The expectation from customers is there; 53 % of customers expect AI to deliver improved speed and efficiency. The question is whether the infrastructure behind the contact center can support that. But too often, it can ' t.
Data siloed across disconnected systems means AI is working with an incomplete picture. Workflows that still require humans to manually pass information create bottlenecks automation can ' t fix.
The result? AI that moves faster toward the wrong answer.
Predictive AI, connected systems, and automated workflows are converging to identify problems, diagnose root causes, and trigger corrective actions autonomously.
A telecom provider, for example, can detect network degradation in real time, identify the affected customer segment, and initiate remediation workflows before call volume ever spikes.
With such autonomous resolution, customers may never need to reach out at all. Organizations with the right data foundation and workflow infrastructure are already running with this today.
The contact center’ s orientation shifts as a result. Instead of firefighting customer issues, teams are supervising and refining automated resolution loops. Instead of triaging incoming volume, agents are handling those cases that genuinely require human presence.
AUTOMATION MATURITY EMERGES AS A STRATEGIC ADVANTAGE
The organizations that are winning customers’ loyalty( and sales) are not the ones with the biggest AI budgets. They are the ones that start with the CX they want to enable.
Such companies anticipate these key questions from customers:
• What products and services do you sell?
• What do we get based on the products and services we bought?
• What requests can we make?
Successful companies design their solutions around those answers, then build the workflows to capture and fulfill every request. That outside-in clarity is what makes everything else work.
Success requires four things working together:
1. Targeted use cases with clearly defined outcomes.
2. Trusted unified data that AI can actually act on.
THE COMPANIES PULLING AHEAD HAVE... STARTED MEASURING WHETHER CUSTOMERS ACTUALLY GOT WHAT THEY NEEDED.
3. Disciplined governance that keeps automation improving rather than drifting.
CRM
4. Closed-loop workflows that continuously learn from every interaction.
None of those are technology purchases. They are organizational capabilities that have to be built deliberately.
Leaders also need to change what they measure. Activity metrics, calls handled, cases logged, and average handle time( AHT) were designed for a model where human throughput was the primary lever. But they do not capture what matters in an AI-enabled contact center.
Instead, the metrics that reflect real progress are resolution time, first contact resolution( FCR) rates, proactive issue identification, and customer satisfaction, e. g., CSAT. Organizations that continue measuring activity while deploying AI will keep optimizing for the wrong things.
The companies pulling ahead have stopped measuring how many contacts they deflect and started measuring whether customers actually got what they needed.
Those that build this maturity unlock faster resolution, higher satisfaction, and real cost efficiencies that compound over time. Those that do not will find themselves struggling to scale automation safely, with AI that surfaces problems faster than the organization can address them.
Michael Ramsey is the Group Vice President of Product Management, CRM and Industry workflows at ServiceNow, which enable organizations to create seamless customer experiences and drive fierce customer loyalty. In this role, he is responsible for strategy and execution throughout the product lifecycle, including managing strategic partnerships, investments, and M & As.
30 CONTACT CENTER PIPELINE