3 nonnegotiables for AI-powered revenue operations

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Revenue teams have embraced AI, using it to automate administrative work and accelerate deals. The ambition is there, but achieving AI's promise is more complex.

The challenge is that most AI-based systems are fragmented, fragile, and hard to use. In fact, according to a 2024 LinkedIn and Ipsos report, more than half of revenue leaders feel their current technology stack is too cumbersome and are overwhelmed by the number of AI technologies on the market.

Moving from one-off AI pilots to enabling teams to sell smarter and faster requires a new way of thinking about AI. Here are three nonnegotiables to succeed with AI in revenue operations now and in the years to come.

1. A single source of revenue truth

Today’s enterprise systems are fragmented. Customer relationship management (CRM) systems track your customer data. Pricing history data is held by your configure, price, quote (CPQ) system. Sales fulfillment information is recorded by your order management system. And service desks house your support requests.

These systems operate in silos, and AI can't connect the dots between them. In fact, according to Validity’s 2025 State of CRM Data Management report, 76% of CRM users said less than half of their organization’s CRM data is accurate and complete.

This isn't just frustrating—it's a key barrier to AI success. When AI operates from dirty data, scattered information, and incomplete context, it can’t deliver on its potential.

Most revenue technology stacks evolved through acquisition and integration. Marketing automation was bolted onto CRM. CPQ was added as a layer. Order management was stitched in through APIs. Each new connection introduced latency, data quality issues, and maintenance overhead. This barely worked when humans made most decisions and could tolerate inconsistencies.

AI can't operate effectively in this environment. When most critical business data exists as custom database objects with proprietary code, it’s impossible for AI to automate at scale.

Moving from one-off AI pilots to enabling teams to sell smarter and faster requires a new of thinking about AI.

A unified data architecture breaks down these barriers. It creates a single source of truth for customer data, product catalogs, pricing rules, and revenue metrics, helping ensure the completeness and accuracy that AI needs to work effectively.

A unified architecture means:

One source of truth: A single data model and architecture where customer data, product catalogs, pricing rules, and transaction history live together

Standard data models: A canonical data structure designed with prebuilt, shared CRM objects for leads, opportunities, quotes, orders, contracts, subscriptions, and entitlements so data can be easily transformed, reused, and modeled across AI workflows
Single system of action: A connected, intuitive experience for selling teams, with every interaction automatically captured and analyzed

Bidirectional intelligence: AI updates flow instantly to every stakeholder and system automatically

When your data is complete, connected, and coherent, AI agents can drive action across sales. Revenue operations teams can deploy AI workflows to launch new products, update pricing, and adapt sales motions, without calling IT at every step. Sales reps and account teams see the benefits of AI creating a virtuous cycle of data-driven trust, leading to greater revenue growth.

As an example of AI in action, when a new lead is recorded, it can be tracked by sales, intelligently enriched by marketing automation, and converted to an opportunity. When a sales rep generates a quote, it references the same account, contact, and product requirement information.

When the quote becomes an order, it pulls from those same shared data models, visible to order fulfillment and downstream operations. Once the prospect becomes a customer, the same account record is seamlessly linked to field installation and support cases. This single source of truth is essential to scaling AI across the entire revenue engine.

2. Intelligent workflow automation

Revenue operations run on repeatable processes: lead qualification, opportunity management, sales forecasting, quote approvals, contract routing, and order fulfillment. These workflows cross departmental boundaries and involve multiple decision-makers. When they're managed through email chains and manual handoffs, deals slow down and errors multiply.

Launching new products and services on traditional CRM tools can be a lengthy process. They’re not only hard to stand up and maintain, but they’re also difficult to use.

When most critical business data exists as custom database objects with proprietary code, it's impossible for AI to automate at scale.

