Tips to Ensure Service Reps Can Access the Info They Need
Customer expectations are rising, and so is the complexity of delivering great support. For businesses running contact centers, the difficulty isn’t just about answering questions quickly; it’s about anticipating and understanding customer needs in real time and responding swiftly, precisely, and empathetically.
Enter the latest iterations of contact center intelligence (CCI) and a new wave of technology that blends artificial intelligence, automation, and data-driven insights to assist agents right when and where they need it most. By moving beyond outdated tools and siloed systems, CCI can transform the customer experience into one that’s smoother, more intuitive, and decidedly more human. In today’s customer service-driven economy, adopting these intelligent capabilities isn’t just an upgrade; it’s a necessity.
CCI in 2025 deftly leverages artificial intelligence, automation, and advanced analytics to enhance the efficiency and effectiveness of customer service operations. Rather than replacing human agents, its purpose is to empower them, delivering timely, relevant information, streamlining routine processes, and offering data-driven insights that enable faster, more accurate problem solving.
Brian Vargas-Meinel, chief technology officer of Tovie AI, says CCI is about using the latest technology and real-time data to help agents deliver faster, more accurate, and more personalized support. “It includes tools that understand customer intent, predict next actions, surface relevant information, and automate routine interactions,” he says. “At its best, it makes customer conversations smoother and agent workflows more efficient. It’s no longer just about dashboards and reporting. It emphasizes systems that support agents during the conversation, not after it.”
Harry Folloder, chief digital and technology officer of Alorica, subscribes to that theory as well. “CCI today is no longer about canned scripts or basic general data lookups; it’s centered on AI-powered, complex insights delivered in real time, exactly when agents need them,” he states. “Think of it as having a knowledgeable coach that whispers the right answer in your ear at precisely the right moment. It’s the blend of generative AI, machine learning, and natural language processing to empower agents in the moment.”
In addition to employing AI, CCI includes tools like smart routing that connects customers to the right person quickly, knowledge bases that are easy to search, and analytics that help teams continuously improve, according to Pablo Payet, senior manager of executive escalations and customer care for Granicus, providers of a citizen engagement platform for government.
“But it goes far beyond basic automation or analytics—it’s focused on creating an adaptive, real-time ecosystem that continuously learns, improves, and empowers both customers and agents,” notes Rob Brame, a solutions consultant at Omilia, a conversational AI provider.
Others, like John Nash, vice president of strategic initiatives at Redpoint Global, believe modern CCI should be defined as a company’s ability to use a personal understanding of a customer to help progress a customer journey, ideally in a way that benefits both the company and the customer.
“That increasingly means refining messages and offers in the context and cadence of the customer, which is increasingly omnichannel,” Nash continues.
How CCI Has Evolved
Bill Balvanz, a contact center customer specialist with Sinch, maintains that CCI has transformed dramatically over the past decade, mainly due, unsurprisingly, to AI. By managing routine, repetitive inquiries, AI allows human agents to focus on more complex issues where their expertise is truly needed, thereby easing the burden on staff, reducing operational costs, increasing first-contact resolution rates, and ultimately enhancing overall service quality.
“Also, the evolution of real-time and historical analytics has unlocked a new level of operational intelligence,” Balvanz says. “Supervisors and managers can now respond in the moment, spotting rising call volumes or struggling agents while also using historical data to forecast staffing needs and make smarter, more strategic decisions.”
Additionally, the advancement of CCI has moved the emphasis beyond simple efficiency toward delivering proactive, tailored, and more natural interactions, driven, of course, by sophisticated, next-generation technologies.
“This transformation empowers agents, enhances customer experiences, and optimizes operations, making contact centers smarter and more customer-centric,” points out Suresh Kumar Bennet, executive vice president and global head of business process services at Hexaware Technologies. She summarizes several examples of the major changes under way at contact centers:
- Proactive service, with a transition from reactive support to predictive analytics that anticipates customer needs.
- Conversational AI, with a shift from basic chatbots to AI-powered assistants capable of handling complex, humanlike interactions.
- Real-time agent support, with AI that provides live recommendations, call transcriptions, and next best actions to agents for faster issue resolution.
- Omnichannel integration.
- Personalization by analyzing customer data and preferences.
- Automation for repetitive tasks like ticket categorization and follow-ups.
- Sentiment analysis in real time, helping agents adjust their tone and approach dynamically.
- Generative AI for content creation, such as drafting responses, FAQs, and training materials.
- Workforce optimization that predicts call volumes and adjusts agent schedules for peak efficiency.
- Data-driven insights into customer behavior and agent performance.
“This evolution has been transformative,” Folloder insists. “Imagine going from using paper maps to having real-time satellite navigation with traffic updates; that’s what it feels like having gone from computer scripts and reactive knowledge bases to dynamic, AI-driven ecosystems that support agents.”
