Generative AI in Contact Centers [+Real-World Wins]

Consumers are increasingly bypassing traditional customer support channels in favor of third-party AI tools like Gemini and ChatGPT 5 to resolve their support issues. This trend signals a growing enterprise challenge for contact centers relying solely on legacy methods of support delivery.

Generative AI offers a way forward. By equipping agents with real-time, AI-driven guidance, enterprises can reclaim CX leadership and deliver the fast, personalized support customers now expect.

94% of business leaders already use regular AI to assist agents live during customer interactions, handling mundane tasks that slow down agents. Adoption of generative AI in contact centers will be a win-win for everyone involved. Customers receive faster, personalized resolutions. Agents work even more efficiently. Brands win customer loyalty and goodwill.

In the sections that follow, you’ll explore how using generative AI in contact centers can help you scale support, boost agent effectiveness, and enhance CX through practical use cases, adoption frameworks, and real-world success stories.

🔖 Fundamental Read: Generative AI in Customer Service: 6 Use Cases & 10 Tips

What does generative AI mean for contact centers?

Generative AI in contact centers means using advanced machine learning, natural language processing, and large language models to automate and improve contact center tasks.

GenAI drafts responses, summarizes calls, and even handles basic customer interactions, allowing service teams to work faster and smarter. Unlike scripted bots, it understands context, learns from patterns and generates outputs that feel human.

This technology streamlines CX operations and takes on repetitive work that once drained agent time and energy.

Will GenAI replace agents?

Generative AI augments rather than replaces agents. It removes repetitive tasks from the agent’s queue, freeing them to focus on complex issues and customer relationships. With genAI as a trusty sidekick, agents transform from task executors to problem solvers.

For example, in high-volume environments, GenAI can clear backlogs, pre-fill customer details, and prepare suggested responses before an agent even picks up the line. This means agents start each interaction with context at their fingertips, which enables them to solve problems faster and more empathetically.

Yet, many agents continue fearing loss of their jobs due to GenAI. But a Gartner survey of 822 business executives revealed that customer service and support leaders expect headcount reductions of only 5% or less due to generative AI.

Emily Potosky, Senior Director of Research in the Gartner Customer Service and Support Practice, points out that some level of human-assisted service will always be necessary.

Take Klarna's example. The company replaced 700 employees with generative AI, believing that automation could handle everything. However, Klarna soon had to rehire staff after recognizing the limits of tech alone.

The move drew critique from business leaders like Chamath Palihapitiya and stands as a reminder that GenAI aids, not eliminates, the human touch.

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Further, CX Guru Shep Hyken has often talked about the benefit of infusing AI and human touch into customer experiences. In the video below, he discusses his perspective on our Podcast, CX-WISE. Watch it now to get the full picture.

Top 3 benefits of generative AI in contact centers

For enterprise leaders, the following GenAI benefits translate into measurable gains in scalability, service quality, and cost efficiency.

1) Boosting agent productivity

A large-scale study by Stanford and MIT, covering over 5,000 customer support agents, found that using a generative AI conversational assistant boosted productivity by 15% in issues resolved per hour. It drafted responses, surfaced relevant solutions and guided agents with real-time prompts, so they spent less time searching and more time serving.

2) Reducing case duration and attrition rates

DTE Energy, one of the largest energy companies, struggled to meet its service level agreements (SLAs) with traditional contact centers. After adopting generative AI, the company cut case duration by 38% and reduced agent attrition by 94%.

GenAI shortens every case by quickly analyzing intent, pre-filling forms, suggesting next steps, and automating after-call tasks, so agents avoid repetitive work, hit their goals, and stay engaged in their roles.

3) Driving cost efficiency

Generative AI saves time and real money. Contact centers can do more with fewer resources by automating manual tasks, improving first-call resolution and lowering turnover.

It can also optimize staffing by forecasting demand and offering virtual support at scale. This allows companies to redeploy budgets into areas that drive growth and innovation instead of spending them on overtime, churn or inefficient workflows.

