Artificial intelligence is redefining contact center operations far beyond the traditional service ticket, opening opportunities for customer support to redefine their value proposition to the organization and customers.
By 2027, half of organizations aiming to drastically cut their customer service headcount will reverse course, according to a June report from IT research firm Gartner.
In the study of 163 service leaders, 95% said they plan to retain human agents to help define AI’s role strategically– underscoring a “digital first, but not digital only” approach.
Meanwhile, emerging technologies, including agentic AI, are helping contact centers move beyond simple issue resolution and into areas that create organizational and customer value.
With AI-driven capabilities like predictive routing, sentiment analysis, and SLA-aware escalation models, companies are now building hyper-efficient workflows that promise measurable gains in both agent productivity and customer satisfaction.
John Quaglietta, vice president analyst at Gartner, says AI changes the game for ticket classification and improving routing accuracy by employing natural language processing and machine learning capabilities to understand customer intent, sentiment and urgency.
“This provides the ability to auto-tag tickets with issue type and assign priority without manual intervention,” he says.
By automating the tagging and routing process, companies can reduce misrouted tickets, increase first-contact resolution, and shorten mean time to resolution.
Quaglietta explains these improvements translate directly into better customer satisfaction and overall reductions in cost to serve.
Data and Automation Success
Talkdesk senior director of product marketing Kevin McNulty emphasizes the foundational importance of data for automation success.
“To build reliable AI systems for workflow automation in contact centers, two data sources are especially critical: unstructured interaction data and a dynamic, always-current knowledge base,” McNulty says. “This data holds rich context about customer intent, agent behavior, and resolution paths.”
In addition to data, training methods must support long-term adaptability.
“Model training approaches must include supervised learning on historical workflows, combined with reinforcement learning to optimize over time,” McNulty explains. “Incorporating human-in-the-loop feedback ensures the system improves safely and stays aligned with business goals.”
One of the most transformative applications of AI in contact centers is real-time sentiment and emotion detection.
“Understanding customer sentiment in real-time provides the organization the ability to tailor the customer’s path to resolution,” Quaglietta says.
This includes routing negative sentiment to human agents, managers or escalation teams based on the intensity of the sentiment.
McNulty adds that the next level of emotion analysis goes far beyond binary sentiment.
“Real customer conversations are nuanced and reducing them to a binary label strips away important emotional context,” he says.
He points to tools that can detect specific emotions—frustration, confusion, or urgency—and enable dynamic routing, escalation, or tone adjustment in real time.
These capabilities don’t just prevent escalations—they actively improve customer satisfaction.
“Over time, this leads to more empathetic service, faster resolution, and a measurable lift in customer satisfaction scores,” McNulty says.
That same proactive intelligence underpins AI-powered SLA escalation models, which allow organizations to predict SLA breaches and bottlenecks in real-time and adjust escalation paths dynamically.
“The framework should contain automated triggers and tiered responses based on SLAs,” he says, noting this reduces resolution time while ensuring compliance across complex environments.
Enter Agentic AI
McNulty also points to a new paradigm: agentic AI.
“Rather than waiting for SLA breaches, AI agents proactively manage cases against SLA targets in real time,” he says.
An SLA monitoring agent can track every case and inject urgency into workflows before violations occur.
A routing agent can then reprioritize assignments based on SLA status and agent availability, while a knowledge agent accelerates resolution by surfacing content tailored to the issue—and the deadline.
Because the SLA context is embedded in every AI decision, agents don’t just react to breaches—they anticipate them, turning escalation into a self-adjusting, SLA-driven system.
As AI takes on more tactical tasks, the human role in contact centers is also evolving.
“AI is not just a technological transformation; it is a human one,” Quaglietta says. “Support leaders should use AI to reimagine the role support plays in the organization and what new skills, roles and responsibilities emerge or are needed.”
McNulty echoes this, urging leaders to frame AI as augmentation, not replacement.
“Frame AI as a teammate—one that handles repetitive tasks so agents can focus on more meaningful, complex, and empathetic interactions,” he says. “When agents feel valued, empowered, and part of the transformation, they don’t resist the future of work—they help build it.”
Investing in AI Literacy
To sustain that engagement, McNulty recommends proactive strategies, for example, investing in AI literacy and involving agents early in the process.
“Preserve and even expand autonomy and create new growth paths,” he says.
From his perspective, these are essential not only for performance, but also for morale.
Ultimately, the benefits of AI in workflow automation go beyond efficiency. They enable smarter, faster, more humane service interactions—at scale.
“AI will both automate interactions and workflows and augment staff in this pursuit,” Quaglietta says. “This is a critical message that leaders need to craft and deliver to their teams to keep them engaged and prepare them for a new future.”