AI for Contact Centres: 6 Opportunities for Better Efficiency and CX

Customer experience (CX) is becoming a more important differentiator as markets remain uncertain and household spending declines. Almost every business now has a dedicated CX budget, and 39% are planning to spend more on these initiatives in 2026. Yet, despite all the progress, there’s one area where gaps often remain: contact centers.

The pressure is rising on both sides of the service equation. According to Calabrio, 40% of contact centers have seen higher demand for 24/7 availability, while 36% say customers now expect more personalization, speed, and transparency. At the same time, 61% of contact center managers report an increase in challenging customer interactions.

To address the higher volume of calls, ongoing staff shortages, and rising cost-to-serve, many businesses are turning to contact center AI. About 98% of enterprise contact centers already use AI in some capacity, but only 12% say they have fully captured the technology’s value. 

Customer acceptance remains a constraint. 1 in 2 are concerned that AI in contact centers will leave them without a human to connect to, and only 29% trust organizations to use AI responsibly.

This is why AI use case selection matters. AI can improve contact center efficiency and customer experience when it is connected to the right workflows, grounded in reliable data, and integrated with the systems agents already use — and that’s what we’re looking at in this post. 

How Contact Center AI Improves Efficiency

Long waiting times and slow issue resolution are two common issues at contact centers. Contact center AI solutions improve efficiency by reducing avoidable demand and giving human agents more operational context at the point of service. The strongest gains come when AI is applied to predictable workflows first, then connected into CRM, knowledge management, routing, and quality assurance systems rather than deployed as a disconnected automation layer.

With such a setup, contact center AI: 

• Lowers the volume of transactional queries. Across industries, between 50-60% of all customer queries are repetitive (e.g., password lost) or transactional (e.g., getting a refund). Automating them makes sense because they are standardized. AI algorithms can handle predictable queries early in the workflow, which reduces queue pressure and increases time-to-resolution. 

• Reduces handling time and after-call work. Efficiency gains also come from reduced time spent per interaction, both during and after the call. AI copilots can generate real-time summaries of customer issues for agents, provide contextual support during conversations, suggest next actions based on historical cases, or automatically log outcomes into CRM systems. 

• Decreases waiting times. When routine queries are resolved through chatbots or virtual agents, more human capacity becomes available for complex cases. About 45% of contact center leaders expect AI agents to reduce wait times in the next 12 months.

• Enables faster staff onboarding and productivity ramp-up. Agent training has historically been a slow and resource-intensive process. With “AI coaches,” new hires can practice various scenarios before encountering the first customers. Such a simulation-led onboarding has delivered 20-30% reductions in time to proficiency among new staff at several enterprises. 

6 AI Use Cases in Contact Centers 

The strongest contact center AI use cases target specific workflow constraints rather than broad automation goals. 

In particular, most leaders are deploying algorithms for intelligent query routing, real-time agent assist, post-call work automation, and several other use cases that both drive measurable efficiency gains and better customer experience.

1. Contextual Assistance

Arguably, the most effective use case of AI in contact centers is real-time agent assist. Algorithms can tune in during live calls or chats and suggest answers, next-best actions, knowledge articles, and de-escalation prompts, based on the provided knowledge base. 

This reduces the amount of manual search that agents have to do while the customer is on the line. Instead, they receive relevant context — account details, latest corporate policies, product information, payment details — in real-time. So they can process complex cases in less time and with less dependence on supervisor support.

Verizon recently embedded an AI assistant, built with Google Cloud technology and Google’s Gemini models, into its customer service workflows. The system draws on roughly 15,000 internal documents, helping agents respond to customer questions with more accurate and timely guidance during live interactions. 

According to Verizon Consumer Group CEO Sampath Sowmyanarayan, the assistant helped reduce call times and enabled its 28,000-person service team to shift more of each interaction from issue handling toward relevant sales opportunities. Moreover, sales through customer service channels have increased by nearly 40% after the deployment.

The case also shows why agent assistance should be treated as a workflow capability rather than a standalone chatbot. Verizon didn’t use AI to fully deflect customer demand. Instead, the tool empowers agents with the knowledge at the point of need and promotes re-skilling to sales-led conversations.

The idea that contact center AI augments, not replaces, human agents, is also backed by broader research from Stanford and MIT. Customer support agents with access to a generative AI conversational assistant increased productivity by 15% on average, measured by issues resolved per hour. The effect was strongest among less experienced and lower-skilled agents, which suggests that real-time assistance can help standardize parts of expert behaviour across a larger service team.

