What Holistic CX Looks Like in the Age of AI

What Holistic CX Looks Like in the Age of AI

The complexity of customer experience (CX) has surged over the past decade. However, most enterprises continue to service their customers using the same number of agents in their contact centers. Not surprisingly, this has had an impact on the quality of CX. Recent surveys show that 25% of Americans said they’d rather shave their head than contact customer care; another 42% said they’d rather clean a toilet.

What’s going wrong? For starters, businesses offer multiple channels for customer care, including IM, social media, texting, phone, email and live chat. Unfortunately, with each channel having its own solution for managing interactions, most of these communications become siloed. Under such conditions, even the most capable human agent may struggle to understand a customer’s issue.

Cloud-based technology and smart applications can streamline these multi-touchpoint operations, but only 20% of customer care operations currently utilize these capabilities. The remaining 80% of organizations continue to deal with customer frustration and poor CSAT rates.

Let’s imagine a new paradigm for CX—one infused with AI.

The Age of AI and Its Impact on CX

The true opportunity of holistic CX in the age of AI is the ability to perform all the essential tasks that a top-notch organization performs, but in real time, including:

• Monitoring CSAT on a continuous basis.
• Pinpointing challenges experienced by individual agents.
• Identifying the qualities of top-performing agents.
• Developing remedial tools to improve challenges.
• Providing personalized coaching to guide every agent’s interactions.
• Deploying smart chatbots to guide agent interactions so they’re empowered to “own” and resolve every interaction on the same level and at the same rate as top performers.
• Identifying use cases and implementing smart automation in response to customer-care issues.

How to Ensure That AI Delivers on the CX Promise

Generative AI has advanced and can now replicate and mimic human actions and characteristics more accurately. In the CX world, it is helping organizations replicate the best agents.


For AI to optimize CX, it needs excellent building blocks, including a hefty volume of labeled CX data, and smart algorithms that can learn and recommend actions accordingly. Data scientists can glean highly relevant, actionable insights from CX data in all its forms—calls, chats, texts, emails and interactions with chatbots—leading to better interactions.

Putting AI to Work in CX Today

How will AI affect CX operations on a day-to-day basis?

Imagine a platform infused with the AI functionality described above. Now picture the manager of a call center who starts her day by examining a dashboard that displays up-to-the-minute stats on the state of CX.


What does she see? CSAT is up—great. But conversions for upgrades are down, which is disappointing, and transfer rates are up, which is concerning. How can AI help her rectify the real-time issues facing her department today?

The call center system’s AI helps her understand why CSAT is up by prompting her to look at her best-performing agent. His performance is truly stellar—and the AI is directed to analyze his interactions with customers and develop a script to help underperformers improve, boosting overall CSAT even higher.

Next, the manager looks at the lowest performers, drilling down to individual agents to assess if they have similar issues. She sees a common denominator: Agents with the lowest CSAT are failing to resolve the customer’s issue during the first interaction.

With a click of a button, she can create a self-guided coaching package based on the top performer’s interactions and send it to low-performing agents to review. A chatbot can be deployed to provide guidance to these agents as they interact with customers, so that they can improve and take better ownership of their work.


Next, she looks at the falling rate of upgrade conversions. What’s going on? Her dashboard reveals that 46% of premium upgrade contracts are incomplete or abandoned. Is that normal? She asks her AI dashboard to look at what other companies in the industry are doing in premium upgrades. Benchmarking is easy work for AI.


Right away, she sees that 75% of companies in the sector automate the upgrade process. Can her company do the same? The AI can assess her company’s processes to determine if they have the mapping to automate premium upgrades, and if they do, this can be done quickly. The customer care manager can do this analysis at her desk—no data scientists or engineers needed.

If the mapping exists, a click of a button can implement it. To ensure that the new automation is effective, the AI will monitor customer sentiment 24/7 and alert the manager of any problem.

Finally, she turns her attention to transfers. Using her dashboard, she queries the data to see the source of the problem, such as interactions that come from a specific channel (e.g., social media) and are routed to the wrong queue. This is an insight a human can glean only with many hours of effort, whereas AI can uncover it in real time, enabling immediate redress.

Here, the AI is incredibly useful, because it can detect the sentiment of the consumer, however it is expressed. It also identifies the pathways to deliver a positive outcome—in this case, a high CSAT and conversion.

If this sounds too good to be true, it is a fact that AI has the power to revolutionize customer experience, and it is already being leveraged for CX by a growing number of enterprises.