Before tackling the future of customer engagement head-on, organizations should examine their past to identify what needs to change and why. This is why we’re introducing our CCaaS Crash Course series — our look back at the foundational elements of contact center automation, paired with a glimpse into how today’s omnichannel communication will change in the face of more advanced service capabilities.
We’ll begin with an approach that contact centers use daily: automated call routing. It’s a staple of modern contact centers, critical for efficiently managing large volumes of calls. Traditionally, this meant relying on structured logic and static menus to route inquiries by department, skill, or schedule. While this helped reduce bottlenecks in the past, it’s increasingly strained under the weight of rising customer expectations and complex service conditions.
AI-powered automated call routing reimagines the conventional process, utilizing data and predictive modeling to assess intent, prioritize urgency, and match each inquiry with the most suitable agent or resource, often before either party has spoken with the other. It’s changing the way contact centers operate entirely, lowering the number of transfers and changing public sentiment by replacing expected aggravation with issues resolved quickly and accurately.
In this guide, we’ll walk through how traditional routing works, where it falls short, and how AI is reshaping execution and mentality overall, helping organizations improve service speed, quality, and outcomes.
Automated call routing is the process of directing inbound calls to the right agent, department, or resource using preset rules or real-time inputs. At its core, the goal is to reduce wait times and simplify how contact centers handle volume, especially during peak periods.
The logic behind automated routing can be as simple as assigning calls based on business hours or agent availability. In more advanced setups, it incorporates variables like language preferences, caller ID, or IVR responses to guide customers to the right resource.
While routing can vary in complexity, it’s always designed to do one thing: get each customer to the best possible destination, faster.
Traditional automated call routing follows a rule-based process that unfolds in three distinct phases: qualification, queue assignment, and call distribution. Each step utilizes static logic to handle large volumes of inbound communication with consistency, albeit not with native intelligence.
When a caller reaches the contact center, they’re typically met with an IVR system. This step collects information through touch-tone responses or basic voice input, categorizing the call by identifying its purpose, the preferred language, and/or the department needed.
Once the IVR collects input, the customer is assigned to a queue based on preset conditions, including but not limited to:
These queues are built for efficiency, but they don’t dynamically account for urgency, call history, or agent performance. The queue simply holds the customer until the system finds a match.
After entering a queue, calls are delivered to agents based on routing rules, including:
Each rule serves a purpose, but the routing decisions themselves are static, not contextual. The system doesn’t “know” if an agent is the best match; it only knows that they meet the rule’s requirements. Traditional routing can appear efficient on paper, but often proves inconsistent in practice.
The phrase “If it’s not broken, don’t fix it” may be easy to recall when you’re not on the receiving end often, but consider the following scenario: if a caller presses 2 for billing, they go to the billing queue. If it’s after hours, the call gets forwarded to voicemail. This rigidity creates efficiency on the back end, but it often fails to meet the actual needs of the person reaching out.
One major issue is context blindness. Traditional call center automation doesn’t understand why someone is calling; it only follows inputs. That means a caller who’s angry, on a deadline, or facing a repeat issue gets treated the same as a first-time caller with a 1-minute fix. The system can’t recognize urgency or frustration, and can’t adapt if the caller chooses the “wrong” option in the IVR menu.
Misrouted calls are another common pain point — a customer might be sent to the wrong department, forcing a transfer or restart in their journey entirely. Each additional step increases handling time and contributes to negative sentiment. For agents, it means wasted time and a harder path to resolution.
There’s also a lack of adaptability. When volume spikes or wait times get backed up, traditional routing doesn’t rebalance. It follows the same preset rules — regardless of workload — meaning some agents are overwhelmed while others sit on the sidelines. Supervisors may try to adjust routing logic manually, but these changes take time and don’t always respond quickly enough to help.
The reality? A system that appears efficient from the inside but is broken for the customer on the other end.
The emergence of AI in contact centers is impacting every stage, but initial outreach may be experiencing the biggest shift. Intelligent call routing replaces fixed rules with real-time decision-making; instead of sending every caller through the same IVR tree, AI systems assess each situation as it happens, matching callers to the most qualified resource based on context, not just category. The usual steps of qualification, assignment, and distribution are revitalized through a process that prioritizes best-in-class CX.
Before a customer even reaches an agent, AI systems gather historical data, including CRM history, past support outcomes, language preferences, account status, and even voice tone, to form a snapshot of the customer’s profile.
AI in contact centers weighs that data against current conditions, like which agents are available, their current workloads, previous resolution rates, and who has managed similar issues effectively. The system determines which resource is most likely to resolve the issue on the first try and lines up the perfect staff member accordingly.
While the best-fit agent or virtual assistant is most likely to resolve a customer’s concerns, escalation is always a possibility. If and when it arises, AI can automatically hand off the situation with full context, ensuring the next agent doesn’t have to start from scratch. In some cases, the AI can even resolve the request on its own.
Simply put, a growing number of organizations, from enterprises to government departments, are realizing traditional tools can’t keep up with what customers expect today: faster answers, fewer transfers, and more relevant support from the start.
The old model — press 1 for billing, press 2 for support — wasn’t built for nuance. It doesn’t recognize a caller’s history or understand that an emergency call needs immediate care over a casual inquiry. AI routing steps in to solve for those gaps, using data to make better decisions about who should handle what and when.
This rise can be attributed to the paradox of thinning teams and increased productivity demands. Agents are taking on more responsibility, and leaders are looking for ways to get stronger performance from the team they already have.
The rise of AI in contact centers stems from the need to make every second on the phone count. AI-automated call routing delivers that by making decisions more responsive, more accurate, and less of a burden on already-busy teams.
If you’re determining how artificial intelligence fits into your contact center strategy, call routing is a great place to start — it’s one of the clearest examples of how AI can make a measurable difference.
To help you visualize how it works, we’ve put together a quick-reference guide that outlines the traditional and AI-enabled approaches side by side.
Download our free infographic to see what smarter routing looks like in practice.