Where Are Contact Centres REALLY Seeing AI Success?

Contact centres are experiencing mixed results with AI deployments right now. Whilst some are undoubtedly seeing measurable improvements in efficiency, customer satisfaction, and operational resilience – others are struggling to see any ROI at all.

To explore the reasons behind this unsettling trend, we asked our panel of technology experts exactly where leading contact centres are applying AI in their operations to get the best return on their investment – so you can adapt your strategy accordingly and start seeing the results you deserve.

Opening the Door for Faster Onboarding of Staff  

A large UK Local Authority customer services team introduced AI-powered agent assist to provide rapid access to knowledge, process and applications integrated to back-end systems. 

This helped to optimize handling times and reduce agent effort.  This also opened the door for faster onboarding of staff.  

Importantly, customer satisfaction scores improved as conversations became more natural and less fragmented by putting callers on hold.

The key to success was tight, secure integration with existing data sources and a focus on driving more efficient, higher-quality interactions.

Contributed by: Lewis Gallagher, Senior Solutions Consultant, Netcall

Improving Accessibility Across More Than 150 Supported Languages

Real-time, AI-powered translation is proving to be a big win, especially for public sector and global service organizations.

For example, in one case, a UK council using 8×8 improved accessibility across more than 150 supported languages using AI-driven multi-language support.

With great translation in place, contact centre agents can communicate instantly with citizens without needing to transfer or delay service. Not only does this improve first-contact resolution rates, but it can also build trust within local communities by providing faster, more respectful service.

Beyond inclusivity, real-time translation also plays a critical role in emergencies, ensuring vital information can be shared without delay or misunderstanding. It’s one of the best examples of AI delivering clear social and operational impact.

Contributed by: Lisa Orford, VP for Contact Centre, 8×8

Uncovering Product Flaws That Are Unsettling Customers

Sometimes the most rewarding wins are quite unanticipated. That’s what happened for one of our customers who implemented AI Insights Voice of the Customer (VoC).

They expected, and hoped for, valuable insights into their clients’ engagements with their organization… This included product impact, customer preferences, staff training gaps and more.

AI also uncovered hard data supporting trends the company was previously only able to guess at, along with the ability to learn many of the reasons behind them.

The big surprise was the discovery that a whole category of interactions, which had appeared to be enquiries, were in fact uncovering a product flaw that was unsettling customers.

Something they weren’t aware of, which was totally missed in wrap-up data and evaluation sampling. However, once they had the volume of data available, AI was able to highlight the distinct patterns that showed this up and help them address it. 

Contributed by: Colin Edgar, Sales Manager, Enghouse Interactive

Reducing Queues, Shortening Time to Resolution, and Improving Consistency for Customers

Contact centres are seeing AI wins where autonomous AI agents take on high-volume, repetitive interactions end-to-end, freeing teams to focus on more complex or sensitive cases.

The strongest results typically come from applying AI to a clearly defined customer journey – such as balance and payment queries, account updates, status checks, or straightforward troubleshooting – where intent is predictable and outcomes are measurable. 

When implemented with the right guardrails and continuous optimization, AI agents can reduce queues, shorten time to resolution, and improve consistency for customers.

In one example from a debt resolution contact centre in our client base, after an AI agent joined the team, productivity increased by 30% and resolution rate improved by 12%. The key is to start with the highest-frequency drivers, measure impact, and iterate based on real contact patterns.

Contributed by: Ben Booth, CEO, MaxContact

Deploying Self-Service Options Built on Evidence – Not Guesswork

The contact centres seeing the most significant returns on AI are those treating it as a journey, not a switch.

While the ultimate goal is often Conversational AI, and the business case it unlocks, the most successful deployments are built on a foundation of manageable steps.

Success begins with tools like interaction analytics and auto-summarization. Before deploying a bot to handle customer queries, businesses must understand why customers are calling.

Interaction analytics uncovers these demand drivers, providing the accurate data needed to inform the effective deployment of Conversational AI, whilst auto-summarization provides a risk-free way to develop the business case for these early phases.

This approach also allows the organization to navigate any potential cultural resistance gradually. By using these tools to learn their environment first, leaders ensure that when they do deploy self-service options for customers, they are built on evidence, not guesswork.

Contributed by: Luke Cuthbertson, Head of CX Consulting Practice, Route 101

Managing Higher Volumes – Without Seasonal Hiring or Increasing Permanent Headcount

To keep pace with holiday volume spikes without losing its personalized, human touch, Lovepop integrated “Joy”, an omnichannel agentic AI assistant.

Joy lowered email response times by 99.93%, reducing from 7 hours to just 18 seconds while preserving high-quality interactions.

