Governing AI Inside Enterprise Pricing And Discount Workflows

Eshaan Jain is a Senior Product Manager at Mphasis, focused on governance, security, and AI-driven automation for CPQ and CLM on Salesforce.

​I spent three years building the clause-extraction models behind Amazon’s $40 billion transportation procurement portfolio. They pulled pricing terms and renewal dates from scanned agreements with a level of accuracy the business trusted without a second read. The hard part was never getting the model to read a contract correctly. It was building an approval trail that let a finance leader sign off on the output with confidence.

That lesson carried straight into my next role as Lead Product Owner for Salesforce and Vlocity CPQ at T-Mobile (employed through Mphasis), where the systems in question don’t just read contracts. They set prices and approve discounts.

Once AI moves from reading contracts to influencing prices and approving discounts, governance becomes a business problem rather than a purely technical one. When an AI system can recommend or make a pricing decision, the critical question becomes where to draw the line between what the system can decide on its own and what still requires human approval.​

The Governance Gap Shows Up In The Price​

​Quote-to-cash is where AI governance is first tested because the output has a dollar figure attached.

A pricing engine that recommends a 12% discount, or an agent that routes an exception around a human approver, produces a business decision made without a human in the room signing off on it.

That gap between what these systems can now do and what companies have in place to supervise them is what enterprise AI leaders need to close this year, before it shows up on a customer’s invoice instead of in an internal review.

AI Adoption Is Moving Faster Than AI Oversight​

At the board level, only 31% of directors now say AI is absent from the agenda, down from 45% in the prior edition. Still, 66% describe their board’s AI knowledge as limited to none.