🔖Download Now: CCW Research | The Role of AI in Redefining Contact Center Efficiency

Top 6 use cases of generative AI in contact centers that deliver results

According to Gartner, 85% of customer service leaders plan to explore or pilot a conversational GenAI solution in 2025. But which use cases are moving the needle for enterprises? Let’s break down the top seven, with real impact.

1. Speech and sentiment analytics to improve CSAT and reduce churn

Many contact centers struggle to capture the complete picture of every customer interaction. Necessary signals such as frustration, confusion, and urgency get missed in manual reviews, leading to lost insights, poor coaching, and declining CSAT scores.

Use case: Generative AI transforms this by transcribing every call and chat in real time. It tags sentiment and instantly detects intent or escalation risks. Leaders can coach teams proactively, predict customer churn, and boost customer satisfaction while cutting hours of manual effort.

💡Pro Tip: Integrate GenAI in your contact center quality management software so you can spot a spike in negative sentiment instantly during product-related calls.

Platforms like Sprinklr can help managers get real-time alerts, investigate quickly, and retrain agents or update processes the same day, averting potential churn.

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🔖 Base Read: What Is Contact Center Sentiment Analysis for Businesses?

2. Real-time agent assistance

Boosts resolution speed

Agents often struggle to find the correct answers or solutions quickly, leading to longer handle times, customer frustration, and missed SLA targets.

Use case: Generative AI is capable of delivering live response suggestions, finding relevant knowledge articles, and providing contextual prompts to agents as they work, which helps them resolve queries faster and more confidently. This lowers average handle times and ensures more first-contact resolutions.

For example, when an agent faces a complex inquiry, GenAI instantly offers a step-by-step solution and drafts a personalized summary, letting the agent respond accurately and close the case on the first try. The image below demonstrates how this happens in Sprinklr.

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3. Automate after-call workflows

Save time and reduce agent fatigue

Manual after-call work, like writing summaries, tagging issues, and updating records, drains agent time and leads to errors or delays, fatiguing agents and reducing their productivity.

Use-case: Generative AI instantly summarizes tickets, tags issues, auto-documents conversations, and flags potential escalations, all within a single workflow. This automation frees agents from repetitive admin, allowing them to focus on high-value interactions and reducing overall handling costs.

4. Deploy autonomous virtual agents

Scales self-service, handles multilingual queries, and cuts wait times

Customer self-service is now an expectation, not a luxury. According to Gartner, 38% of Gen Z and millennials will abandon a service if they cannot resolve the accruing issue themselves. Moreover, without effective self-service, contact centers are likely to face higher volumes, longer wait times, and more frustrated customers.

Use case: Generative AI powers autonomous virtual agents and troubleshooting chatbots that can resolve common issues, handle multilingual conversations, and guide customers through intelligent troubleshooting. Consequently, there are fewer tickets, faster resolutions, and smoother experiences.

For example, when a customer starts a chat about a technical issue with a virtual agent, it gathers all the context and customer information, tries to diagnose the problem, and even provides a tailored step-by-step solution, sometimes fixing the issue with zero human involvement.

For a contact center handling 2M annual interactions, even shifting 20% of queries to AI self-service could free up the equivalent of 100+ full-time agents, significantly lowering operational costs.

5. Auto-generate content and knowledge articles

Keeps information always current

Outdated FAQs, help docs, and knowledge base articles slow agents down and frustrate customers who rely on self-service. Manually creating and updating this content is time-consuming and often lags behind product or policy changes.

Use case: Generative AI can create, update, and localize FAQs, help documentation, and knowledge articles in real time, automatically pulling from the latest product updates or support cases. It can even produce multilingual and voice-ready content to serve diverse customer bases.

💡Pro Tip: Look for contact center software that offers an AI knowledge base, capable of generating and translating FAQs and SEO-friendly articles in real time and in multiple languages.

