Implementing Contact Center Automation With AI
March 17, 2026
AI is at the forefront of nearly every CX conversation today. For contact center leaders, the challenge isn’t whether to adopt AI; it’s deciding what to do next — and where AI will actually make an impact.
Many organizations have already started experimenting with AI in their contact centers. But simply turning AI on and letting it run loose won’t deliver meaningful business impact. It can also raise new questions around generative AI governance, data security, and responsible AI use.
So how should CX leaders approach contact center automation with AI?
I sat down with our solution consulting team to explore how CX leaders should identify automation opportunities and what the key considerations must be for building a practical business case for AI.
Identifying the opportunity for change
For CX leaders, delivering on the promise of AI means creating real, top-level business outcomes. Reducing customer churn, improving productivity gains, and conducting hyper-personalized conversations that efficiently address customers' needs in the very first interaction.
When considering how and where to use AI for contact center automation, it's easy to start with metrics: average handle time (AHT), after-call work (ACW), etc. While these are great contact center metrics that help gauge the health of a contact center, they often fall short of demonstrating CX's real impact.
Much of today’s generative AI work in CX still centers on specific KPIs.
KPIs can be helpful for establishing a starting point. But on their own, they don’t spark the right strategic conversations.
When the focus stays only on metrics, leaders often get stuck asking questions like:
Is my data ready?
Is it organized?
Do we have the right governance and oversight?
Those questions matter — but waiting for the perfect conditions can slow progress.
The advice is simple: get started.
But getting started doesn’t mean guessing. The most successful organizations begin by understanding what’s actually happening inside their contact center — so they can prioritize AI initiatives based on real customer needs and operational data.
The key areas for implementing AI
Once you've made the decision to implement AI in your contact center, the first question becomes:
Where should you begin?
This requires a strategic, data-driven approach — partly so you can show clear impact before and after adoption. While each contact center will have different priorities, identifying the areas where friction already exists and determining how AI can augment those workflows.
For most CX leaders, the initial use case will fall into one of two categories: self-service or agent assistance.
In part, this is because interactive voice response (IVR) has been in place in many contact centers for quite some time.
For organizations with traditional IVR in place, AI creates an opportunity to move beyond rigid workflows—expanding what’s possible with both self-service and agent guidance.
Self-service
Self-service has long been one of the most accessible starting points for contact center automation, making it a foundational element in many organizations’ AI CX playbook. From AI-powered support chatbots to fully conversational AI agents, these tools can handle routine requests and reduce pressure on live agents.
But organizations shouldn’t limit their thinking to the capabilities available today.
The pace of AI innovation means the tools available even a year from now may look dramatically different from those in place today. Instead of focusing only on specific technologies, CX leaders should start by defining the experience they want to create.
Start with the experience you want to create. What challenges are you solving? Where are customers getting stuck?
From there, leaders can ask more strategic questions:
What kind of insights would you want to increase the revenue or reduce the operational costs of your contact center?
How are you differentiating your business when your customers call in or engage with your self-service options?
What solutions do (or should) you have to help customers proactively solve their problems?
By focusing on the experience first, rather than the tactic, you can intelligently plug AI into the right workflows and where it makes sense for your end goals.
Agent assistance
Customer experience often takes center stage in AI discussions, but AI can also transform the agent experience.
Improving customer experience and improving agent experience go hand in hand. When one improves, the other follows.
AI can help you improve productivity, providing real-time guidance, surfacing relevant customer information, and automating repetitive tasks during interactions.
When agents have all the necessary customer information at their fingertips — without having to spend minutes searching through databases or reading complicated UIs — they feel more engaged and satisfied with each interaction.
In turn, this drives down average handle times, increases your first call resolution rate, and with capabilities like AI-powered summarization, reduces after-call work — all metrics that help show impact.
Just as important, these tools improve the agent experience. When implemented thoughtfully, these kinds of improvements create measurable progress — without disrupting the systems and processes teams rely on every day. When routine tasks are automated and information is easier to access, agents can spend more time on meaningful interactions — improving engagement and reducing burnout.
Bringing it all together
Self-service and agent assistance are common starting points, but they’re only part of the broader contact center automation opportunity.
AI shouldn’t be treated as a standalone tool. Instead, it should be part of a larger CX strategy that connects customer experience, agent experience, and business outcomes.
The challenge many organizations face isn’t whether AI can help — it’s determining which investments will drive the most value first. That’s why many CX leaders start by analyzing real interaction data and operational patterns before deciding which AI capabilities to implement.
The bigger question is how AI fits into your broader CX strategy—across customer experience, agent experience, and business outcomes.
The challenge many organizations face isn’t whether AI can help. It’s determining what to do next — which use cases will create the greatest impact and how to move forward without disrupting what’s already working.
That’s why successful CX leaders approach contact center automation as a strategic journey rather than a single deployment. Early use cases like self-service and agent assistance become the foundation for an evolving AI CX playbook, one that helps organizations expand automation thoughtfully while improving customer and agent experiences.
Turning insight into action
In the webinar, we discuss how CX leaders may rush into automation before fully understanding the operational challenges they’re trying to solve. The most effective AI strategies start with visibility into real customer interactions and operational data.
Knowing where to apply AI is only part of the equation. CX leaders also need a clear framework for evaluating readiness, identifying high-impact opportunities, and building a practical roadmap for adoption.
Approaches like the Five9 AI Blueprint help organizations do exactly that. By analyzing real contact center interactions and operational data, leaders can uncover patterns that traditional metrics alone might miss — revealing where contact center automation can deliver meaningful improvements across self-service, agent productivity, and operational efficiency.
Instead of relying on assumptions or isolated KPIs, this approach surfaces insights directly from customer conversations and agent workflows. Those insights help CX leaders identify which automation opportunities will drive measurable business value today while building toward longer-term transformation.
When AI adoption is grounded in real data — and supported by thoughtful generative AI governance — contact centers can confidently scale automation while maintaining trust, reliability, and service quality.
Ready to determine what to do next with AI?
Understanding where AI will create the greatest impact starts with understanding your data.