This immediacy allowed customers to resolve urgent enquiries like refund requests instantly, significantly boosting their public reputation; the company’s Trustpilot rating improved from 3.6 (Average) to 4.4 (Excellent) within weeks of deployment, and they received a perfect 100% CSAT for AI-powered interactions and an overall CSAT of 94%.

Operationally, AI enabled Lovepop to manage double the ticket volume without seasonal hiring or increasing permanent headcount.

By automating routine logistics like returns and order tracking, Joy allows human agents to focus on complex and more nuanced interactions, delivering scalability and speed without compromising the personal touch essential to their brand.

Contributed by: Matt Price, CEO, Crescendo

Identifying Vulnerable Customers and Assisting With Meeting Regulatory Obligations (Like Consumer Duty)

To date we’ve seen successful adoption and implementation of AI in contact centres spanning every major vertical market.

• Banks are using AI to measure customer satisfaction.
• Insurers are using AI to guide their agents through complex interactions.
• Utilities companies are leveraging AI to understand friction points in their customer processes and streamline the customer journey.

In addition, Capacity customer AAA was able to deflect 30 million calls to virtual agents, which resulted in a 66% savings rate per call while also raising their CSAT score to 4.5/5. The deployment of virtual agents has decreased hold times, even during spikes caused by inclement weather. 

Contributed by: Creovai by Capacity

Predicting When and Where Human Capacity Is Truly Needed

The biggest successes are coming where AI is tightly orchestrated with workforce management. As digital and AI agents handle simple, repeatable interactions, demand patterns are becoming more volatile and harder to predict.

Volumes shift between channels, conversations are more complex, and human advisors are dealing with higher value, emotionally charged work. That requires far more precise forecasting and agile intraday planning than legacy approaches can deliver.

Modern WFM sits at the centre of this change. Advanced forecasting models can incorporate AI usage, new self-service flows, and shifting customer behaviours to predict when and where human capacity is truly needed.

Real-time staffing optimization then ensures the right mix of skills, schedules, and channels to complement AI agents.

When AI and WFM are designed together, contact centres achieve the real prize: a balanced outcome where colleagues feel supported, customers experience faster and more empathetic service, and the business delivers sustainable efficiency and growth.

Contributed by: Nanouk Harper, Solution Analyst, Aspect Software

Making Sure AI Can Learn Across Systems – Not in Silos

Contact centres seeing real AI ROI are not stitching together point solutions. They are consolidating onto a unified AI platform that brings voice, chat, QA, insights, and automation into a single intelligence layer.

When data lives in silos, AI cannot learn across systems. When it lives in one platform, every interaction improves the next. That is where ROI compounds.

Leading teams are using a unified stack to identify automation opportunities from historical conversations, deploy virtual agents against real workflows, and evaluate performance using the same quality standards applied to human agents. Insights do not sit in dashboards.

They directly inform automation, coaching, and policy decisions. This creates a closed learning loop instead of isolated experiments.

There is also a clear architectural shift. Contact centres do not need general purpose “god models”. They need domain-specific AI tuned for CX tasks, with tight latency control and full observability.

Owning the infrastructure stack enables cost efficiency, predictable performance, and faster iteration. That is where operational resilience comes from. We have explained why owning the infrastructure is important in this blog.

Contributed by: Ashish Nagar, CEO, Level AI

Using Insights to Improve Routing, Coaching, and Process Design – Not Just Reporting

The biggest AI successes we’re seeing in contact centres come from solving very specific, high-friction problems rather than trying to “AI everything”.

For example, AI makes it possible to analyse huge volumes of qualitative and quantitative data – sentiment, intent, repeat drivers, escalation patterns – that would be impossible to review manually. Leaders are using these insights to improve routing, coaching, and process design, not just reporting.

Crucially, the best outcomes come with checks and balances. AI works best as an assistive layer, with human validation and clear guardrails. The real win is matching the right AI capability to the right problem – not replacing people but augmenting them where it matters most.

Contributed by: Tara Aldridge, Head of Product Enablement, Vonage

Achieving Greater Workforce Efficiency Across Both Front-Office Sales and Back-Office Underwriting Teams

With our financial services customer Together, Content Guru was awarded “AI Project of the Year” at the Digital Technology Leaders Awards 2025 and “Best Use of AI & Automation” at the CCA Global Excellence Awards.

Building on their long-term partnership, Together has transformed how customer information is captured and processed.

What was previously a manual, time-consuming loan approvals process is now significantly automated through real-time AI-generated summaries, delivering a 50% reduction in Average Handling Time (AHT) alongside improved data quality, and achieving greater workforce efficiency across both front-office sales and back-office underwriting teams.

