Theme: The future doesn’t converge on an either-or choice between HR and Robot technology; instead, the winning formula is a human-AI collaboration. Response speed and availability merge with empathy and unique expertise to provide a unique contact center customer experience.
Introduction
As a part of the GenAI revolution, enterprise call centers are experiencing groundbreaking changes. These include mind-boggling routine task automation inroads, significantly improved customer engagement, optimized workflows, and actionable insights beyond our recent imagination. How do we know this? Three primary indications:
According to a Zendesk survey covering the customer service arena, early AI adopters have been delighted to yield a 90% return on their investments (on average).
Aligning with (1) above, McKinsey's research estimates that customer care functions deploying generative AI tools can expect to increase productivity between 30 and 45 percent (with a massive impact on operating overheads).
According to research from 8x8’s table below, 69% of AI-empowered contact centers have improved their customer service capabilities, with a 48% leap in customer satisfaction.
Call centers in competitive marketplaces
In short, an accelerating "dog-eat-dog" environment has increasingly spotlighted customer satisfaction as an essential focus. Illustrating this, Intercom states, "A whopping 87% of support teams say customer service expectations have increased in the past year." Indeed, enterprise call center stakeholders cannot ignore the potential AI contributions to achieving stellar customer satisfaction KPIs.
Call center competitors aren't standing still. Are you?
AI implementation can be a daunting task. Why? Traditional challenges, such as data silos, integration with legacy systems, and employee resistance, hinder effective implementation. IBM research shows that these barriers to full AI functionality in the workplace keep 40% of enterprises in the exploration and experimentation phases.
Despite the above, a Metrigy survey shows that 70% of contact centers increased their AI spending in 2023 and 2024, a trend expected to continue in 2025. In other words, enterprise call centers must start investing in AI systems to keep up with the competition. However, strategic initiatives require careful forethought to get it right, erase trial and error, and yield the ROI you expect and deserve. If this resonates with you, read on.
The right call center AI thinking for the present and future
The optimal AI strategy involves embracing modular, API-friendly solutions, phased rollouts, and change management to significantly put AI integration onto a smoother, faster, and more cost-effective path. Observe these fundamentals, and the long-term rewards will emerge, substantially outweighing all the perceived barriers preventing enterprise call centers from enjoying the abundant fruits AI offers. Moreover, to ensure a seamless process, consider the Callback team, with its heralded expertise and technology resources, as the partner you want on your shoulder. They’ll cover your journey from end to end for an exceptional AI-centric outcome.
AI Applications in Call Centers
AI is a broad concept, so let's start with a brief overview of AI use cases in enterprise call centers. According to Metrigy, the following eight applications are the most prevalent uses of AI in the sector.
Customer Self-Service Bots:
AI-powered bots empower customers to resolve their inquiries independently through interactive FAQs or guided troubleshooting.Contact Center Workforce Engagement Management (WEM):
AI tools in WEM analyze agent performance and customer interactions to optimize scheduling, training, and resource allocation.Triage Bots:
Triage bots assess customer inquiries and direct them to the appropriate department or agent based on urgency and complexity.Agent-Assist:
AI-powered agent-assist tools provide call center employees real-time suggestions and information during customer interactions, helping them resolve issues more effectively.Employee Self-Service Bots:
These bots allow employees to access company resources or IT support autonomously, reducing the burden on support teams while helping employees find solutions quickly.Voice of the Customer (VoC) Programs:
AI-driven VoC programs analyze customer feedback from various channels (surveys, social media, etc.) to extract insights about customer preferences, helping organizations make data-driven decisions.AI for employee collaboration:
AI enhances employee collaboration by automating routine tasks, improving communication, and providing real-time insights.Identity Verification:
AI-driven identity verification systems enhance security by quickly and accurately confirming user identities.
The most significant AI challenges and solutions facing call centers today.
Challenge #1 - Data Silos:
In enterprise call centers, data silos can signify a significant barrier to effective AI implementation. Why? Unaddressed, they create undesirable ripple effects as follows:
Disconnected data storage prevents AI from accessing a holistic view of customer interactions essential for automation and personalization.
Forbes underlines this item, pointing out that 95% of businesses struggle with managing unstructured data.
Customer data scattered across various platforms—such as CRM systems, IVR solutions, and other databases—obstruct agents from providing proper service.
Data fragmentation represents the opposite of a McKinsey research report that reflects between 71 and 76 percent of customers demand two vital service considerations:
Personalization
Customization.
