Improve Customer Experience with AI

When we talk about the future of Customer Experience (CX) with AI, we inevitably refer to the need for agent humanisation, cognitive perception, personality, and empathy in order to build trust in these solutions, ultimately driving customer adoption and satisfaction.

According to McKinsey (2025), CX will increasingly become “agent-first”, but with a well-designed human-in-the-loop approach, evolving from script-based FAQ-style chatbots to automated AI agents capable of resolving tasks. The factors that will drive better agent performance include effective governance, maintaining human validation where it truly matters, and strong adoption practices.

Real Implications for Customer Experience (CX)

Customers adopt automation when they see it reduces effort or solves a problem, but they quickly lose trust if there is a lack of transparency, frequent errors, or no option to escalate to a human. Therefore, it is crucial to design AI with these aspects in mind; otherwise, adoption and meaningful benefits will not be achieved.

Operational Efficiency as a Driver of Innovation and Growth

In November 2025, McKinsey published “State of AI in 2025: Agents, Innovation and Transformation”, highlighting that most organisations are still in experimental or pilot phases. Around two-thirds reported that AI had not yet been scaled across the organisation, remaining in proof-of-concept or isolated initiatives.

Even so, these initiatives have shown positive impact, particularly in cost reduction and revenue generation, with around 64% stating that AI is driving innovation.

For approximately 80% of companies, operational efficiency is the main driver of these initiatives, combining efficiency with clear innovation and growth objectives, going beyond simple cost reduction.

There is also strong market interest in agent-based projects, with around 62% of organisations already experimenting with agents, indicating high curiosity and strong expectations for future adoption.

Technology, media, telecommunications, and healthcare are the sectors with the highest adoption levels.

Among companies with revenues above $50 billion, around half have reached the scaling phase of AI initiatives. In contrast, only 29% of companies with revenues below $100 million report having AI projects in expansion. These organisations are already allocating more than 20% of their annual budgets to AI initiatives.

Companies deriving the most value from AI are agile organisations with well-defined delivery processes, robust and scalable data and technology infrastructures, strong talent management policies, and practical use cases applied across business processes, supported by continuous KPI measurement and monitoring.

Recent studies on AI advancements point to a future where teams will consist of both humans and intelligent machines, whether robots or agents. However, for these Human-AI Teams (HATs) to function successfully, people must be receptive to this model and capable of integrating AI into their processes and systems.

Human Perception and the Sociocognitive Process as Drivers of AI Adoption

Recent research on chatbots and AI agents shows that consumers often hold sceptical attitudes, resulting in lower interaction and inconsistent usage intentions. For this reason, understanding what drives engagement and adoption is critical.

To improve human–agent interaction, IT teams are increasingly collaborating with psychologists and sociologists to understand how human perception and sociocognitive processes—through which people form impressions of others—influence relationships with AI agents.

Some studies show that empathetic agents are perceived as more approachable and competent, positively influencing satisfaction and encouraging adoption and recommendation.

Warmth and competence have been identified as key mediating factors, indicating that consumers attribute human qualities to interactions with agents, enhancing their service experience.

One study also examines how three social attributes of chatbots / AI agents (perceived warmth, perceived competence, and social presence) affect consumer trust and intention to use them. The findings show that all three positively influence trust, continued usage, and satisfaction.

“Implicit Theory”, which refers to beliefs about the malleability of human traits such as intelligence and personality, can influence cognitive processing and behavioural decision-making.

Perceived warmth is associated with kindness, care, and understanding. When agents display warm behaviours, users are more likely to build trust and engage more deeply. This can be expressed through linguistic style and emotional sensitivity, making personalisation and adaptability to customer personality and communication style essential.

Perceived competence is built through the agent’s ability to respond to challenges, solve problems, and understand user needs. Positive interactions reinforce perceptions of intelligence, knowledge, skills, and efficiency, significantly increasing the likelihood of adoption and recommendation. This was identified as the most important factor in the study “The Effect of Perceived Warmth, Competence, and Social Presence of AI-Driven Chatbots on Consumers’ Engagement and Satisfaction”.

Social presence refers to the perception of agents as social entities. When agents behave in a human-like manner, particularly in communication, users see them as social partners, leading to more natural, pleasant, and accepted interactions.

Other early studies suggest that people do perceive AI agents as social actors rather than just technological tools, although this remains an emerging area of research.

Enhancing the Customer Journey Through AI Agents

Agents are increasingly acting as service providers, becoming a key interface between companies and consumers. As such, they should be designed and personalised according to customer personality types to better meet expectations.

AI advancements have transformed customer service across industries, with AI agents playing an increasingly critical role in customer experience.

Recent research also explores how AI-driven interactions impact customer loyalty and retention, analysing variables such as satisfaction, personalisation, responsiveness, and perceived trust.

The findings show that well-designed AI agent systems not only improve operational efficiency but also foster stronger emotional connections, increasing customer loyalty and reducing churn. When interactions are reliable, personalised, and efficient, AI agents help build long-term relationships.

Engagement, decency, and a clear focus on helping and solving problems were identified as key personality traits, as well as minimum requirements for trust and satisfaction. An adaptable personality further strengthens trust in the agent.

Companies such as Sephora, H&M, Pizza Hut, and Mitsubishi Motors are examples of successful AI agent implementations that personalise customer experience, significantly improving service quality, satisfaction, and retention.

The customer journey is made up of multiple touchpoints, and any friction or confusion can lead to frustration and abandonment. AI agents help smooth or eliminate these friction points by providing instant support—whether answering questions, guiding customers, offering suggestions, sending reminders, or encouraging purchase completion.

When executed successfully, this leads to improved customer experience, increased conversions, reduced cart abandonment, and higher satisfaction.

Customer Experience (CX) with AI requires well-planned integration between persona, context, and user control.

Ana Candeias

Data & AI Director @ Mind Source

Published in Marketeer