All the buzz about AI has generated many questions within organizations that deliver customer service through contact centers.
Putting aside the hype, it’s evident that AI has the potential to do all of the above, shaping a future where the customer experience becomes more personalized and efficient.
When a customer reaches out for support through any channel, they expect fast, reliable answers and speedy resolutions, regardless of the volume of issues the company handles. And they expect you to “know” them, which means understanding their behavior patterns and their purchase and service history. They have little tolerance for brands that don’t leverage technology to eliminate service friction.
The rise in AI is enabling organizations to meet increasingly demanding service expectations with high reliability at scale. By training large language models (LLMs) appropriately, leading organizations and their technology partners are using AI to transform the customer service experience in two critical ways: improving self-service and better equipping agents to address service issues. And that’s translating to bottom-line benefits—including improved customer satisfaction, greater efficiency, and lower costs.
In an omnichannel world—where customers are likely to interact with companies via email, chat, and phone—AI can provide a single, unified process and holistic data view, no matter which channels the customer uses. Leveraging the rich data within a customer relationship management (CRM) system, well-trained LLMs can analyze huge data sets to assess the customer’s profile, and then curate and deliver a personalized response to their inquiry.
Even the most experienced contact center agent can’t match the speed and accuracy of an AI-powered service process. And that opens the door to moving more transactions to a self-service mode without degrading the experience, freeing agents to handle more complex issues. For inquiries that need a human touch, an AI-powered process eliminates long wait times while the agent manually pours through customer data. Instead, the agent can receive a curated, AI-generated summary to guide them through the call effectively and efficiently. By understanding the situation faster AND getting accurate, curated information, the agent provides a more satisfying service experience. It makes for happier, more productive employees and happier customers.
Generative AI also can reduce contact center costs by opening the door to scaling back staffing or redeploying employees to more complicated work. It will become more affordable to run a 24/7 service operation by using AI-powered chatbots as an option during normal business hours or as the exclusive service channel during off-hours. For contact centers that handle hundreds or thousands of inquiries daily, just automating 10-20 percent of the volume can significantly impact the bottom line.
Given these significant benefits, it comes as no surprise that a Gartner survey found 69% of customer service and support leaders plan to make new investments in conversational AI, and 48% believe customer-facing AI-powered virtual assistants can have a high impact on the customer service function.
AI won’t replace human interaction in the contact center, but it will let machines do what they do best, freeing humans to do what we’re particularly good at.
Predictive analytics are a prime example: AI will be able to predict service issues based on an analysis of huge data sets, and then contact customers proactively. If the data shows you’re experiencing shipping time variability, you could alert customers to possible delays. AI not only makes this complicated task feasible; it frees finite human resources to focus on more complex tasks while improving the service experience.
Multilingual support is another great application. Think about a company that sells to customers globally, all speaking various languages. Today, providing service by phone might require staffing the contact center with multilingual employees or transferring the call to someone who speaks a particular language. Tomorrow, any agent will be able to converse with any customer. For example, the technology could translate a Spanish-speaking customer’s words into the agent’s native English, then translate the agent’s response into Spanish. There’s no friction in the experience, and language skills are no longer a limitation in hiring.
Turning concept into reality will require a pragmatic approach that preserves brand equity and ensures data privacy—two big fears about AI-powered contact center service.
LLMs will need to be trained to understand and represent the brand, essentially speaking the brand’s language and portraying its personality. Apple and Nike would never present their brands the same way in marketing, so their generative AI implementations could never be generic. Getting it right means taking the time to validate, test, and adjust before rolling out applications full scale—potentially using AI to assist agents before implementing it for self-service.
The same measured approach will be critical to protecting customer data, with AI-powered contact center applications carefully architected with tight controls. For example, deploying LLMs in the cloud offers many advantages, but the data needs to be segmented from other companies’ information. A thoughtful approach will also minimize the impact on the contact center workforce, ensuring the company’s human resources are deployed effectively in an AI-powered environment.
The idea of going slow might seem counterintuitive to the warp speed of AI, but jumping in too fast with both feet can backfire. A technology partner that’s well-versed in CRM functionality and implementations understands how generative AI can transform service, and knows how to integrate AI into the contact center environment will be the best guide.
I predict we’re on the cusp of a huge wave of AI-powered contact center applications, with AI completely altering how we view customer service and how customers interact with companies and brands. Very soon, adopting AI to enhance the service experience won’t be a differentiator; it will be the only way to survive and thrive.