Gen AI gives customer centricity the spotlight it deserves

Gen AI is playing a major role in enabling product companies to become more customer focused. But it’s only part of the answer.

A criticism of generative AI is that it’s mainly focused on increasing worker productivity. But, in fact, its hidden strength may be jumpstarting corporate productivity.

Using generative AI’s ability to quickly churn through vast troves of data, product companies can —finally—jumpstart their customer-centricity strategies by shifting their focus from the products customers buy, to the customers themselves.

Viewing customers as people rather than simply as users of the software, hardware or services they buy is a transition that businesses have been attempting for years. Now, by applying generative AI to the task and pairing it with organizational change, they can empower customer-facing employees with the information and cross-functional muscle they need to think and act in a customer-first way.

Why customer centricity has never been more important

Back when product companies were focused on selling licensed products, their core strength was engineering. The move to selling software as a service (SaaS) upended that sales model, but with a catch: The subscription model—and the recurring revenue it enables—requires ongoing customer engagement to support the rapid rollout of products and features.

As a result, engineering now shares top billing with customer-facing functions like to sales, marketing and, especially, customer success. Successful products require not just great features and functionality but also a standout experience.

But getting to know enterprise customers as more than just buyers of your products take a combination of data and organizational focus that most B2B product companies lack. We even hear businesses that sell sophisticated data-centric products admit to falling short on a customer focus within their own organizations. They know little about their buyers other than the software release or hardware model they use. Worse, they lack clarity on how customers use their products and even whether they’re happy with them.

Where gen AI makes a difference with customer centricity

Enter gen AI. As companies make their way up the gen AI learning curve, we see growing numbers of businesses implementing AI as a productivity partner for achieving a customer focus.

Here are three ways product companies are making the transition.

• Getting proactive about what customers need. Telecom giant T-Mobile is doubling down on customer centricity through its new partnership with OpenAI. It’s teaming with ChatGPT’s parent company to build IntentCX, an AI-driven platform that will help customer service agents understand customer intent and sentiment and offer proactive suggestions.
  
  Instead of generating “next best actions” created through traditional rules-based solutions and fixed datasets, IntentCX will be trained on billions of data points from actual customer interactions of subscribers who use its T-Life app. Because it will be integrated into T-Mobile’s operations and transaction systems, IntentCX will ultimately be able to make not only proactive recommendations but also, with permission, changes to the customer’s service.
  
  For example, when customers reach out about network issues, the system will be able to determine whether the issue is with the carrier or the user's device. For customers with older phones, the AI can offer deals on new devices and, if given permission, complete the purchase. T-Mobile hopes to launch IntentCX in 2025.
  
  
• Initiating personalized customer outreach. We know of a billion-dollar software company that’s training its private large language model (LLM) on extensive internal data and then prompting it to generate offers to website visitors based on their browsing interests and price points.
  
  Yet the pilot effort isn’t just a modern spin on cold calling. Company executives emphasize that a key aspect of the pilot’s success is oversight of the LLM’s missives by members of the company’s extensive dealer network, who remain in the loop before messages are sent.
  
  The results are promising: The company says it is seeing a meaningful uptick in response rates among prospects and customers that have received an LLM-generated message.
  
  
• Making an offer at just the right moment. To get a unified view of its customers, a communications company is partnering with our team on a digital twin strategy to capture and integrate customer data from its broad-based lines of business, products, partners and channels. Gen AI then analyzes the data to create hyper-personalized customer communications.
  
  The results have been eye-opening. Based on the data analysis, the company has changed its cross-sell and upsell strategies. Customer service agents now promote products and services and drive customer interventions at the moment the data shows is optimal for each customer.
  
  Although still in its early stages, the digital twin pilot has yielded promising results for revenue generation and customer retention, and an exponential increase in customer satisfaction.

Product development, meet customer success

Gen AI doesn’t enable customer centricity on its own—organizational change is also needed.

Because customer service agents are on the front lines with customers—and take direct fire on what is and isn’t working—they’re a gold mine of product information. Yet too many businesses erect walls between the product design and engineering organization and the customer service function. Customer service leaders tell us they’re often left out of the product roadmap process and have no voice in product design—and are left to scramble to form support teams when products are released.

To become fully customer centric, businesses need a bi-directional, closed-loop operating model where customer success and service is tightly coupled with the product organization. And gen AI, particularly LLMs, offer a tremendous opportunity to extract valuable insights that can be shared with product and customer functions. This style of human-first AI adoption is important to its success.

The key here is to encourage collaboration and incentivize people to work together. By doing so, more companies can become invested in not just what the competition is doing but also what their own customer service agents are telling them.

For example, as we scaled up the engineering and customer support for a leading marketing brand, they challenged us to make customer support integral to the product roadmap by identifying features to add to the backlog. In response, we created a synergistic approach, forming a cross-functional team that brought together customer service agents with data and AI experts as well as product managers.

Make no doubt about it: The shift to customer centricity requires a lot of data and effort. Yet the payoff is a business primed for success.