Software like Sprinklr integrates KB with your CRM and database, ensuring both customers and agents have accurate, ready-to-use information from day one. It can even collect feedback, improve and update articles accordingly, and onboard new agents with step-by-step training manuals.

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6. Automate reporting and analytics

Speeds up decision-making

Manually compiling reports and analyzing performance data takes valuable time away from managers and QA teams, delaying operational decisions and limiting visibility into trends.

Use case: Generative AI can instantly generate performance reports, create real-time dashboards, and deliver deep-dive analysis on demand. This allows you to spot issues, track contact center metrics, and make data-driven decisions on the fly, without waiting for scheduled reporting cycles.

How to drive adoption of Gen AI in contact centers: A stepwise blueprint

More than 70% of Gartner’s 2023 survey respondents said hasty GenAI adoption will be a top-ranked legal and compliance issue over the next two years.

The truth is, in the rush to reap the benefits, many contact centers make premature moves and neglect foundational aspects of customer handling. The blueprint below outlines a secure, step-by-step adoption path that will help minimize operational and compliance risks.

Step 1: Align on outcomes and guardrails

➔ Define your desired outcomes (reducing handle time, improving CSAT, boosting first-contact resolution).

➔ Set guardrails around where AI can operate, accuracy thresholds, and when to escalate to human agents.

For example, you might allow GenAI to draft responses for routine queries but require a supervisor review before it reaches VIP account issues, to balance efficiency with customer trust.

Step 2: Build the data foundation

➔ Audit interaction data, resolve inconsistencies, and protect sensitive information.

➔ Centralize transcripts, chat logs, and KB content for secure, accurate AI access.

Remember, GenAI in contact centers is only as good as the data it’s trained and connected to.

Step 3: Establish responsible AI governance

➔ Set policies for how it will be prompted, how outputs will be reviewed and how errors will be escalated.

➔ Include controls to prevent hallucinations, enforce brand tone, and maintain audit trails.

You might require a daily review of AI-generated call summaries by team leads, ensuring accuracy and compliance before records are stored or shared.

Step 4: Select the right model and architecture

➔ Choose a GenAI model that fits your contact center’s needs, whether a vendor-hosted LLM, an open-source model or a hybrid, while balancing accuracy, speed, and cost.

➔ Define retrieval methods, context size, and integration points early.

For example, your contact center might select a model optimized for short latency to ensure real-time agent assist doesn’t slow live interactions.

Step 5: Design a low-risk pilot

➔ Start with a contained, high-volume workflow that offers clear ROI potential, such as automating post-call summaries or handling routine FAQs.

➔ Define success metrics, human-in-the-loop checkpoints, and fallback processes.

Your contact center could pilot GenAI for summarizing chats, with supervisors reviewing all outputs for the first 30 days to validate accuracy before scaling.

Step 6: Prepare agents and supervisors

➔ Train staff on how GenAI supports their work, not replaces it and set clear workflows for editing, approving, and giving feedback on AI outputs.

➔ Encourage agents to flag errors and share improvement ideas.

Your contact center could run live training where agents practice refining AI-suggested responses, building trust in the tool before full rollout.

Step 7: Integrate into real workflows

➔ Connect GenAI to existing systems like CRM, cloud telephony and knowledge bases to act within live customer interactions.

➔ Configure triggers for tasks such as drafting replies, suggesting next best actions or summarizing conversations.

Your contact center might integrate GenAI with its chat platform so agents see AI-suggested responses in real time, reducing typing time and improving consistency.

Step 8: Measure, learn and iterate

➔ Track key metrics like accuracy, CSAT, average handle time and AI override rates to assess performance.

➔ Review a sample of AI outputs regularly to identify improvement areas and retrain prompts or update data sources.

Through weekly reviews, your contact center might discover that AI summaries miss specific compliance details, prompting updates to its knowledge base and output rules.

Step 9: Scale with compliance and security built in

➔ Expand GenAI to new channels, geographies, or use cases only after confirming it meets all security, privacy, and regulatory requirements.

