years of corporate AI enthusiasm needed a slogan, “redo everything with AI” would be a strong candidate. Customer service, marketing, R&D, finance, HR and procurement have all been repackaged as AI workflows, with forms that people once filled in now generated, documents once read by staff now scanned by models, and buttons once clicked by hand now triggered by agents.

Baifang, a Chinese company that sells enterprise software for finance and tax work, thinks that framing is the biggest mistake companies make. In a widely shared argument, the firm says the value of AI in the enterprise is not uniform: some steps gain enormously from automation, while others are already efficient and become slower, costlier or harder to audit when a model is inserted.

The company’s example is invoice and expense processing, the kind of back-office work that Chinese firms handle at enormous volume. A model can read a scanned receipt, match it to a purchase order and flag a mismatch far faster than a clerk. But when the same model is asked to redesign an approval chain that already works, the result is often a longer process, more exceptions and a new layer of review that nobody budgeted for.

That distinction matters because Chinese enterprises are under pressure to show returns on AI spending. Baifang’s advice is to start with the tasks that are high-volume, rules-heavy and easy to measure, such as document recognition, data entry and reconciliation, then expand only where a clear cost or error reduction shows up.

The firm also points to a structural problem: many AI projects are sold as platform overhauls. Vendors promise to rebuild the whole stack, which makes procurement slow and integration risky. A narrower approach, adding AI to a handful of steps inside existing systems, is less glamorous but easier to justify and to roll back if it fails.

This is a familiar pattern in enterprise software worldwide. Early cloud projects often failed when companies lifted and shifted every application at once, and succeeded when they moved the workloads that benefited most. Corporate AI appears to be following the same curve, from grand transformation pitches toward targeted tools with measurable outcomes.

For Baifang, the argument is also a positioning play. As a finance and tax software provider, it wants customers to treat AI as a feature inside workflows they already trust rather than as a reason to replace those systems. The company’s message to buyers is to ask what a model actually changes at each step, and to keep the answer narrow enough to test.

What to watch is whether enterprise buyers reward that discipline. If more Chinese firms start publishing AI budgets tied to specific back-office metrics rather than broad transformation programs, the “redo everything” era may end quietly, one invoice at a time.