Q & A

Why is a formal, scientific approach to experience management necessary now that AI is beginning to assume responsibility for the tactical execution of customer support? Whether applied to customer support or another function, an understanding of the core competencies of experience management provides the foundation for selecting, developing, and executing appropriate AI strategies. This applies whether AI is used to collect and analyse data, provide information in a customer support setting, or create avatars that interact and communicate with customers. More than 90% of AI initiatives in customer support fail to produce a return on investment.

Why is that? Is it because the tools are flawed? More often, the problem is inadequate data or data that has not been properly integrated across the organisation.

The motivation may also be misguided. An organisation may adopt AI because everyone else is doing so or because leaders expect it to reduce costs. What is often missing is a more fundamental question: How will AI help us perform our core competencies more effectively and improve the quality of the experiences we provide to customers?

How will it strengthen our ability to understand customer needs? How will it improve the employee experience? How will it equip employees with the information they need to interact effectively with customers?

The problem stems from an incomplete understanding of the principles and frameworks required to adopt experience management as an operating philosophy and deliver it effectively. AI should not be viewed merely as a cost-saving measure or a way to create efficiencies. It should be treated as a set of tools that enables an organisation to perform its work more effectively.

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AI succeeds when it strengthens an organisation’s core experience management capabilities rather than simply automating tasks. That starts with trusted, connected enterprise knowledge and a clear strategy for using AI to improve customer and employee experiences, not just reduce costs. Without a strong knowledge foundation, even the most advanced AI initiatives can struggle to deliver meaningful outcomes. Organisations must first ensure that the information powering AI is accurate, accessible, and aligned with their experience goals.

SearchUnify supports this approach through SearchUnifyFRAG™ (Federated Retrieval-Augmented Generation), which grounds AI responses in trusted enterprise knowledge. By delivering accurate, context-aware assistance, it helps organisations improve decision-making, enhance customer and employee experiences, and realise measurable value from their AI investments.

I argue that consumers make decisions based on a desired emotional state. In fact, at least 90% of consumer decisions are driven by emotion. When we choose an experience, we seek more than functional performance, such as the quality of food in a restaurant or being greeted promptly. We also have a desired emotional state in mind. We want to move from our present state to a preferred state, and we tend to describe that desired state in emotional terms.

How we reach that state is largely a function of the environment with which we engage and the people within it. Lou Carbone divides an experience into three categories of clues. The first is functional clues, which are often process-driven and help explain the functional outcomes of an experience.

The second category is mechanic clues, which encompass sensory inputs such as the sights, sounds, tastes, and other elements we encounter in an environment. The third category is humanic clues.

Humanic clues are traditionally created by people. Increasingly, however, when customers contact customer support, AI agents are taking on that role. The question is whether those AI agents can be designed to demonstrate empathy. I believe they can.

With the appropriate quality and quantity of data, AI agents may even be capable of demonstrating empathy more consistently in some situations. Human beings, after all, are not perfect.

Although organisations can establish standards and train employees to anticipate and respond to customer needs, individual performance will inevitably vary. Employees can certainly be empathetic, but a properly designed AI agent can be trained to anticipate a vast range of situations and coached to respond appropriately.

Customers also do not always need to believe they are speaking with a person. I sometimes find that Claude, for example, responds with more empathy and understanding than some of my human friends. There is therefore a legitimate role for empathetic AI agents that can deliver high-quality humanic clues consistently and help customers achieve their desired emotional states.

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As Tom explains, great customer experiences are shaped not only by what organisations do, but by how they make customers feel. Empathy begins with understanding a customer’s context, intent, and desired emotional outcome. As AI takes on more customer interactions, it must be designed to recognise those signals and respond in ways that feel helpful, personalised, and appropriately human.

SearchUnify supports this approach through Agentic RAG and AI Support Agents that ground every response in trusted enterprise knowledge and customer context, while seamlessly handing emotionally sensitive or complex conversations to human experts when empathy requires a human touch.

Experience management today is largely a product of how empowered consumers have become through their access to information and their ability to use it in decision-making. Customers can search for products, read reviews, and use AI to form opinions based on vast amounts of information. They can then find and purchase products digitally. The traditional business model, in which a company creates a product and Marketing develops a plan to sell it, is no longer sufficient.

Consumers often value reviews and the opinions of others more than company advertising, and they can use readily available information to make well-informed decisions. In response, organisations need to share information openly and be as transparent as possible about their operations.

They must provide transparent, high-quality information that reassures business partners that they have chosen the right relationship and that their trust is well placed. People can access a tremendous amount of information about an organisation online, not only from customers but also from employees.

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Tom highlights a fundamental shift in customer expectations: empowered customers no longer rely solely on brand messaging; they expect transparent, trustworthy information that helps them evaluate options and make confident decisions. In an information-rich world, trust is built by providing accurate, contextual knowledge that reassures customers and strengthens long-term relationships.

SearchUnify helps organisations deliver that transparency through SearchUnifyGPT™, which provides grounded, context-aware answers from trusted enterprise knowledge. By enabling customers, partners, and employees to access reliable information across every touchpoint, organisations can build the confidence and credibility that Tom identifies as essential in today’s experience-driven economy.

In today’s environment, CX roles are often eliminated because practitioners cannot demonstrate how experience management affects the bottom line. The C-suite speaks the language of finance, while CX professionals often rely on measures of experience delivery, such as NPS, customer satisfaction, and customer effort. Although CX practitioners may understand the connection between those measures and financial performance, the argument is not always easy to make to senior leaders who expect more immediate returns. The long-term success of experience management depends on practitioners being able to think and operate in financial terms. They must become financially literate.

This means being able to read a profit-and-loss statement or an annual report and clearly understand how an individual’s work and a department’s activities affect the bottom line. It also means demonstrating how customer experience, employee experience, and experience management overall can both drive revenue and reduce costs.

Measures such as NPS and customer satisfaction can serve as useful internal language, but practitioners must understand how improvements in customer satisfaction translate into increased revenue in both the short and long term. They must also understand its relationship to loyalty and the financial value that customer loyalty creates for the firm.

These relationships should be understood and discussed routinely so that, when CX professionals work with other parts of the organisation, they can speak in terms of both operational KPIs and financial outcomes.

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The future of customer experience depends on demonstrating business value in financial terms. While metrics such as customer satisfaction and resolution times remain important, they matter most to the C-suite when they can be linked to revenue growth, cost reduction, customer loyalty, and long-term profitability.

SearchUnify helps organisations make that connection through SearchUnify Analytics, which translates operational performance into measurable business outcomes. This impact is reflected in customers like Accela achieving a 92% reduction in first response time, helping strengthen the business case for customer experience as a driver of financial performance.