AI’s Role in Deal Recommendations and Governance
AI can recommend deals almost instantaneously, but the gap between recommendation and decision is widening. DealHub AI advocates for encoding pricing logic, approval thresholds, and margin rules once and enforcing them on every quote, regardless of whether a representative (rep) or an AI agent generates it.
The Current State:
- Every revenue leader now incorporates AI in some part of the deal cycle, from drafting proposals to suggesting discounts.
- While AI speeds up deals, a recommendation isn’t necessarily a decision. Many organizations lack clear governance for deals recommended by AI, often due to urgency or lengthy approval processes.
- This results in faster deals that may drift off policy, shifting the responsibility to revenue leaders who need to address these gaps.
The Shift to AI Governance:
AI governance is now a revenue question, as pricing, approvals, and customer commitments become affected by AI. Once AI influences what customers are offered, governance shifts from model risk and data handling to commercial decisions about deal terms.
Redesigning Processes for AI Integration:
According to McKinsey, organizations unlock value from AI when they redesign processes around it, not merely add AI onto existing processes lacking proper governance.
Governed Execution:
DealHub AI introduces the concept of governed execution, where pricing logic, approval thresholds, and margin rules are set once and applied consistently to every quote, regardless of its origin (rep or AI agent). This ensures that:
- Reps can still work quickly, receiving recommendations from AI.
- Deals only proceed if they meet policy criteria.
This approach distinguishes between fast AI and trusted AI in the revenue process.
Benefits of Governed Execution:
- Finance gains guarantee that deals adhere to policies.
- Revenue teams trust AI recommendations because they are governed by business logic, not just engineering solutions.