The rise of large language models has, of course, accelerated the amazing pace of progress in recent years.
“LLMs can now generate contextual replies, summarize past interactions, and even guide agents through complex cases. Sentiment and intent detection have also matured as well, allowing systems to understand the emotional tone of a call and respond accordingly,” Vargas-Meinel says.
“And the agent experience has changed a lot, too. Instead of juggling five different tools at once, there’s now a real push for unified platforms,” Payet adds.
Rob McDougall, CEO of Upstream Works Software, also credits much of this progress to the advancement of agent assist applications, which can track what customers are saying (or typing) and recommend responses or correct documentation in real time to an agent.
“Now, the agent gets the information more quickly with less work, shortening contact times overall, and the information is more likely to be correct, reducing repeat calls and improving first-call resolution overall,” McDougall says. “Things like call dispositioning and summarization ensure that after-work policies are followed, without lengthening the agent’s time on the interaction but also shortening a future interaction because the information is there.”
And in another change, past contact center AI was largely dependent on rigid, rule-driven chatbots or interactive voice response systems with limited adaptability. But thanks to recent progress in natural language understanding, LLMs, and contextual AI, modern solutions now facilitate seamless, humanlike interactions across both voice and digital platforms.
“For example, in banking, instead of navigating complex IVR menus, customers can simply ask, ‘What’s my available balance after pending transactions?’ and get a precise answer instantly,” Brame says. “We’ve moved from after-call analytics to in-the-moment guidance, with AI surfacing knowledge, suggesting responses, and analyzing sentiment live.”
Real-World CCI Successes
Several companies are prime examples that have benefited from robust CCI. Take Amazon, which uses AI, automation, and real-time analytics to deliver proactive, personalized support with fast resolution times. Meanwhile, Reliance Jio excels through strong back-end systems and automated self-service, enabling quick issue resolution without agent intervention. Always innovating, Apple combines CRM tools, sentiment analysis, and expert training to provide empathetic, tailored support. And Delta Air Lines now streamlines service during disruptions using AI and sentiment tools, ensuring timely and adaptive responses.
“Monzo is another great example: They use AI to route and triage queries with speed and precision. Agents get full visibility of the customer journey, which means fewer handovers and better outcomes,” Vargas-Meinel says. “Also, American Express stands out for how it empowers agents with intelligent tools while keeping a human touch.”
Smaller players can vouch for the benefits of CCI progress, too. Just ask Amra Beganovich, founder of Colorful Socks, a lifestyle e-commerce brand.
“As our operations expanded and we managed thousands of inquiries during peak seasons, we learned that fragmented access to customer data is a challenge our agents constantly face. When shipping details, order history, and customer conversations all exist in different systems, agents waste time toggling between platforms. That delay not only hinders resolution; it erodes customer trust.”
Consequently, Beganovich’s company built an AI-powered support dashboard that aggregates real-time data from sources like Shopify, its shipping partners, and customer messaging channels. The dashboard can now bring up entire order histories, flag problems like delivery delays, and suggest reply templates based on the message’s tone and keywords.
“Since we implemented this system, we’ve reduced average response time by 40 percent, and we’ve increased first-contact resolution,” she adds.
But not every CCI implementation goes smoothly. Modern contact center agents are confronted with a perfect storm of challenges: an increasing number of communication channels, an overload of data, rising customer expectations, and fragmented systems. As demonstrated by Beganovich, one of their greatest daily obstacles is retrieving the right information at the right time. Often, agents must navigate through multiple disconnected platforms, such as CRM systems, knowledge bases, and transaction tools, forcing them to search for answers while the customer waits.
“And even when data is available, it’s rarely contextualized or prioritized,” Brame laments. “With routine inquiries increasingly handled by self-service AI, agents now deal with more complex, emotionally charged problems that require instant, accurate access to deeper knowledge. Agents don’t just need answers, they need context such as historical interactions, sentiment analysis, and policy nuances to resolve these escalated cases effectively.”
Context is also a major gap.
“Without real-time access to a customer’s full history, sentiment, and intent, agents are left making guesses or asking repeat questions. This slows everything down and damages trust,” Vargas-Meinel cautions.
Ineffective search tools make these challenges worse, hindering the quick retrieval of information and often presenting irrelevant results. On top of that, the sheer volume of disorganized data creates additional difficulty.
“The bottom line is that agents need to locate the right data, understand it instantly, and communicate it clearly, all while keeping the conversation flowing naturally,” Balvanz adds. “The difficulty lies in knowing where that information resides and building proficiency in accessing it, consuming it, and relaying it to customers in a confident way that doesn’t make it seem like they’re seeing it for the first time.”
