Where Should You Be Using AI in New CCaaS Implementations?

AI dominates the conversation around Contact Centre as a Service (CCaaS). Vendor demos promise autonomous agents, hyper-personalized journeys, and dramatic cost reduction.

Yet when organizations deploy new CCaaS platforms, research consistently shows the majority of AI functionality goes unused in early deployments.

With the help of several consultants from The Global Association of Independent AI and Contact CX Consultants, we look at why this is happening, where AI is actually being used in CX deployments, and how customers can benefit.

AI Adoption Is Happening More Slowly in Some Areas Than People Realize

It would be fair to say that AI is now embedded across most modern CCaaS platforms. The question is – at what level and to what end?

At the moment, we are seeing usage at a very low level, but this is increasing as we see better ways to use AI and the technology develops to accommodate.

What is misunderstood is the nature of change. AI adoption is happening more slowly in some areas than people realize. Organizations are seeing gains in efficiency, consistency, and insight – rather than radical reinvention and dramatic ROI gains.

“What we are seeing at the moment isn’t an AI revolution in CCaaS – but rather an evolution – as we try to understand the technology ourselves.

Users are hesitant to wholeheartedly jump on the AI bandwagon but typically use it in obvious areas that lend themselves to automation, like translation, transcription, summary, and agent assist.

Early adoption of conversational and agentic AI can be seen but not wholesale usage, and this is where the major ROI will be realized in the future.” – Ian Nevin, GAIA-CC Co Founder.

Transcription, Translation, and Especially Summarization Deliver Clear Value

So, although not sexy, this is where the majority of AI is found in a CCaaS deployment.

Transcription, translation, and especially summarization deliver clear value in contact centres, making them among the most widely adopted AI features.

They reduce AHT, improve employee experience, and drive fast productivity gains and ROI. These capabilities are easier to deploy, carry lower AI risk, and often produce more accurate post-call summaries than rushed manual notes.

Added to this is the ability to label the data. You can transcribe it, assign sentiment to it, assign a topic to it, assign a resolution type to it, and you have much cleaner and more consistent call data when you can apply all of that to it.

That then provides the body of data that you can interrogate (with AI or directly) to understand your operation better. This provides more context with actionable insights.

“In our view, transcription and summarization currently deliver the biggest efficiency gains in CCaaS, reducing handle time and after-call work so agents can focus on the customer.

However, not all solutions are equal, so customers need to be aware that some simply transcribe verbatim, while others provide structured summaries, key actions, and next-best actions.

And the most successful implementations know that it’s an evolving technology so rigorous ongoing testing across real interactions is critical to ensure accuracy and consistency and that important context isn’t lost for future follow-ups.” – Sam Mathie, Soniza Consulting.

Real-Time Assist Reduces Cognitive Load Immediately

Agent-assist capabilities represent one of the clearest success stories for AI in CCaaS. These tools provide real-time support to agents through knowledge suggestions, next-best actions, and automated summaries.

The impact is immediate. Agents spend less time searching for information and more time engaging with customers. Average handling times fall, while consistency improves.

“If you want to see AI working today, sit with an agent using real-time assist. It reduces cognitive load immediately, positively impacting every significant contact centre KPI” – Mark Nichols, Vital CX.

Crucially, these tools keep humans in control. Rather than attempting to replace agents, they enhance performance. This makes adoption easier and outcomes more predictable.

Intelligent Routing Can Significantly Improve First Contact Resolution

One of the most effective applications of AI is also one of the least discussed: intelligent routing. AI enhances traditional routing by analysing intent, sentiment, and historical behaviour to direct interactions more effectively.

Because it builds on existing logic, it requires limited organizational disruption. The results, however, can be significant, particularly in improving First Contact Resolution and reducing transfers.

“While other AI projects can get stuck in months of planning, intelligent routing is a ‘quick win’ that can start fixing the customer experience and lowering costs almost the moment you turn it on.” – Tim Banting, Principal Analyst and Founder, So What?, Now What!

Despite vendor narratives around predictive journeys, most organizations are still refining core routing accuracy. This is where tangible gains are being made today.

Chatbots and Voicebots Remain the Most Overhyped AI Use Case

Chatbots and voicebots remain the most visible AI use case – and the most overhyped. Advances in large language models have improved conversational quality, but limitations remain.

Most organizations deploy bots for simple, high-volume use cases, with predictable and reliable journeys, such as FAQs, order tracking, scheduling appointments or account updates.

Although there are benefits here, such as reduced waiting time, it can easily create frustration amongst customers with complex or emotionally nuanced interactions that still require human agents and the voice element.

While the sophistication of true conversational AI is improving at great pace, fully autonomous resolution remains rare in production environments, but this the key area for the future where AI will have a profound impact.

To find out about contact centres and their relationship with chatbots, read our article: Are Chatbots the Tech We All Love to Hate?

Quality Management and Analytics Are Undergoing a Quiet Transformation

AI is also having an impact behind the scenes, particularly in quality management and analytics. Automated transcription, sentiment analysis, and topic detection are now standard capabilities.

Perhaps most significantly, AI enables 100% interaction monitoring. This replaces traditional sampling approaches and provides a complete view of performance and customer experience.

These capabilities are mature, scalable, and quickly deliver consistent value. Unlike more experimental use cases, they are widely adopted across new implementations.

AI-Driven Personalization Is Heavily Promoted – But Remains Uneven in Execution

Alongside this, AI-driven personalization is heavily promoted but remains uneven in execution. In theory, AI enables highly tailored, real-time customer experiences. In practice, most organizations are still working toward this goal.

Current implementations tend to focus on exploiting capabilities such as CRM integration, customer context retrieval, and segmentation.

“True one-to-one personalization is still aspirational for most organizations. Data fragmentation is the real barrier – not the AI.” – Derek Lewis, Lewis CX Consulting

For advice on how contact centres are using customer preferences, read our article: 12 Amazing Things You Can Now Do With Customer Preferences

Data Quality Remains the Most Significant Implementation Challenge

Despite so many promising use cases, several common challenges still limit the impact of AI in CCaaS implementation, including:

“The difference between using an AI solution and leaving it on the shelf comes down to practical application. Involve your operations teams early, and you’ll get real value.

Exclude them, and the AI will look great on paper but deliver limited value to the teams using it.” – Chris Marron, CMA Intelligence.

The Challenge With Deploying AI in the Contact Centre Is the Rush for the Dream

AI is not failing in contact centres. Expectations are.

GAIA consultants working across live CCaaS deployments are seeing genuine, measurable gains, but they’re concentrated in the unglamorous: transcription, routing accuracy, agent assist, and quality monitoring. These are the use cases that quietly improve performance without making it onto a keynote slide.

The transformational vision of autonomous agents, seamless self-service, and one-to-one personalization at scale is being used in limited pockets; it’s already here, but adoption is slow.

If, then, the future of AI is not here just yet (but coming), what advice do consultants give to contact centres looking at adding AI to their contact centre technology?

With thanks to: The GAIA-CC (The Global Association of Independent AI and CX Consultants), a new global network for independent consultants focusing on CX and AI.

For more information on how contact centres are using technology, read these articles next:

• How to Use AI to Connect the Dots – Not Create More Silos
• Top Tips for Handling Bot-Initiated Calls
• 10 Questions to Ask When Buying Your Next CCaaS Solution

Author: Ian Nevin
Reviewed by: Jo Robinson