The podcast: How GenAI Will Transform The Contact Center
Source: Forrester
Why we picked it: The contact center in the digital age is one of the first impressions — and last impressions — a consumer will have when interacting with your financial institution. That being said, it can be the most undervalued and underinvested technologies in a bank or credit union’s tech stack. Our priority is always to get readers the best practices for solutions on issues that matter most to their customers and brand reputation. The Forrester podcast always has great solutions for the customer experience professional, and this one was just one of many great podcasts from their team.
Executive Summary
Generative AI is transforming the very heart of customer engagement: the contact center. This isn’t just another tech upgrade — it’s a paradigm shift that promises to redefine how financial institutions connect with their customers, streamline operations and deliver experiences that were once thought impossible.
But as with any revolution, this one has come with its own set of challenges and opportunities. In a recent podcast with Forrester, senior analyst Christina McAllister shed light on the intricate landscape of generative AI in contact centers, offering a glimpse into the current state of AI implementation, the future of customer service and the steps that financial institutions can take to stay ahead of the curve.
Key Takeaways
- Generative AI in contact centers focuses on three main areas: customer-facing use cases (like chatbots), agent-facing applications (such as co-pilots), and analytics for deriving insights from unstructured data.
- The most widely adopted use case for generative AI in contact centers is post-call summaries, which can save agents 2-4 minutes per call and improve accuracy.
- While gen AI presents significant opportunities, most contact centers aren’t fully ready for implementation due to underlying infrastructure issues and the need for improved knowledge management strategies.
- The future of contact center agents will likely involve handling more complex, emotionally charged situations as AI takes over simpler tasks, requiring a shift in hiring profiles and skill sets.
The Tripartite Revolution of Gen AI
As we delve into the world of generative AI in contact centers, it’s crucial to understand that this technology isn’t a one-size-fits-all solution. Instead, it’s reshaping the industry across three distinct, yet interconnected, domains.
First and foremost, we have the customer-facing applications. For years, businesses of all kinds have dreamed of chatbots that could understand and respond to customer queries with human-like intelligence, and generative AI is getting steadily better at it. Advanced chatbots and virtual assistants are not just capable of handling simple, scripted interactions, but they’re evolving to manage increasingly complex customer inquiries.
The second domain where generative AI is making waves is in agent support tools. Picture a world where every customer service agent has an AI co-pilot, a digital assistant that provides real-time information, suggestions and support during customer interactions.
Lastly, the third pillar of this AI revolution lies in the realm of analytics and insights. Contact centers have always been goldmines of customer data, but most information is locked away in unstructured formats: call recordings, chat logs, and email threads. However, by instead structuring and analyzing this unstructured data, financial institutions can now gain unprecedented insights into customer behavior, preferences and pain points.
What Are the Low-Hanging Fruit of Generative AI?
While the potential of generative AI in contact centers has only begun to tapped, banks and credit unions don’t need to wait for a complete overhaul to start reaping benefits. One of the most readily available and impactful applications is in post-call summaries, which McAllister points out is one of the most highly adopted use cases.
“Historically, that’s been two to four minutes of an agent tapping at a screen, tapping at their keys and trying to consolidate all of the notes that they have about the customer,” she explains. “They’re not consistent, they’re often not accurate.”
Consider the math: if an agent spends just two minutes on each call summary, and handles 50 calls a day, that’s 100 minutes — nearly two hours — spent on administrative tasks rather than helping customers. Multiply that across an entire contact center, and the potential time and cost savings become staggering. But it’s not just about efficiency. These AI-generated summaries are consistently formatted, capturing key details that human agents might miss.
The result? Better documentation, improved customer insights and a solid foundation for further AI-driven improvements.
The Road to Implementation: Challenges and Prerequisites
While the benefits of generative AI in contact centers are clear, the path to implementation is not without its hurdles. Most contact centers aren’t yet fully ready to embrace this technology, McAllister says. These systems — while functional — weren’t designed with AI integration in mind. Upgrading this infrastructure isn’t just a matter of installing new software; it often requires a fundamental rethinking of how the contact center operates.