2. After Call Work (ACW) Automation 

Agents’ job is hardly done when they log off the customer call or finish a chat. There’s a significant chunk of after-call work most have to handle next:

• Create interaction documentation. Produce a summary of the contact reason, conversation details, and delivered resolution.

• CRM data entry. Update a customer record with case information, contact reason, and required follow-ups.

• Disposition coding. Tag the interaction with an appropriate call type or reason code in the ACD system. 

• Follow-up scheduling. If the issue wasn’t solved, create callbacks, escalation tickets, or confirmation emails.

• Surveys. Send a post-call Net Promoter Score (NPS) or customer satisfaction score (CSAT) survey.

These common ACW tasks take agents 11.6 minutes to complete on average, costing the business anywhere between $100 and $150 in salaries and overheads. ACW costs also scale alongside call volumes, putting further pressure on the profit margins. 

AI can reduce the volume of manual work that agents need to do. Once a call is recorded with consent and transcribed, an AI system can generate a case summary, extract key decisions, identify the contact reason, and prepare follow-up notes for review. When connected to CRM or contact center platforms, the same workflow can also support automatic case updates and quality assurance analysis.

AI-based ACW automation typically saves 3 to 5 minutes per interaction when it is configured around the organization’s own brand language, workflows, and customer data. Retrieval-augmented generation (RAG) can further improve reliability by grounding summaries and suggested responses in approved enterprise knowledge, rather than relying on the model’s general training alone. The output still needs governance, but the agent’s role shifts from manual documentation to review and correction.

Edvantis recently implemented such a setup for KPS Labs. Their contact solution, which utilizes Twilio’s recording and transcription APIs, records, transcribes, and generates an AI-based summary of each call. So agents spend less time on note-taking, and supervisors can easily run quality assurance and mine the conversations for insights. 

3. Intelligent Routing

When queries do not reach the right person in time, service quality degrades. Many companies still rely on static IVR menus, broad queue categories, or manual transfers, which often increase waiting time and weaken first-contact resolution. 

Intelligent routing brings algorithms into the workflows, which use customer intent and operational context to decide where the interaction should go.

AI-driven routing engines typically have three components: 

• Natural language processing to identify intent before the customer reaches an agent
• Customer data, such as history, value, product type, or issue category
• Agent skill matching, based on availability, expertise, workload, and routing rules

Effectively, the system predicts which outcome a customer needs each time and routes their request to the best-fit queue, channel, or person. What’s more important — the algorithm learns over time based on feedback, and delivers better recommendations.  

Contoso Bank recently deployed such intent-based routing at its multi-region contact center. The retail bank receives a lot of customer inquiries on everything from opening a new account to reporting a lost card or suspicious account charge. They’ve deployed Microsoft’s Customer Intent Agent to analyze past interactions, generate customer intent categories, and group them into operational clusters such as Account Management and Card Services.

Those intent groups were then mapped to the right user groups. For example, Account Management queries could be routed to Account Management America or Account Management Europe, depending on the customer context and service structure. Within each group, the assignment engine allocated work to representatives based on capacity, presence, and other routing attributes, helping reduce misroutes and improve resolution speed.

Telecom Italia used a similar approach at its Caring Services Enterprise Division, which handles more than 13 million calls each year, including three million back-office inquiries. By using the Genesys platform, the company now distributes queries to agents based on skills, timing, and workload, achieving a 6% productivity gain and a 75% reduction in unresolved customer issues

4. Conversational Analytics 

Thanks to high-performance data querying, modern algorithms can mine unstructured data at scale to provide businesses with better customer knowledge. Algorithms can extract topics, sentiment, intent, brand perception, and recurring issues from calls and chats to reveal service gaps and training needs. 

With NPS and CSAT fatigue rising, many customers will not explain what went wrong after a poor interaction. Only 3 in 10 customers will share directly what went wrong during their interaction. Conversational analytics supplies you with a broader evidence base for coaching and process improvement.

The University of Pittsburgh Medical Center (UPMC) implemented a conversation intelligence platform to coach its staff.  Based on the past conversations and CX rating, the team created a “World Class Call Score” that customer concierges receive daily. The score automatically measures each person’s behaviours, such as appropriate introductions, ownership of the call, effective closing, empathy, appointment scheduling, and references to self-service tools during the call. Effectively, providing each staffer with evidence-based guidance tied to specific interaction patterns.

The platform gave the team a wider view of each concierge’s performance and reduced the need to replay call sections manually. Transcripts made it easier to identify issues inside the conversation, which supported more consistent and timely coaching.

