AI and Customer Experience: Why Companies Still Fail

AI: A Promise Yet to be Fulfilled

Artificial intelligence, while promising, does not always succeed in enhancing the user experience as hoped. Indeed, without the right context, it can provide responses that lack relevance, thereby degrading the experience rather than improving it.

Today, interactions with businesses, whether positive or negative, strongly influence the perceptions of consumers and employees. An ineffective customer service, an inspiring manager, a seamless online transaction, or a meeting perceived as unnecessary—all these elements shape individuals' opinions. Managing these experiences relies on understanding feelings, identifying causes, and taking appropriate action. Although AI has generated high expectations for optimizing these interactions, companies still struggle to meet the expectations of their customers and collaborators.

Understanding Without Acting: A Persistent Challenge

Companies face a growing gap between understanding the signals emitted by their customers and employees and their ability to translate that understanding into concrete actions. The signals from customers, whether regarding their satisfaction or engagement, directly influence organizational performance. However, companies often lag behind: they detect signals too late, analyze data retrospectively, and make decisions after the consequences have manifested.

Today, analytical tools allow for almost real-time understanding of situations, but that is not enough. The key lies in the ability to act at the right moment, when the experience is truly unfolding for the customer or employee. With AI accelerating decision-making cycles, this responsiveness becomes a crucial differentiating factor.

The Importance of Context in AI

Many companies see generative AI as a solution to instantly transform the customer or employee experience. However, this promise encounters a limitation: without a nuanced understanding of context, AI can produce inappropriate responses.

For example, an AI may respond to an unhappy customer without understanding the source of their frustration or their history with the brand. Standardized or disconnected responses only amplify frustration and deteriorate the experience.

Context is built from accumulated experience data over time, such as employee expectations, reasons for customer loyalty or disengagement, and weak signals of satisfaction or dissatisfaction. Each interaction, across all channels, enriches this context.

This contextual understanding allows AI to be relevant. The richer the context, the more precise and effective the AI becomes. This connection between experience signals and business outcomes relies on years of measurement, analysis, and learning through millions of interactions.

Experience as a Strategic Differentiator

In the age of AI, traditional competitive advantages are rapidly eroding. Products are copied, prices align, and processes standardize. Experience thus becomes one of the last sustainable differentiators.

For the first time, companies have the technological means to improve this experience in real-time. Experience management is no longer limited to customer satisfaction or human resources. It becomes a strategic lever influencing decision-making, system learning, and business differentiation.

AI makes this transformation possible, but only a deep understanding of context can make it truly effective.