Enterprise software procurement has traditionally followed a fairly standard pattern: a business unit identifies a need, IT evaluates candidate tools for technical fit and security, and a decision gets made with IT as the primary technical gatekeeper. Enterprise AI procurement is increasingly departing from that pattern, involving a broader cross-functional committee from much earlier in the process.
Why the process has broadened
AI tools raise categories of risk that traditional software procurement processes weren't necessarily built to evaluate well: data privacy exposure if sensitive information is processed by a third-party model, questions about intellectual property and output ownership, and reputational risk if an AI system produces a visibly wrong or biased output in a customer-facing context. These risks span legal, security, and business leadership in ways that a narrower IT-led evaluation process often wasn't equipped to fully assess.
What the broader committee typically evaluates
A more mature enterprise AI procurement process now commonly includes legal review of data handling and liability terms, security assessment of how the vendor protects and uses submitted data, and business unit sign-off on the specific use case and acceptable error tolerance, in addition to the traditional IT evaluation of technical integration and reliability. This is a meaningfully slower process than traditional software procurement, which has become a point of tension in organizations under pressure to move quickly on AI adoption.
The tradeoff this creates
Organizations moving fastest on AI adoption are sometimes the ones that have compressed or skipped parts of this broader review, accepting more risk in exchange for speed. Organizations moving more cautiously are generally doing more complete cross-functional review before deployment, accepting a slower pace in exchange for lower risk exposure. Neither approach is universally correct; the right balance depends on the specific risk profile of the use case being deployed.
What this means for vendors
AI vendors selling into the enterprise increasingly need to satisfy a wider set of stakeholders than a traditional software sale required, and vendors that have built clear answers to legal and security questions into their sales process, rather than treating them as an afterthought, are seeing that translate into shorter procurement cycles.