Noteworthy Solutions
Fortunately, the latest tech tools can solve many of these issues and streamline operations significantly. The most effective strategies today prioritize real-time access to accurate, contextually rich data, which not only improves agent productivity but also elevates the overall customer experience.
Here’s a roundup of some of the most effective CCI resources in use today:
- Generative AIhelps automate replies, templates, and knowledge articles, tailoring them to the customer’s query and context.
- Omnichannel platformsunify voice, chat, email, and social media in one interface, allowing agents to switch channels without losing context.
- Agentic AI frameworkswork alongside human agents, offering real-time suggestions, automating tasks, and enhancing decisions using generative AI.
- AI-powered knowledge assistants deliver real-time, context-aware recommendations based on customer history, automating tasks like ticket tagging and response drafting.
- Unified knowledge systemscentralize support content like FAQs and guides into a single searchable hub.
- CRM integration with knowledge basesgive agents a unified view of customer data and support content, enabling personalized service and workflow automation.
- Sentiment analysis toolsuse AI to detect emotions and tone during interactions, offering agents real-time insights and next-step suggestions. Resources like CallMiner help agents respond empathetically and allow managers to refine strategies based on emotional trends.
- Workforce optimization toolsforecast demand and schedule agents accordingly while also providing training and performance monitoring features. Platforms like NICE or Verint improve readiness and efficiency by aligning staffing with customer needs and performance insights.
- Real-time collaboration tools, such as Slack or Microsoft Teams, allow agents and supervisors to communicate instantly, share resources, and solve issues collaboratively.
Vargas-Meinel is particularly impressed by AI-powered agent assist systems, which monitor calls or chats in real time and offer next best actions, suggested replies, or knowledge snippets.
“Today, we also have unified desktops that can bring all customer data, across CRMs, help desks, and communications systems, into one view, removing context switching and speeding up resolution,” he says. “Additionally, smart knowledge engines can use AI to match customer queries with internal content and policies, providing relevant, bite-size information instantly so agents don’t waste time searching. Also, intent and sentiment tracking help route queries to the right person or trigger escalation automatically, adding another layer of support in high-pressure situations.”
Cloud-based contact centers, meanwhile, provide significant benefits over traditional on-premises setups in helping agents handle customer problems quickly and effectively. These platforms mainly function through API interactions, offering a high level of customization and easy integration with external CRM systems.
“With cloud-based contact centers, once caller information is accessed, these integrations provide historical context, enabling faster troubleshooting and more personalized interactions. When combined with a knowledge base and even AI-powered suggestions, agents can resolve issues more efficiently, increasing first-call resolution rates and reducing handle time,” Balvanz says.
Balvanz’s recipe for CCI success? Choosing the right partner.
“The hardest part is finding a vendor that not only meets your current needs but can also scale with your business while offering strong customer service and training support to ensure a smooth transition into a more efficient, agile, and future-ready environment,” he explains.
Be sure to involve your agents in the rollout of any new tools, collect their feedback, and update processes based on what’s working in practice, Vargas-Meinel adds.
Bennet suggests the following additional steps to maximize the effectiveness of your contact center and better position your agents for success:
- Regularly train agents on tools, soft skills, and simulated scenarios.
- Gather agent and customer feedback to refine tools and workflows.
- Collect customer feedback to improve your product.
- Run campaigns in more effective ways through personalized messaging over customer-preferred channels.
- Ensure accurate, integrated customer data across systems.
- Leverage AI analytics for trend identification and proactive support.
- Use AI to tailor interactions based on customer history and sentiment.
- Boost engagement with rewards, challenges, and performance tracking.
- Regularly update tools and test usability for efficiency.
- Reduce workload with automation and support mental health initiatives.
- Define metrics and refine tools to meet business objectives.
“Most importantly, your company must shift from a technology-first mindset to an agent-first approach when deploying these resources and strategies,” Folloder insists.
Investing in AI will be crucial, but don’t forget that a good customer experience is still a mix of self-service and assisted service, McDougall says.
“Drill down into how customers are contacting you,” he recommends, “and ask yourself: Why are they contacting you and in what volume, and how well are the individual agents handling those interactions?”
Looking ahead, the most successful contact centers will be those that treat intelligence solutions not just as efficiency tools but as platforms for continuous learning and growth, Folloder predicts.
“The true power of AI in customer experience lies not just in automation,” he says, “but in its ability to create a feedback loop that sharpens both machine and human performance over time.”
Erik J. Martin is a Chicago area-based freelance writer and public relations expert whose articles have been featured in AARP The Magazine, Reader’s Digest, The Costco Connection, and other publications. He often writes on topics related to real estate, business, technology, healthcare, insurance, and entertainment. He also publishes several blogs, including martinspiration.com and cineversegroup.com.