“Generative AI has really been an important catalyst in the contact center and also enterprises at large, but in the contact center, it’s been a little bit of a kick for folks to dig in and tackle some of the challenges that have been lurking around for years but have been really hard to prioritize,” she adds.
Equally crucial is the state of knowledge management within the organization. Generative AI thrives on data, but that data needs to be organized, accessible and high-quality. Many institutions are finding that their current knowledge management strategies are inadequate for the demands of AI. Developing a coherent, comprehensive knowledge management system is often a prerequisite for successful AI implementation.
Then there’s the human factor. The AI readiness of agents and supervisors varies widely across organizations. This is compounded by the high attrition rates common in contact centers. McAllister points out, “we have that added wrinkle that contact centers have super high attrition, so you have that perpetually rotating roster of novice agents, which is especially challenging when you’re navigating a space that is changing as quickly as the contact center is.”
Dig deeper into banking chatbots:
- Nine Best Practices As Banks’ Online and App Customer Experiences Converge
- Why Banks Risk Data Chaos by Rushing Into AI for CRM
- How Banks Can Leverage AI at the Contact Center
Charting the Course: Strategies for Successful AI Integration
Given these challenges, how can contact centers successfully navigate the transition to generative AI? The key, according to McAllister, lies in taking an incremental, strategic approach.
Rather than attempting a wholesale transformation overnight, contact centers should focus on incremental evolution. This might start with implementing AI in specific, high-impact areas — like the post-call summaries mentioned earlier. These initial successes can then build momentum and provide valuable learnings for further implementation.
When it comes to technology choices, prioritizing open and integratable solutions is crucial. The AI landscape is evolving rapidly, and today’s cutting-edge solution might be tomorrow’s legacy system. By choosing flexible, interoperable technologies, contact centers can avoid vendor lock-in and remain agile in the face of technological change.
The question of cloud migration often arises in discussions of AI implementation. While cloud-based solutions offer numerous advantages, McAllister emphasizes that a full migration to the cloud isn’t always a prerequisite for AI implementation. Some innovative vendors have developed solutions that can integrate with on-premise systems, allowing banks and credit unions to start benefiting from AI without undertaking a complete infrastructure overhaul.
How to Successfully Embed AI Agents Into Your Contact Center Strategy
For contact centers looking to begin their generative AI journey, McAllister offers several practical suggestions:
Start with the low-hanging fruit. Implementing AI-powered post-call summaries can provide quick wins and demonstrate the potential of the technology. This can help build organizational buy-in for further AI initiatives.
Take a deep dive into your agent workflows. Understanding where agents spend their time and what tasks are most burdensome can help identify prime opportunities for AI-driven improvement. This might involve implementing real-time support tools for knowledge access and guidance.
Don’t neglect your knowledge management strategy. Audit your current knowledge bases and information systems. Are they comprehensive? Up-to-date? Easily accessible? Improving your knowledge management can yield benefits even before AI implementation and will set you up for success when you do implement AI tools.
Invest in your people. As you implement AI tools, don’t forget about the human side of the equation. Provide training not just on how to use the new tools, but on the evolving skills needed in an AI-augmented contact center. This might include training on emotional intelligence, complex problem-solving, and how to work effectively alongside AI systems.
Keep the big picture in mind. While it’s important to start small and show quick wins, always keep your long-term vision in sight. How do you want your contact center to operate in three years? Five years? Ten years? Let this vision guide your AI implementation strategy.
Remember, the goal of generative AI in contact centers isn’t to replace human agents, but to augment and empower them. By providing agents with powerful AI-driven tools and insights, financial institutions can create a synergy between human empathy and machine efficiency, delivering customer experiences that are truly transformative.
Editor’s note: This article was prepared with AI language software and edited for clarity and accuracy by The Financial Brand editorial team.