AI is reshaping how brands interact with customers across the purchase journey. From chatbots handling routine inquiries to personalization engines delivering tailored recommendations, organizations are deploying AI to meet rising consumer expectations while managing operational costs. Yet adoption comes with trade-offs: Consumers remain skeptical of AI replacing human support, and many organizations struggle to connect CX investments to measurable business outcomes.

This FAQ addresses how AI fits into customer experience strategy in 2026.

What is customer experience?

Customer experience (CX) is the cumulative perception a customer forms across every interaction with a brand, from initial awareness through post-purchase support. CX encompasses both functional elements (ease of checkout, response time, product quality) and emotional factors (trust, satisfaction, sense of being valued).

Strong CX correlates with business outcomes. Three in four consumers say they will spend more with businesses that provide a great experience, and 73% will switch to a competitor after multiple bad experiences. For organizations, CX has become a competitive differentiator as products and pricing converge across categories. The challenge lies in delivering consistent experiences across channels while managing costs, a tension AI is increasingly positioned to address.

How does AI improve customer experience?

AI enhances CX by enabling faster response times, personalization at scale, and predictive capabilities that anticipate customer needs.

• Speed and availability. AI-powered chatbots handle inquiries 24/7, reducing wait times.
• Personalization. Machine learning analyzes behavior patterns to deliver relevant recommendations. Websites using AI chatbots report 23% higher conversion rates than those without, per Glassix data cited in an EMARKETER report.
• Predictive service. AI can identify at-risk customers before they churn by analyzing engagement signals, enabling proactive outreach.

Eighty percent of customer service organizations will use generative AI to improve agent productivity and CX in 2025, according to Gartner. The technology augments human agents rather than replacing them entirely, handling routine tasks while escalating complex issues.

How does AI personalization work in marketing?

AI personalization uses machine learning to analyze customer data (browsing history, purchase patterns, demographic information) and deliver tailored content, product recommendations, and messaging to individuals or micro-segments.

The process typically involves:

• Data collection. Aggregating first-party data from website interactions, transactions, and CRM systems
• Pattern recognition. Algorithms identify behaviors that predict preferences and intent
• Dynamic content delivery. Systems serve personalized emails, product recommendations, or website experiences in real time

Seventy-one percent of consumers expect personalized interactions, and 76% get frustrated when this doesn't happen, according to McKinsey. Yet execution remains a challenge: only 15% of CMOs believe their company is on the right track with personalization. The gap between consumer expectation and organizational capability creates opportunity for brands that invest in the underlying data infrastructure and talent.

How are retailers using AI to improve customer experience?

Retailers deploy AI across the customer journey, from product discovery through post-purchase support. Key applications include personalized recommendations, conversational shopping assistants, and intelligent search.

• Product recommendations. AI analyzes browsing and purchase history to surface relevant items. Over 70% of US digital retailers believe AI-driven personalization will affect their business more than any other factor, per a Bolt survey cited by EMARKETER.
• Shopping assistants. Conversational AI guides customers through product selection. Klarna's genAI-powered assistant handled the equivalent workload of 700 full-time agents in its first month.
• Visual and semantic search. AI interprets natural language queries and images to improve product discovery.

Agentic commerce represents the next frontier. OpenAI's Instant Checkout feature lets users purchase without leaving ChatGPT, potentially opening a new retail channel. McKinsey projects US B2C retail could see up to $1 trillion in orchestrated revenues from agentic commerce by 2030, though adoption may follow a slower trajectory similar to social commerce.

What are AI chatbots used for in customer service?

AI chatbots handle routine customer inquiries, freeing human agents for complex issues. Common use cases include order status, returns processing, account management, and FAQ responses.

• Volume handling. Chatbots can process thousands of simultaneous conversations, reducing queue times
• Triage and routing. AI assesses inquiry complexity and routes to appropriate channels
• Agent assistance. Real-time suggestions help human agents respond faster with relevant information

Live chat achieves 87% customer satisfaction, suggesting consumers value the immediacy. However, chatbot effectiveness depends on implementation quality. Poorly designed bots that fail to resolve issues or frustrate users seeking human assistance can damage CX rather than enhance it.

What are the pros and cons of AI in customer service?

AI in customer service offers efficiency gains but introduces trade-offs around customer sentiment and implementation complexity.

Benefits:

• Cost efficiency. Gartner predicts agentic AI will reduce operational costs by 30% by 2029 as AI resolves common issues autonomously
• Scalability. AI handles volume spikes without proportional staffing increases
• Consistency. Automated responses maintain uniform quality across interactions

Drawbacks:

• Customer resistance. Sixty-four percent of consumers would rather organizations didn't use AI for customer service, per Gartner. Sixty percent worry AI will make it harder to reach a human.
• Complex issue limitations. AI struggles with nuanced problems requiring empathy or judgment
• Implementation costs. Upfront investment in technology, integration, and training can be substantial

The data suggests a hybrid approach: AI handling routine inquiries while humans manage escalations and emotionally charged interactions.

How do you measure customer experience?

Organizations track CX through a combination of perception metrics, behavioral data, and business outcomes.

Core metrics:

• Net Promoter Score (NPS) measures likelihood to recommend. Scores above 50 indicate exceptional loyalty; retail averages 41.
• Customer Satisfaction Score (CSAT) captures satisfaction with specific interactions. Scores above 80% indicate excellent performance.
• Customer Effort Score (CES) assesses ease of resolution. Customers scoring high on effort are 4x more likely to churn.

Business linkage:

• Retention and churn rates
• Customer lifetime value (CLV)
• First-contact resolution (FCR)

A significant measurement gap persists: 70% of companies aren't linking their CX data to revenue. This disconnect makes it difficult to prove ROI on CX investments or prioritize improvement initiatives. Organizations that connect CX metrics to financial outcomes gain clearer visibility into which interventions drive value.

How should brands balance AI and human support?

Effective CX strategy in 2026 requires deploying AI where it adds value while preserving human touchpoints where they matter most.

AI handles well:

• High-volume, repetitive inquiries (order status, password resets, FAQs)
• 24/7 availability for basic support
• Data analysis and personalization at scale

Humans handle better:

• Emotionally charged situations (complaints, sensitive issues)
• Complex problem-solving requiring judgment
• Relationship-building with high-value customers

Fifty-one percent of customers are willing to use a GenAI assistant for service interactions, per Gartner, but that means nearly half prefer alternatives. Brands should design clear escalation paths from AI to human agents and avoid forcing customers through automated systems when they explicitly request human assistance. The goal is augmentation, not replacement: AI handling volume while humans deliver the differentiated experiences that build loyalty.

EMARKETER forecast data was current at publication and may have changed. EMARKETER clients have access to up-to-date forecast data. To explore EMARKETER solutions, click here.

We prepared this article with the assistance of generative AI tools and stand behind its accuracy, quality, and originality.