A lack of one or the other creates considerable customer dissatisfaction, enough to snap long-standing brand loyalty.
The Data Silos Solution: Data lakes, middleware, and AI configurations.
What is a data lake? It's a centralized repository for storing vast amounts of structured and unstructured data. It allows:
AI systems to:
Collect data from several diverse sources.
Review customer interactions across multiple touchpoints comprehensively.
Organizations to leverage silo data with agility and flexibility.
As Salesforce writes: "Data lakes let you build AI initiatives on a vast and diverse data foundation. That foundation is ideal for training AI and machine learning models to personalize customer experiences, make predictions, inform decision-making, and offer real-time recommendations."
What is middleware? It acts as an intermediary layer that facilitates communication between different software. By connecting applications and platforms, middleware enables and ensures:
A smooth data flow.
Excellent information access and utilization across various systems.
Extensive system overhauls are a thing of the past.
Visualization of middleware. Source: Red Hat
In summary, data lakes and middleware solutions serve distinct yet complementary roles in overcoming data silos.
Challenge #2 - Legacy Systems:
According to McKinsey, 70 percent of Fortune 500 companies' software dates back twenty years (and longer).
Also, many call centers rely on legacy systems like CRMs, knowledge bases, and communication platforms (not designed for new AI technologies.
As a result, it's challenging to integrate modern AI solutions with existing infrastructures in enterprise call centers, leading to delays, cost overruns, and operational disruptions.
The Legacy Solution: Modular, API-friendly, AI-centric remedies and phased implementation strategies.
With modular, API-friendly AI algorithms, call centers can get the best of both worlds - keeping legacy systems while also reaping the benefits of automation and AI. How? API (Application Programming Interfaces) enables different software applications to communicate with each other.
In call centers, APIs enable stakeholders to:
Integrate new software with older versions of CRM systems, knowledge bases, self-service portals for customers, and more.
Create a smoother data exchange and communication between the old and the new without the need for a complete system overhaul.
Adopt a phased implementation strategy that helps to mitigate risks of connecting new technologies into early-vintage legacy systems.
A Tip: Gradually introduce AI capabilities—starting with less complex use cases—letting your team refine the processes, gain valuable insights, and build confidence before scaling up.
To sum up, API-friendly AI solutions:
Facilitate seamless communication between legacy and modern systems.
Enable real-time data exchange without complete system overhauls.
Reduce implementation time, risk, and costs compared to complete system overhauls.
In the words of OpenLegacy: "Integrating legacy systems allows organizations to quickly adapt to changing market conditions and leverage new technologies without abandoning their older systems."
Looking ahead, AI systems for call centers will become even more sophisticated and all-encompassing. With spectacular growth and innovation expected in this space, we advise investing in modular systems with professional advice offered by companies like Callback. We create new opportunities for stakeholders to deploy new technologies without hassle, downtime, or unnecessary expenditure.
Projections for market growth in call center AI. Source: Future Market Insights
Challenge #3 - Employee Resistance:
The third challenge in AI implementation is organizational. Why?
An AI-enhanced workplace creates employee resistance based on threatening traditional roles and job descriptions.
According to Intercom, 61% of C-level support executives are more optimistic about AI's potential to bolster team success than a year ago. In contrast, only 45% of support agents feel the same way.
Management fears employee resistance is the most formidable obstacle to implementing new technologies.
A lack of team alignment inevitably leads to fragmented efforts and confusion regarding the AI implementation process. It can derail the process, resulting in wasted resources and missed opportunities for holistic integration.
The Employee Resistance Solution: Effective Change Management and a Prototype Approach.
According to research from Deloitte, organizations that invest in change management are more likely to outperform organizations that don't invest in change management by between fifty and sixty percent higher in:
Enjoying AI initiatives that exceed expectations.
Achieving positive outcomes.
Why? Clear agent communication about AI implementations drastically improves implementation processes. It involves:
Articulating the vision for AI integration
Outlining how it will benefit both the organization and its employees.
Removing tedious, repetitive tasks from job descriptions.
Focusing on the rewarding aspects versus the pitfalls.
Transparency with regular updates and open channels for feedback, addressing concerns, and building trust among team members.
A common misconception is that it's an either-or choice between HR and Robots when the winning formula is a human-AI hybrid collaboration. While technology excels in response speed and availability, people are crucial in situations calling for empathy and unique expertise.
As James Laurie, CX Operations Specialist at Up Learn, says: "AI makes support teams happier because it reduces the number of queries they have to handle, making their workload far more manageable." He underlines that agents spend less time putting out fires and more time enriching customers' lives, which is a more rewarding 360-degree outcome and a valuable perspective for employees in AI implementation processes.
