Contact Centers in 2027: How AI Assistants Revolutionized Customer Service
By Adam Thies, Anna Ng, and Mike Jortberg
Date: April 4, 2027
My name is Alex, and I’ve been a customer service executive for over a decade now. Little did I know that the contact center landscape was about to undergo a seismic shift, one that would completely redefine the way we interact with customers.
It all started back in 2024 when the first stories of generative AI assistants’ impact began making headlines. The example that really caught everyone’s attention was Klarna, the Swedish financial services company, when it reported that after just a month of deploying their GenAI assistant, it had handled a staggering 2.3 million customer service chats — two-thirds of its total volume! It was the equivalent of 700 full-time agents’ workloads, and it slashed the average service time from 11 minutes down to just 2 minutes.
At the time, those numbers were simply mind-blowing, and they prompted a tidal wave of contact centers, including my own, to jump on the GenAI bandwagon. We were all racing to leverage these AI-powered assistants, using real-time information to proactively resolve issues and prevent future interactions altogether. The gains in productivity and efficiency were unprecedented.
But what we didn’t realize back then was that this was just the tip of the iceberg. The boost in productivity ushered in a whole new set of challenges and opportunities that would fundamentally alter the roles and responsibilities of everyone at the contact center.
Executives: Navigating financial growth
As an executive at our contact center, navigating the financial implications of GenAI was a top priority. The initial investment was steep, but it was quickly offset by the surge in productivity and cost savings. We were reinvesting those savings into business growth, expanding our market share and customer touchpoints where AI could be deployed. It was a delicate balance, though, as we had to closely monitor new metrics like task productivity and compute costs to prioritize our use cases effectively.
Managers: Evolution into strategic business leaders
For our managers, their roles evolved from operational supervisors into strategic business leaders. With the newfound capacity for seamless customer interactions, they needed to develop a deep understanding of how to optimize processes and workflows using GenAI. Allocating resources for the ideal human-AI balance became their primary focus, and the ones who could master this balance saw their teams’ productivity and job satisfaction skyrocket.
IT solution designers: Increase of AI applications
Our IT solution designers were under immense pressure to keep up with the rapid pace of customer service solution development. They had to adopt AI-powered tools like GitHub Copilot and Amazon CodeWhisperer to match the increasing demand for new applications. Selecting the right GenAI vendors and ensuring scalability became critical aspects of their roles.
Impact on frontline contact center staff
But perhaps the most significant impact was felt by our frontline contact center staff. Routine tasks were automated, leading to a reduction in team size — a bittersweet reality. However, it also opened up opportunities for those who remained to take on more complex, rewarding roles that required human empathy and decision-making skills. The high turnover rates that had plagued our industry became a thing of the past, as our remaining professionals were highly skilled and focused on customer retention and growth.
Looking back, the initial uncertainty and fear that accompanied these changes were understandable. But thanks to effective change management strategies and retraining programs, we were able to navigate this transformative period successfully.
Reflecting on 2024: Lessons from the Jevons paradox
As I reflect on our journey, one lesson in particular stands out: the Jevons paradox. This paradox comes from The Coal Question, a book published in 1865 by the economist William Stanley Jevons in which he examined what happens when a technology produces a significant increase in workplace productivity. The piece of technology he examined was the Watt steam engine, which was five times more efficient than previous models. At the time, the expectation was that a more efficient engine would reduce the demand for coal — the main resource needed to produce steam. However, Jevons found that the opposite was true: the more efficient machine actually increased demand for coal. This increase was due to a proliferation of new use cases for the steam engine, thus increasing the total amount of coal consumed.
Just like the more efficient steam engines of the 1800s led to an increase in coal consumption, our boost in productivity with GenAI didn’t reduce customer demand — it increased it. Customers who previously wouldn’t bother reaching out were now able to access effortless, high-quality service, driving up interactions and creating new opportunities for us to better meet their needs.
The path of high productivity: Crawl, walk, run
The realization that GenAI actually increased customer demand was a turning point for us. We understood that we couldn’t view GenAI solely through the lens of cost savings; it was a catalyst for growth and innovation. We adopted a “crawl, walk, run” framework to progressively implement GenAI solutions based on our risk tolerance.
In the crawling phase, we deployed our GenAI assistant to produce summarized case histories when interacting with customers. It didn’t generate any new content, but rather condensed and summarized our written records to give our support team a comprehensive understanding of each customer’s history with us. This simple step allowed our agents to be more effective and minimize potential misunderstandings or hallucinations from the AI.
Once we gained confidence in the system’s capabilities, we decided to walk by having the assistant produce generic content for our customers. Because we run a travel agency, this meant implementing a GenAI assistant that could showcase detailed travel options at the click of a button. If a customer wanted to know the best warm weather locations for a three-week trip in April where they could explore historic sites, go scuba diving, and get a preview of the local dinner menus, our assistant would generate a specialized, thorough response with all the relevant options.
Finally, after carefully monitoring the system’s performance and fine-tuning it to our needs, we were ready to run by incorporating use cases that allowed for generating personalized content. This meant our GenAI assistant could proficiently handle a wide array of queries, from refunds and returns to payment issues, cancellations, disputes, and invoice inaccuracies. Customers could now process all these tasks and update their accounts directly through the chatbot, ensuring swift and effective solutions.
As we navigated this crawl-walk-run journey, the customer impacts of our AI solution evolved accordingly. In the beginning, it provided a more seamless experience by streamlining our internal processes and making them more efficient. As we progressed, it ultimately boosted customer satisfaction and loyalty by delivering new, high-quality touchpoints tailored to their specific needs and preferences.
Preparing your organization for 2027
Back in 2024 I heard NVIDIA’s CEO Jensen Huang comment on the impact AI-generated productivity would have on organizations. His words really resonated with me and encapsulate much of what came to pass for our industry. He said, “Productivity doesn’t result in us doing less. Productivity usually results in us doing more. Everything we do will be easier, but we’ll end up doing more. Because we have infinite ambition. The world has infinite ambition. If a company is more profitable, they tend to hire more people to do more.”
Huang’s message echoed the Jevons paradox from the 1800s: a boost in productivity brings more opportunities, not fewer. For contact centers like ours, “more” meant an increase in workforce, process, strategic realignment opportunities, and new customer problems to solve. I knew we had to be proactive in addressing these new issues head-on if we wanted to innovate new customer experiences and grow our market share.
Now, in 2027, I can confidently say that the contact center industry has been forever transformed. Those who proactively adopted GenAI and reimagined their operations thrived, while those who resisted fell behind. The future is here, and it’s an exciting time to be a part of this evolution.
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