For many businesses, a shift is taking place in how they are using artificial intelligence (AI) to grow. Recent surveys found enterprises are doubling down and increasing their own investments in more advanced AI and data technologies and are seeing increased returns on AI investments. A few catalysts are lighting the way for this perspective shift, according to our eBook “AI in the Enterprise.” One prominent shift is how large companies are harnessing AI to facilitate personalized, empathetic conversations and recommendations with their customers.
While it may seem improbable that a large business with millions of customers is able to connect personally with each customer, AI plays a major role in making the improbable a reality. And when done correctly, the end result is a more loyal customer with a higher lifetime value. But capitalizing on this AI capability is easier said than done. It takes intentional development, proper governance, and the flexibility to grow and adapt to customer needs. Let’s explore how businesses are finding success with customer relationships using AI.
Enterprises that leverage AI-based technologies are seeing solid returns – and gaining on their competition.
A new paradigm in customer relationship building: hyper-personalization
Over the years, and especially after the pandemic, businesses are finding more success when they foster personalized relationships with customers. And much of that effort is reflected in strategic goals – think company initiatives like “become more customer-centric” or “get closer to our customers.” Across industries, customers have higher expectations of the companies they do business with, compared to 20 years ago. They expect faster service, more recognition for their brand loyalty, and more exceptional customer service for their purchases. This has shifted the paradigm from a service-centric approach to a relationship-centric approach.
For many businesses, early investment in AI has provided a head start over their competitors in creating relationships with customers. AI helps them navigate the mountains of data they’ve collected to form personalized customer experiences. And across enterprises and business users – from marketing directors to chief data officers – AI offers a key element to operational success.
But what does hyper-personalization look like? Depending on the touch point and the customer, hyper-personalization can look like recommending financial assistance to customers that have experienced a natural disaster. Or perhaps when a customer calls their insurance provider, the customers service representative is already prepared for their request and knows whether the company needs to do further evaluation before processing the claim. Whatever the customer’s circumstance, AI can help companies know more about their customers so they can offer the right solution or promotion. The end result is a happier customer and more efficient and effective operational processes.
Imagining a responsible future with AI
Of course, these AI-based decisions don’t exist in a vacuum. To achieve success, companies have to consider how their data is interpreted, how they’re connected with enterprise systems, and how customer needs are interpreted through data.
In an already-tight IT labor market, AI can help companies bridge the gap in staffing issues – like offering chatbots for quick fixes, instead of routing all customer concerns to a representative – and identifying other inefficiencies, like resource roadblocks or workflow bottlenecks. And for some companies, AI works as a co-pilot to customer agents, offering smart responses based on customer context and historic success. AI works as an intermediary to drive more efficient customer and business solutions. As our eBook highlights:
“A significant benefit of AI is that it relieves people of mundane and time-consuming tasks, so they can focus on more important, impactful, or high-value work.”
However, AI doesn’t always have a positive connotation in the public eye, so proper governance and guardrails with how businesses collect and use data also matters. Businesses have to walk a fine line between hyper-personalization, avoiding bias creep, and falling into breaches of privacy. But with more laws being created around data collection and management, enterprises have a more level playing field for AI use. Businesses with specialized processes and that are designed to comply with government regulations will be rewarded for their efforts.
Learning from leaders in AI decisioning
So which companies are leading the way in building customer relationships with AI? Our eBook found organizations in the banking, insurance, and telecommunications industries are already set up for success, as those businesses often revolve around existing customer connections:
“Organizations that have an existing relationship or connection with their customers have an advantage. Organizations in these industries can build value by using AI to get closer to their customers, engage them one-to-one with empathic and personalized recommendations, anticipate and proactively resolve customer service issues, and build better customer experiences.”
Beyond the eBook, we’ve observed similar customer success stories within those industries in recent years. For telecommunications giant Verizon, AI capabilities are powering “Next Best Action” decisioning for consumers by using customer context and situational relevancy to extend offers to customers in different channels. The result: more timely, relevant offers and more engagement.
In the insurance realm, Aflac is using AI-powered email bot technology to automate over 3,000 emails a week. This type of smart automation frees agents from manual processes and improves response times, so Aflac can deliver fast, accurate, personalized customer experiences. Since implementation, Aflac is processing and responding to 30% of incoming customer inquiries without the need of an agent at all and is seeing faster responses to the other 70% of emails. Their AI technology is generating personalized suggestions for agents, specific to each customer request.