Contributed by: Martin Taylor, Co-Founder and Deputy CEO, Content Guru

Supporting Agents in Live Conversations to Cut AHT by 60 Seconds

Contact centres are seeing the biggest AI successes when they take a phased, practical approach; starting small, proving value fast, and scaling with confidence. 

Most organizations start simple: AI that lifts agent performance in the moment. Real-time knowledge surfacing, automated summaries, intelligent suggestions; small changes that make every conversation faster, more accurate, and more human.

No disruption. No heavy rebuilds. Just instant wins that improve CSAT and reduce operational drag.

From there, success compounds. Organizations implementing this way are already seeing the benefits. For example, when AI is used to support agents in live conversations, an average call handle time can be cut by 60 seconds. 

With these foundations in place, organizations feel more confident expanding into advanced agentic AI and exploring fully autonomous use cases. In short: start with assistive AI, scale into automation, and grow into agentic intelligence.

Contributed by: Jurgen Hekkink, Head of Product Marketing, AnywhereNow

Uncovering Insights That Directly Improve Staffing, Coaching, and Performance

Metrigy reports that 85.6% of organizations now deploy AI alongside human agents, and that balance is key.

As agentic AI matures, chatbots and digital assistants are increasingly trusted to handle repetitive, high-volume enquiries, while routing more complex interactions to skilled agents where empathy and judgement matter most.

Behind the scenes, AI is also helping organizations better understand the why behind customer and agent interactions.

With around 50% of contact centres now using interaction analytics, which is the biggest area of interest for future AI investment, leaders are analysing conversations across both human and AI agents to uncover insights that directly improve staffing, coaching, and performance.

Supervisors are also seeing gains through AI-powered forecasting, scheduling, and capacity planning, driving smarter operations and continuous improvement across the contact centre.

Contributed by: Magnus Geverts, VP of Product Marketing, Calabrio

Predicting CSAT, NPS, and Effort Scores Directly From Customer Interactions

AI-inferred CX metrics are gaining traction as organizations move away from low-response, delayed customer surveys.

By analysing conversation language, sentiment, effort signals, and interaction patterns, AI can predict CSAT, NPS, and effort scores directly from customer interactions.

Research shows strong correlation with traditional survey results while providing broader coverage and near-real-time visibility. This allows CX leaders to identify dissatisfaction early, segment experience by agent or call reason, and act before issues escalate.

The key advantage is completeness: instead of relying on feedback from a small percentage of customers, organizations gain insight into the full customer population.

Successful teams use AI-inferred CX metrics to complement surveys initially and increasingly rely on them for faster, more actionable CX management.

Contributed by: Tatiana Polyakova, COO, MiaRec

Building a Roadmap That Preserves “High-Touch” Human Engagement

According to Metrigy, 85% of consumers still prefer human interaction over virtual agents. Businesses are currently adopting automation faster than customers are becoming comfortable with it – often at the direct cost of CSAT.

The biggest AI successes aren’t found in replacing people, but in empowering them. Tools like real-time transcription, knowledge assist, and automated post-interaction summaries provide rapid ROI by removing the “tech tax” on agents.

By leveraging conversational analytics to understand high-value interactions, businesses can build a roadmap that preserves “high-touch” human engagement.

This approach doesn’t just improve efficiency; it fosters the empathy and relationship-building that drive long-term loyalty and genuine customer satisfaction.

Contributed by: Matthew Clare, VP, Product Marketing, UJET

Free Planners From Repetitive Forecasting Tasks

Jonathan O’Connor, Resource Planning Manager at a leading UK contact centre, saw immediate results after implementing AI‑enhanced forecasting.

Within five weeks, service levels improved by 32% thanks to machine-learning models that analysed historical traffic across calls, email, chat, and social media, producing highly accurate demand forecasts.

“We no longer spend hours manually adjusting schedules or reallocating staff during peaks. The system gives us confidence that we have the right people in the right place at the right time,” O’Connor explains.

Planners were freed from repetitive forecasting tasks, allowing them to focus on scenario planning and team coaching. At the same time, agents benefited from smarter schedules and better coverage, improving both customer experience and workforce satisfaction.

This case shows how AI-driven forecasting can deliver rapid, measurable improvements while empowering both planners and agents.

Contributed by: Patricia Merchan, Marketing Director, Peopleware

Where Has AI Had the Biggest Impact on Your Operation?

Click here to join our Readers Panel to share your experiences and feature in future Call Centre Helper articles.

For more great insights and advice from our panel of experts, read these articles next:

• New Ways to Boost First Contact Resolution (FCR)
• Ways to Boost Customer Loyalty in a Digital World
• Get the Best Out of Your Hybrid Agents

Author: Megan Jones
Reviewed by: Xander Freeman