“I am excited to introduce our next step in this journey,the Webex AI agent. This is in pilot right now, it will be available to our customers in an early calendar quarter in 2025. This AI agent brings together conversational intelligence and generative AI to deliver natural conversations with hyper-personalization.”
Those words came from Cisco, the enterprise digital communications and technology company; specifically from Anurag Dhingra, SVP and GM of Cisco Collaboration. The words sounded strangely familiar. In the same month of October, we had heard about similar initiatives from Oracle, which also primarily serves the enterprise, as well as from Zendesk which serves mid-size to enterprise customers and has a narrower offering very much focused on the customer experience.
Something was in the air. On the one hand, the aim of automating the customer experience (including automating the work of service reps) seemed very ambitious. At the same time, if everyone starts doing it, it will soon be table stakes.
But is it a realistic aim? And is customer experience really reducible to the service (or support or success) experience? We spoke to an expert, Isabelle Zdatny, head of thought leadership at the Qualtrics XM Institute.
Defining experience management
First, its useful to clarify the relationship between Qualtrics, an experience management software vendor, and the Institute. The first thing to know is that the Institute is product-agnostic. “Our role is like a think tank inside Qualtrics,” said Zdatny. “We are focused on CX and EX [employee experience] professionals and providing them with the insights, practices and principles they need to be successful in their role — to grow their personal skills as well as help to build an effective and sustainable experience management program. Unlike other internal experts at Qualtrics, we’re less client-focused and more focused on category building — what is experience management and what do people need to know to do it well?”
The XM Institute was formed in 2018 when Qualtrics acquired Temkin Group, a boutique consulting firm founded by Bruce Temkin, who Zdatny calls “the godfather of customer experience.” She had been with Temkin Group since 2013.
Zendesk’s intention of providing AI agents across all channels, working either autonomously or in harness with humans to field customer queries, is an approach to what it calls “customer experience management.” Certainly, Zendesk believes that customer experience “refers to all the interactions between a business and its customers,” but its explanation of its new capabilities always seems to circle back to the call center or to digital customer service channels; the same could be said about the announcements from Oracle and Cisco. Isn’t that perspective narrow?
“It absolutely is,” said Zdatny, “because that’s just reactive fire fighting. Experience management is about more than responding to customer complaints. You have limited resources as an organization. There are probably thousands of problems you could go fix. Experience management is going to help you figure out which ones to focus on, where you should be allocating attention and resources. And it’s not just about fixing what’s broken,” she emphasized. “It’s about how we are delivering the emotionally engaging, innovative experiences that will help us to stand out in a crowded market.”
How does Zdatny think about customer (and employee) experience? She had two definitions, first a “scholarly” one used internally at the Institute; the second, one used in conversation with the C-suite. First: “The discipline of driving actions using an ongoing flow of insights into how customers and employees are thinking, feeling and behaving. It is a systematic business practice, not a set of isolated activities.”
Second, and more simply: “Understanding and optimizing the experiences of customers and employees.”
How should experience management evolve within organizations? “What we see in early stage organizations is a fragmented approach,” said Zdatny. “Product, sales. “What makes for a good program is a centralized group that is able to consolidate and coordinate experiences across the entire organization; you call a contact center or walk into a store, you are having the same type of experience.” For very small companies, she said, centralizing responsibility for experience in one person can work just fine.
Key to optimizing the customer experience, as Qualtrics has long emphasized, is gathering feedback. That isn’t necessarily straightforward. “Early stage CX programs focus really heavily on feedback. Unfortunately, they don’t take a lot of action based on that feedback. They’re collecting a lot of insights but not using those insights to make changes,” explain Zdatny. “An effective CX team has that data and analytics but also other supporting functions like experience design and change management. Feedback is foundational but you can’t have an effective experience program if the insights are just being passed over the wall for other teams to deal with.”
Dig deeper: Zendesk saturates CX with AI and voice
The road to fully the automated experience
Perhaps the simple solution to centrally organizing and continually optimized CX, whether in the broad sense or the narrower sense of support and service, solves for a lot of these challenges. That’s a view enthusiastically advanced by Jeff Wartgow, VP product management, Oracle CX service. But he agrees it’s a matter of advancing along a continuum.
First comes improving service by switching from traditional chatbots to conversational AI. Second, improving the performance of human service reps by providing them with AI assistance (or, indeed, assistants). Third and fourth: improving execution by automating service processes and transforming service with automated execution plans. I asked Wartgow to distinguish between the latter two.
The third stage is: “I know how to fix this, I’m going to automate how we fix it.” The fourth: “What if I don’t know how to fix it, can I automate the planning for how to fix it?” In other words, it’s the difference between using AI to automate a known response to a service issue and using AI to figure out the response to a service issue.”
One challenge businesses will face in pursuing this path is that it will need to have its knowledge base in order for the AI agents to be successfully trained. Two years ago, said Wartgow, Oracle completely rebuilt the knowledge base in Oracle Fusion Cloud. “Say there are 15 service requests and we fixed them all the same way. Shall we just turn that into a knowledge article? You just press a button and genAI will write the article and put it in the knowledge database.”
Wartgow agrees that the knowledge base will also need to be constantly refreshed and says that Oracle has a mechanism to “put fresh water in the fish tank.” Oracle’s knowledge base can also ingest large quantities of legacy knowledge, even hidden in large manuals, and create knowledge articles tuned to specific tasks. “We had to do all this first before we could even start talking about these agents,” he said. Oracle will encourage clients to use the Oracle knowledge base rather than some internal alternative as the main source of truth for Oracle service agents.