➔ Localize processes for laws like GDPR or TCPA and keep monitoring for risks.

For example, a contact center adding AI to voice calls in Europe might implement automated consent prompts before each interaction to ensure GDPR compliance.

🔖 Visit: Sprinklr’s Matchless Trust Center

Step 10: Plan the operating model and budget

➔ Define ownership across CX, IT, security, and legal teams to manage GenAI deployment and evolution.

➔ Set a budget for licensing, infrastructure, and ongoing optimization.

A contact center might allocate funds for quarterly model fine-tuning and cross-department AI governance meetings to ensure performance and compliance stay on track.

Real impact of generative AI in contact center CX strategy

Deloitte’s 2024 global contact center survey shows that one in six contact centers has already deployed generative AI capabilities. Let’s look at some real-world examples of generative AI in contact centers and how enterprises are actively realizing measurable benefits.

Samsung Europe: Turning multilingual complexity into higher conversions

Samsung Europe manages operations through decentralized, multilingual contact centers.

But these struggled with sharp demand spikes of up to 3 times the usual volume during product launches and low weekend traffic in smaller markets, which complicated shift coverage. They also relied on generalist agents who supported multiple product lines, limiting both conversion potential and the ability to deliver a high-touch customer service.

To address these challenges, Samsung piloted a program with Teleperformance — building a sales-trained offshore team based in the Philippines that is focused exclusively on mobile sales in Germany, Switzerland, and Austria. It used Sprinklr’s care console to arm the team with capabilities like generative AI-enabled real-time translation, sales tools, intent-based routing, and out-of-hours WhatsApp messaging to maintain a high-touch experience at scale.

In just four weeks, the team matched or exceeded local conversion rates. They handled 99.3% of chats, with two-thirds being meaningful, converted over one in five chats, achieved 94% CSAT, lifted revenue per chat by up to 25%, cut missed opportunities to under 1% and drove incremental weekend revenue through extended coverage, summed up perfectly by Simon Perrin, Chat Lead at Samsung Europe, as below:

DTE Energy: Cutting case durations and agent attrition by boosting agent satisfaction

Facing 40%+ annual attrition, long case durations, and missed SLAs, DTE Energy shifted to a digital-first contact center model focused on agent experience and efficiency.

With Sprinklr Service and generative AI capabilities, like intelligent live chat, AI-assisted workflows, and unified analytics, agents managed more interactions, responded faster and maintained quality, while leaders optimized in real time.

The result was an optimized case duration by 38%, raised agent satisfaction scores from 4.0 to 4.5 out of 5, and dropped attrition to 2.3% per month. The contact center also met first-response SLAs faster and improved its overall operational efficiency.

Planet Fitness: Improving speed and quality in social care at scale

With 38 social care reps managing up to 3,000 cases monthly across 2,400+ locations and multiple social channels, Planet Fitness needed speed without losing quality.

It adopted Sprinklr AI+ to help agents draft on-brand, channel-personalized responses and to give their reputation manager smarter listening queries with proactive alerts on emerging topics.

The result was faster, more thoughtful customer interactions and a better balance between speed and quality. Real-time insights from generative AI also helped the team act quickly on trends, strengthening customer relationships while maintaining high service standards.

Your contact center’s GenAI success hinges on the right partner and platform

Enterprises eager to deliver an exceptional customer experience must ensure personalized interactions, act with proactive intelligence, and operate from a unified platform. Using publicly available GenAI might lead agents to believe they are generating faster replies, but in reality, they are using only a fraction of AI’s potential while putting both enterprise and customer data at risk.

With a trusted service provider like Sprinklr, trusted by brands like Microsoft, Ikea, and Uber, you gain a full stack of AI-powered contact center tools purpose-built for next-generation customer experience.

Sprinklr delivers everything from agent assist to AI analytics to automated self-service, all with enterprise-grade compliance. Get your personalized free demo today and receive a hands-on guide to adopting GenAI with confidence and security.