SME AI

Enterprise AI products assume a dedicated platform team and an implementation project spanning months. In a 20–100 person company neither assumption holds; setup usually falls to the HR manager or the finance lead.

The second difference is the buying process. The enterprise model involves quote negotiations, minimum seat commitments, and annual renewal talks. At SME scale that process alone can consume more time than the product returns.

The third is the usage profile. SMEs typically have a small number of heavy users, and per-seat licensing inflates cost unnecessarily for that shape.

An SME-focused product is built so that deployment requires no configuration. Ready department templates, a start flow limited to picking a template and attaching documents, and prices visible without a quote are all parts of that approach.

Scope is deliberately narrow too. Rather than dozens of SaaS connectors, deep integration with the few systems actually used in that market delivers more value. Paying for unused breadth is directly visible in an SME budget.

Local context is another point of difference: department templates written against local legislation and ready connectors for local accounting and ERP systems are typically absent from global products.

At SME scale the right evaluation criterion is not a feature list but time to first value. Can the product answer a real question within a week, or does it require an implementation project first?

The second criterion is ownership: who will build it, who will maintain it, and what happens if they leave? Products requiring configuration either never get deployed in this profile or become consultant-dependent.

The third is exit cost. Does your data stay in its own format, and is it easy to move? Content converted into a proprietary format makes switching products expensive.