The results extended beyond coaching quality. Within one year, UPMC’s Member Services team increased appointment scheduling numbers by 200% by measuring when concierges should offer scheduling support. The team also came in 9.35% under budget, reduced call volume by 187,470 calls, and cut help desk calls from concierges by 16%.

5. Real-Time Personalization

Data silos across various customer support systems often lead to high fragmentation in customer knowledge. Agents can’t see past interactions, purchase history, or other important data without switching screens. Loss of information occurs during hand-offs between different agents or departments. Naturally, this creates frustration among customers. 

While unifying contact center technology could address a lot of these inefficiencies, implementing these solutions is a resource-intensive and time-consuming process. Contact center AI solutions can be implemented much faster to assist with information look-up and interaction personalization. 

Enel X Way applied this approach as competition increased in the electric vehicle market. The company needed better support tools for customer service representatives, especially for interactions involving installation, shipping status, cancellations, and inventory. 

By deploying Minerva CQ, Enel X Way consolidated five systems of record into a single screen, giving agents a more complete operational view during customer conversations.

The workflow covered the full interaction cycle:

• Before the call: Minerva CQ surfaced customer information so the agent could personalize the interaction without manual lookup.

• During the call: The system provided a “keyboard-less” experience by pulling relevant workflows, customer data, dialogue suggestions, behavioural cues, and knowledge content into the agent’s screen.

• After the call: AI generated an instant summary, along with NPS and CSAT indicators, sentiment progression, call drivers, and key decisions for follow-up or quality review.

In the first 30 days alone, Enel X Way achieved a 13% improvement in first contact resolution and a 44% reduction in average handle time. The company also estimated that agent onboarding time could be reduced by up to 75%. 

Generally, 78% of contact leaders expect AI to drive proactive, predictive, and personalized engagement in the next several years. But for that, the AI layer should be able to access reliable customer context and present it inside the agent’s workflow, rather than operate as another fragmented system. 

6. Fraud Mitigation 

Conversational intelligence, obtained from customer interactions, can do much more than just improve service levels or give new opportunities for personalization and upselling. It can also alert to possible security risks. 

Transcripts can have mentions of the new types of scams or common tactics bad actors use to compromise client accounts. By collecting and analyzing these signals systematically, you can improve fraud detection and customer protection.

Google Pay recently presented CASE (Conversational Agent for Scam Elucidation), an agentic AI framework designed to collect scam feedback from its users. The system uses a conversational agent to interview potential victims and gather detailed scam intelligence, rather than relying only on short reports or transaction signals. The transcripts are then processed by another AI component, which extracts structured information for downstream enforcement workflows. Google reports that, after augmenting its existing Google Pay India features with this new intelligence, the team observed a 21% uplift in scam enforcement.

The important mechanism is the conversion of unstructured feedback into operational risk data. A customer may describe a fake merchant or a social engineering script in natural language. Algorithms can then turn that conversation into structured attributes that fraud teams and automated systems can use for detection, triage, and enforcement.

Conversational AI can also act earlier in the fraud chain by screening incoming calls. NatWest compares voice characteristics from inbound calls against a watchlist of voices associated with fraud. In less than a year, the bank screened 17 million inbound calls; 23,000 led to alerts, and the bank found that around one in every 3,500 calls was a fraud attempt.

The same voice layer also supported faster authentication for genuine customers. Alongside its library of suspicious voices, NatWest built a safe list of known customer voices, reducing reliance on passwords or security questions during calls. The bank estimated the return on investment from the technology at more than 300%. 

Beyond CX initiatives, conversational data can also make a major difference in fraud mitigation. AI can help detect patterns faster, but the value comes when extracted intelligence can feed enforcement, authentication, case management, and human investigation workflows. 

Adopting AI Solutions in Your Contact Center

Contact center AI solutions have already proven to drive major efficiency gains through volume reduction and resolution time compression, not to mention new revenue enablement through deeper customer knowledge and real-time customization. 

However, adoption remains an uphill battle. The majority of contact center leaders get deterred by high adoption costs (33%), poor system integration (30%), and lack of employee trust (32%), alongside potential ethical and regulatory concerns. 

At Edvantis, our goal is to help companies safely move from AI experimentation to controlled implementation. Our AI solutions development team can support you through use case selection and validation, help build regulatory-compliant architectures, even in regulated industries, and ensure proper solution integrations with existing systems in your stack. 

So if you’re currently evaluating contact center AI implementation, reach out to us for a personalized consultation.