For two decades, customer relationship management (CRM) was mainly about data: capturing it, consolidating it, and forming a holistic view of a customer with it. That’s now table stakes, as every CRM system today offers some version of a unified customer view.
The place where these systems differentiated has moved downstream to what happens after the system flags something. The ripple effects influence customer satisfaction.
Across industries, and despite huge sums spent on customer experience (CX) and front-office tech, customer satisfaction scores have barely moved in more than 10 years.
According to the American Customer Satisfaction Index (ACSI), organizations have seen “no detectable returns” from their CX investments. “If anything,” the ACSI wrote, “the returns have been negative. Customer complaints have now reached record levels, surging by 16% in the first quarter” of 2026.
What’s finally making a difference? Autonomous CRM that acts on your behalf across sales and service.
“AI is changing the way everyone thinks about customer relationships, and CRM has to evolve,” says Liz Miller, vice president and principal analyst at Constellation Research.
Things are already changing. Gartner predicts that “by 2028, organizations that leverage multi-agent AI for 80% of customer-facing business processes will dominate.” Those that don’t “risk losing competitive advantage as customer expectations for low effort, rapid service become the norm.”1
Legacy CRM systems were built to store and surface records, not to act on them. Because these systems lack native workflow automation, service reps must toggle between different systems and apps to resolve customer issues.
On top of that, decades of technical debt have rendered legacy platforms overly complex and cumbersome for sales and service teams to navigate. Rather than empowering employees, traditional CRM systems trap valuable context across silos while drowning users in administrative busywork.
“There’s no more immediate need for reinvention than legacy CRM,” said ServiceNow CEO Bill McDermott on the company’s Q1 2026 earnings call. “It’s a little ironic that a category promising a 360-degree view of the customer has left most enterprises spinning around in circles.”
With no native workflows and too much complexity, AI just can’t work well in a legacy CRM environment.
That has serious consequences. A ServiceNow CX study found that only 45% of a service rep’s time is spent tending to customer issues. They’re otherwise focused on administrative tasks, summarizing calls, and chasing other teams for information.
Storing and unifying customer data is the relatively easy part. The harder, less glamorous step is building an execution layer underneath AI that connects systems, governs AI agents, and redesigns CRM workflows around action, rather than simply looking up information.
That’s the basis of autonomous CRM. Legacy CRM tracks customer activities, whereas autonomous CRM can take action to close deals, fulfill orders, and resolve cases. That’s possible only when AI, data, and workflows live on the same platform.
For example, when an AI agent flags a customer churn risk, it can also trigger a retention plan, assign the account team, and update the forecast, without a swivel-chair handoff at every step.
Or consider a common issue such as a billing error. With legacy CRM, that ticket lands in a queue. A rep opens it and toggles between the billing system to check the invoice, the enterprise resource planning (ERP) system to verify the payment history, and a separate support tool to see if the customer has complained before. If the fix requires issuing a credit, that's another system, another approval chain, and another wait for the customer.
With autonomous CRM, the same ticket triggers a workflow that already has access to billing, ERP, and case history because those systems are connected. Within that workflow, AI agents pull the invoice, check whether the discrepancy happened before, and draft a credit for approval, with the workflow enforcing the guardrails that a billing case requires. The rep's first view of the case is a documented recommendation waiting for a signature.
With autonomous CRM, the same ticket triggers a workflow that already has access to billing, ERP, and case history because those systems are connected. Within that workflow, AI agents pull the invoice, check whether the discrepancy happened before, and draft a credit for approval, with the workflow enforcing the guardrails that a billing case requires. The rep's first view of the case is a documented recommendation waiting for a signature.
The difference between the two isn't that the autonomous version has better AI reading the ticket. It's that AI and workflows were never separated in the first place, so the system had rules to check against instead of a ticket to interpret alone.
That interconnection has meaningful business impact. For example, Everpure (formerly Pure Storage) replaced its CRM system with ServiceNow Autonomous CRM, using Customer Service Management, which connects case data, account history, and AI agents on one platform.
First response and case close times are now more than four and seven times faster, respectively. But here’s what matters more: 72% of Everpure’s customers’ problems get identified before they even know something went wrong.
That’s a new way of operating. The system flags an issue rather than having a rep waiting for an angry customer alert about it.
Most organizations are still bolting AI onto systems that leave the manual work behind: An AI agent flags a case and then passes it to a human to chase across four disconnected systems. That isn't a fix; it's the same old handoff with faster steps.
Customer satisfaction won't move at the aggregate level until that process is streamlined industrywide. That’s what the technology was intended for in the first place.
“The point of AI isn’t more AI,” Miller says. “And the point of CRM was never meant to be more records. The point of AI is how to get to precise decisions faster so customers can make a great decision.”
Lenovo's story is similar to Everpure’s, but on a different scale. Lenovo’s Solutions and Services Group runs device as a service (a subscription model bundling hardware, support, and refreshes) for more than 400 global enterprise customers. Previously, that meant tracking orders and support cases across systems that didn't talk to each other.
After moving to ServiceNow Technology Provider Service Management, connected through Workflow Data Fabric, Lenovo cut onboarding time by 40%. The company expects to resolve up to 60% of support incidents before a ticket reaches a human. Its Net Promoter Score is up 25% and churn is down 20%.
Everpure and Lenovo are case studies in what happens when customer records and workflows live on the same platform. Problems are caught before customers notice, onboarding time diminishes, and customer satisfaction scores increase.
"The world's leading companies are moving from AI chaos to control,” McDermott announced at Knowledge 2026.
For CRM, that control comes down to a simple but crucial shift from systems that report on what’s happening to systems that act on what’s happening.
Find out how ServiceNow can help you rethink the possibilities of CRM.
1 Gartner Press Release, Gartner Unveils Top Predictions for IT Organizations and Users in 2026 and Beyond, Oct. 21, 2025
GARTNER is a trademark of Gartner, Inc. and/or its affiliates.