Next-gen intelligent revenue solutions give teams fast and proactive insights. They move from merely suggesting an action to taking one. Such systems require three capabilities to live up to their potential:

Deterministic workflows that drive completion: Enterprise-level AI systems require the probabilistic intelligence that AI provides with the deterministic controls of workflows. Revenue teams don’t need more data—they need actions that lead to better outcomes. AI might identify a deal at risk, but the response needs to follow a predictable, governable order: Notify the account owner, trigger the next best action, update forecast probability, and escalate if no further action is taken within 48 hours.

Low-code innovation without developer dependency: Revenue team needs change constantly. New products launch, sales strategies evolve, and approval processes shift based on market conditions. With traditional CRM, every change requires developer resources and testing. Revenue operations teams need a low-code approach so they can quickly and easily build and modify AI workflows.

Built-in governance that enables autonomous execution: AI promises autonomy, but enterprises need control, accountability, and consistency. Reliable AI-powered revenue operations require complete audit trails for every decision. They must also have continuous model governance to ensure AI operates within predefined guardrails.

When equipment manufacturer Keysig h t acquired new businesses or launched new products, updating its product configuration systems took excessive time and resources. ServiceNow’s low-code capabilities and AI-guided workflows helped reduce unnecessary custom coding and allow Keysight to bring innovations to market faster.

With a 50% to 80% reduction in configuration setup and maintenance time, system administrators can now contribute to broader company initiatives and accelerate AI-powered revenue.

3. Real-time visibility from lead to satisfaction

Today, revenue teams must navigate countless disconnected tools across the lead-to-renewal process. This creates productivity-killing context switching and searching that negate the efficiency gains AI should deliver.

The fundamental problem? Traditional CRM vendors have bolted AI onto existing software architectures. Each new AI feature becomes another disconnected tool, another integration to maintain, and another opportunity for data to get out of sync. Deals slip through cracks, quotes take weeks to execute, and order fallouts go unnoticed.

A unified data architecture creates a single source of truth for customer data, product catalogs, pricing rules, and revenue metrics.

Instead of traditional CRM, revenue teams need unified, AI-driven CRM. A system that maintains clear visibility across sales, fulfillment, and service can act based on the right context and within the right workflow.

Here’s how AI-powered, unified revenue orchestration drives value for key industries:

Telecommunications

Challenge: Telecom providers face chronic order fallout due to disconnected quoting, billing, and network provisioning systems. Complex product catalogs require IT involvement for every offer change. Changes break processes, leading to revenue leakage, fulfillment delays, and frustrated customers.
Solution: Increase the speed to market of new products and services by connecting CPQ and order management to downstream provisioning and customer service. Use AI to help reps configure and price complex catalogs with dynamic bundling, promotional pricing rules, and dependency management. Automatically trigger order fulfillment from quotes, and quickly address changes with shared visibility across sales and service.

Manufacturing

Challenge: Complex engineer-to-order and configure-to-order products rely on tribal knowledge, leading to multiday quote cycles, frequent bill of material (BOM) errors that cause production scrap, and disconnected handoffs between sales, engineering, and the factory floor.
Solution: Reduce rework and help eliminate costly errors with AI-guided selling. Empower sales reps to align highly customized products to engineering rules and compliance standards. Quickly generate quotes, instantly sync commercial and manufacturing BOMs, and automatically split up work orders and fulfillment tasks, accelerating time to revenue.

Technology

Challenge: Tech companies struggle to manage hybrid sales motions, manual renewals, and disconnected revenue systems, causing delays in revenue realization and customer onboarding.
Solution: Speed up customer acquisition and maximize revenue with a single self-serve platform for enterprise and channel sales. AI intelligently recommends add-ons and services while handling hybrid pricing models. Customer data automatically flows from sales to service, and vice versa, to drive proactive engagement, renewals, and upsells.

Where to go from here

These three nonnegotiables are your guide to turning AI hype into reality for revenue operations. As you evaluate your revenue operations strategy, ask yourself:

Are my revenue teams operating from a shared source of truth?
Do my systems just show me problems, or do they orchestrate solutions?
Is AI bolted on or embedded into my lead-to-renewal workflows?

Find out how ServiceNow can help you put AI to work for your sales teams.