Dig deeper: Oracle aims to automate the complete customer service lifecycle
The holistic experience
When asked, Oracle, like Zendesk, will agree that the customer experience is not reducible to the service experience. “I’m the service guy at Oracle,” said Wartgow, “so I talk a lot about service. But 70% of the interactions a customer will have, whether it be B2C or B2B, will be with the service department. But I don’t feel like I am talking to a brand’s service center, I feel like I’m talking to the brand. I should be able to change from a sales, to a service, to a marketing conversation as fluidly as possible.”
Oracle and Zendesk, then, have blueprints for an almost entirely automated future, at least for the service part of customer experience. How will that sit with a world in which customer experience is more holistic than that? “Consumer concern about having a human to connect to is the only concern that went up over the last year and it was over 50% that were concerned,” said Zdatny, referring to XM Institute research (registration required).
Indeed, she points out that there are regulations in Europe that say you have to make it easy to reach a human. “I understand from the company’s perspective it’s more efficient if you’re deflecting calls away from high cost call centers. Right now, consumers are saying pretty clearly that’s not what they want.”
But Zdatny agrees that it’s hard to say what consumers (or B2B clients) will want three years from now, if the AI agents get really good at their jobs. “Over the long term, that is the direction we’re moving. In the short term, I think a lot of companies are out over their skis,” she said.
The post Customer experience management in the age of agentic AI appeared first on MarTech.
**Managing Customer Experience in the Era of Autonomous AI**
In the rapidly evolving digital landscape, businesses are constantly seeking innovative ways to enhance customer experience (CX). The advent of autonomous artificial intelligence (AI) has ushered in a new era of possibilities, transforming how companies interact with customers, anticipate their needs, and deliver personalized solutions. However, managing customer experience in this era requires a delicate balance between leveraging AI’s capabilities and maintaining the human touch that fosters trust and loyalty. This article explores the opportunities and challenges of managing CX in the age of autonomous AI, along with strategies to succeed in this dynamic environment.
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### **The Rise of Autonomous AI in Customer Experience**
Autonomous AI refers to systems that can operate independently, make decisions, and continuously learn from data without human intervention. Unlike traditional AI, which requires explicit programming and oversight, autonomous AI leverages advanced machine learning algorithms, natural language processing (NLP), and neural networks to adapt and evolve in real-time.
In the realm of customer experience, autonomous AI is revolutionizing how businesses operate. From chatbots that provide instant customer support to recommendation engines that tailor product suggestions, AI is enabling companies to deliver hyper-personalized experiences at scale. Moreover, autonomous AI can analyze vast amounts of customer data to uncover insights, predict behaviors, and optimize interactions across multiple touchpoints.
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### **Opportunities in CX with Autonomous AI**
1. **24/7 Availability and Instant Support**
Autonomous AI-powered chatbots and virtual assistants are redefining customer service by offering instant, round-the-clock support. These tools can handle routine inquiries, troubleshoot issues, and even escalate complex problems to human agents when necessary, ensuring seamless service delivery.
2. **Personalization at Scale**
AI algorithms can analyze customer preferences, purchase history, and browsing behavior to deliver highly personalized recommendations. This level of customization enhances customer satisfaction and drives loyalty, as customers feel understood and valued.
3. **Proactive Engagement**
Autonomous AI can predict customer needs before they arise. For example, predictive analytics can identify when a customer might need a product refill or when they are likely to churn, enabling businesses to take proactive measures to retain them.
4. **Streamlined Operations**
By automating repetitive tasks and optimizing workflows, autonomous AI allows customer service teams to focus on high-value interactions. This not only improves efficiency but also enhances the overall quality of service.
5. **Enhanced Data Insights**
AI-powered analytics tools can process vast amounts of customer data to uncover trends, preferences, and pain points. These insights empower businesses to make data-driven decisions and refine their CX strategies.
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### **Challenges in Managing CX with Autonomous AI**
While the benefits of autonomous AI are undeniable, its integration into customer experience management comes with its own set of challenges:
1. **Maintaining the Human Touch**
Over-reliance on AI can lead to impersonal interactions, which may alienate customers. Striking the right balance between automation and human engagement is crucial to building trust and emotional connections.
2. **Data Privacy and Security**
Autonomous AI relies heavily on customer data to function effectively. Ensuring the privacy and security of this data is paramount, as any breach can erode customer trust and damage a company’s reputation.
3. **Bias and Fairness**
AI systems are only as unbiased as the data they are trained on. If the training data contains biases, the AI may perpetuate or even amplify them, leading to unfair treatment of certain customer segments.
4. **Complexity of Implementation**
Deploying autonomous AI solutions requires significant investment in technology, infrastructure, and talent. Additionally, businesses must ensure that these systems integrate seamlessly with existing processes and platforms.
5. **Customer Resistance to AI**
Some customers may be hesitant to interact with AI-driven systems, preferring human assistance instead. Businesses must address these concerns and provide options for customers who prefer traditional support channels.
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### **Strategies for Managing CX in the Era of Autonomous AI**
To harness the full potential of autonomous AI while addressing its challenges, businesses should adopt the following strategies:
1. **Adopt a Hybrid Approach**
Combine the efficiency of AI with the empathy of human agents. For example, use AI to handle routine tasks and free up human agents to focus on complex or emotionally sensitive interactions.
2. **Prioritize Transparency**
Be transparent about the use of AI in customer interactions. Clearly communicate when customers are interacting with an AI system and provide easy access to human support when needed.
3. **Invest in Ethical AI**
Ensure that AI systems are designed and trained with fairness and inclusivity in mind. Regularly audit AI algorithms to identify and mitigate biases, and involve diverse teams in the development process.
4. **Focus on Data Security**
Implement robust data protection measures to safeguard customer information. Comply with data privacy regulations such as GDPR and CCPA, and be proactive in addressing customer concerns about
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