A Tip: Mitigate risks with prototype projects
Start with low-risk prototype projects for a more seamless AI transition. Before deploying more ambitious initiatives, companies can obtain essential insights, both technical and organizational, by testing and improving AI solutions on a smaller scale. This strategy not only reduces risks but also gives staff members a firsthand look at the advantages of AI, which boosts support for upcoming projects.
Key Metrics for AI Success
Defining and tracking specific, measurable outcomes to measure the impact of AI implementation in an enterprise call center is crucial. Relevant metrics will vary from organization to organization. Here are key metrics to consider:
Customer Experience Metrics:
Average Handling Time (AHT): AI-powered tools can significantly reduce AHT (e.g., by helping agents understand and address queries from customers.) According to Intercom research, fast and efficient resolution of customer issues is a primary advantage for 44% of enterprises using AI for customer service.
Customer Satisfaction (CSAT) Scores: AI-powered personalization can enhance customer satisfaction. An Aberdeen Research study discovered that companies leveraging AI achieve a 3.5 times greater increase in customer satisfaction rates.
First Call Resolution (FCR) Rate: AI can improve FCR rates - a crucial parameter impacting customer satisfaction, operational efficiency, and overall call center performance. Research shows that 82% of teams using AI see higher FCR rates.
Operational Metrics:
Improved agent productivity: The National Bureau of Economic Research finds that "generative AI-based conversational assistants increase as measured by issues resolved per hour, productivity by 14% on average." The increase in productivity is 34% for novice agents.
Reduced operational costs: In 2022, Gartner projected that conversational AI will reduce contact center agent costs by a whopping $80 billion in 2026.
Adoption Metrics:
Employee engagement: AI in call centers can boost employee satisfaction by drastically reducing routine tasks, solving tickets, and addressing emails before they even reach agents.
Conclusion: A Roadmap for Success and FAQs
As we have seen, the transformative potential of AI for enterprise call centers is almost endless: from increased efficiency to higher customer satisfaction, seamless workflow, and significantly improved personalization. However, scattered data, outdated legacy systems, and organizational challenges in navigating technological change are severe barriers to successful AI implementation.
Callback, as a leader in the contact center industry, recommends an actionable step-by-step roadmap that your organization can follow today for AI implementation:
Evaluate Current Data Infrastructure
Assess your existing data infrastructure.
Select the most suitable for data lakes and middleware solution transformation.
Identify Existing Legacy Software Limitations
Organizations must audit their legacy systems to understand potential integration barriers and how modular APIs can help with AI integration.
Assess Organizational Culture and Change Readiness
Contact centers must proactively address the significant optimism gaps with:
Comprehensive change management strategies.
Identifying internal change champions.
Communicating AI's benefits transparently.
Fostering a collaborative human-AI approach that demonstrates how AI augments rather than replaces employee capabilities.
Identify low-risk AI prototypes
Finally, this will help you gain insights into AI implementation management with less risk and more sure-footed leadership. Look for a low-risk project that doesn't interfere with the call center's mainstream operations while addressing key metrics.
The remote customer service paradigm has changed forever with the widespread AI integration in call centers. As Intercom writes: "AI has already influenced customer expectations, and they're higher than ever. Now that customers know how AI can improve their service experience – making it faster and more efficient – they're unwilling to wait around for anything less."
Following the roadmap above with Callback in the wings, your organization is well on the way to successfully implementing AI and reaping the benefits of generative AI, increased productivity, personalization, and customer satisfaction. Call us today for a no-obligation, frank conversation about your AI needs, and never look back.
FAQs
Q1: What are the three big AI challenges facing call centers in the US?
A: (a) Data silo malfunctions (b) Legacy software integration (c) Employee resistance.
Q2: What are the solutions to the three big challenges?
A:
Data Lakes, Middleware, and AI configurations address data silo malfunctions.
Modular, API-friendly, AI-centric remedies and phased implementation strategies address legacy software integration.
Effective Change Management and a Prototype Approach address Employee resistance.
Q3: What is a Data Lake?
A: It's a centralized repository for storing vast amounts of structured and unstructured data.
Q4: What is middleware?
A: It acts as an intermediary layer that facilitates communication between different software. By connecting applications and platforms, middleware enables and ensures:
A smooth data flow.
Excellent information access and utilization across various systems.
Extensive system overhauls are a thing of the past.
