How Businesses Use Agentic AI to Deliver Better Customer Experiences

Put simply, Agentic AI is a type of artificial intelligence empowered with autonomy to take action. It perceives its environment, makes context-aware decisions, and executes multi-step tasks across different systems, all on behalf of customer data to achieve defined outcomes. 

Think of it as the difference between a helpful airport desk authority and a dedicated personal assistant. The authority (traditional AI) can find you with information, but the personal assistant does not just tell you about the flight; they book it, help you arrange transport, and add it to your calendar, based on your known preferences. 

This shift from reactive to proactive problem-solving is why businesses are adopting Agentic AI to build different levels of customer loyalty and efficiency. 

Agentic AI use cases in customer experience

AI in banking and financial services customer experiences

In the financial sector, trust is built on simplicity and security. But processes like loan applications or fraud alerts feel complex and slow. Agentic AI changes this by acting as a proactive financial partner that handles the complexity, giving customers clarity and control. 

(a) Your intelligent financial advisor (Eliminate tedious loan application process for customers)

Agentic AI transforms complex, long-form processes like loan applications into simple, guided conversations. The AI acts as an advisor and, by using the existing customer data to pre-fill information, runs checks, and provides instant pre-approved offers. 

Real-world example (Zota): Zota uses Salesforce Agentforce to power its financial services. They build an AI agent that analyzes a client’s complete financial profile to proactively surface personalized investment opportunities and alerts. Instead of waiting for the client to ask, the AI continuously monitors their portfolio and market conditions to automatically recommend actions that align with their financial goals.

Agentic AI in insurance customer experience   

Filing a claim is often the moment of truth for an insurance customer, and it is often filled with uncertainty and paperwork. Agentic AI redefines this experience by managing the entire process from start to finish, assisting when customers need the most. 

(a) An end-to-end claims concierge (eliminating the confusing claim process)

Here, the Agentic AI not only creates a ticket but also owns the entire service journey. It guides the customer, uses data to assess their needs, and orchestrates a seamless resolution.

Real-world example (UChicago Medicine): UChicago Medicine deployed an AI agent that autonomously orchestrates a patient’s entire care journey. When a patient is diagnosed, the AI schedules the appointment. In addition, it proactively books all necessary follow-ups, coordinates between care departments, and triggers reminders for medication and pre-appointment instructions, managing the entire process.

Agentic AI in retail customer experience 

The modern retail journey is not straightforward; customers do not follow a straight path to purchase. They bounce between your website, social media, and your physical store, expecting every interaction to be connected and seamless. Here, Agentic AI sees a single customer story, and it acts on that in real-time to guide and assist. 

(a) Proactive journey management (Eliminating “Where my order” loop)

This is where AI anticipates problems in the customer journey before they become complaints. Instead of waiting for the customer to report an issue, the system muonitors internal data (like logistics) to identify potential delays and takes corrective action automatically.  

Real-world example (Amazon): Amazon’s logistics and customer service utilized this approach. Their AI-powered systems not only track packages but also proactively detect shipping delays and automatically trigger actions, such as sending updated delivery timelines. In many cases, they provide a discount offer on the next purchase or extend Prime membership, all without human intervention.  

Simply put, Agentic AI did not just answer a question; it managed the situation proactively and built loyalty. 

(b) A digital personal stylist (Eliminating impersonal online experience)

Agentic AI goes beyond the basic “customers who bought this also bought” recommendation. It acts as a curated shopping assistant, analyzing a user’s entire history and current context to suggest outfits or complete solutions through a conversational interface.  

Real-world example (Stitch Fix): Stitch Fix’s built its business around an AI stylist. Their AI stylist analyzes your profile, feedback, and purchases to curate personalized clothing boxes. The human stylist adds the final touch, but the major work of filtering items to your specific likes is done by the AI system.

(c) Unified commerce enablement (Eliminating the disconnected online and offline world)

This involves bridging the gap between online and offline data. Agentic AI uses a customer’s digital activity to personalize their in-store experience, ensuring the brand recognizes them at every touchpoint. 

Real-world example (Nike): Nike uses a member ecosystem powered by AI. When a customer saves a product online, the Nike app notifies its availability/offers at a nearby store. Their SNKRS app uses your engagement history to provide exclusive access to product drops, using customers’ digital behaviour. 

Agentic AI in marketing customer experiences

Customers are tired of generic marketing campaigns that repeat the same things, which can not connect. Agentic AI enables marketers to not feel like advertising and more like a personal shopping service, delivering the right message to the right person at the right time. 

(a) Autonomous customer lifecycle management

Agentic AI does not just execute a single campaign, but it automates the entire customer relationship lifecycle. It automatically segments audiences, identifies the optimal channel and message for each individual. Also, it triggers a series of coordinated, personalized interactions to complete a business process like onboarding, adoption, or retention.

Real-world example (Pearson): Pearson uses Agentic AI to manage the entire learner lifecycle. The AI autonomously personalizes the learning journey for millions of students. It not only recommends a course; it proactively delivers specific learning modules, practice exercises, and motivational content based on a student’s engagement data. If the AI detects a student is struggling with a concept, it automatically triggers supportive interventions to guide them back on track, acting as a 24/7 personal tutor for every single learner. 

AI in energy and sustainability

In the energy sector, enterprises want to reduce costs and meet sustainability targets. Here, Agentic AI transforms its operations of relationships by moving from simple billing and reporting to acting as an autonomous energy advisor that proactively manages and optimizes consumption.

(a) Proactive account management 

Agentic AI transitions the energy provider’s role from a reactive utility to a proactive partner. The system autonomously monitors a client’s energy assets, consumption patterns, and sustainability goals. It not only reports data; it analyzes it in real-time to identify inefficiencies, predict future costs, and deliver a tailored action plan to reduce both expenses and carbon emissions.

Real-world example (ENGIE): ENGIE uses an Agentforce AI agent that autonomously manages corporate energy portfolios. The AI system proactively monitors energy consumption patterns, identifies inefficiencies, and automatically generates and delivers personalized reports with actionable recommendations for reducing costs and carbon footprint, acting as an automated sustainability consultant.

The Foundation: Why is Data the Most Essential for Agentic AI?  

An Agentic AI can only be as effective as the information it can access (or what you have provided). You can not get an intelligent agent who is blindfolded. 

For AI to proactively reschedule the delivery, it will need to see the shipping data. To act as a personal stylist, it needs purchase history and real-time browsing behavior. In addition to that, it needs secure access to account details to guide a loan application. 

If your customer data is in silos, your AI will be severely limited from the start. This is why the platform you build on is critical for AI. To leverage the power of Agentic AI, a unified customer data model is required to provide a 360-degree view of every customer interaction.  

This is the core focus behind platforms like Salesforce Agentforce. By building Agentic AI on top of the customer 360, the AI is not just making guesses; it is now making informed, intelligent decisions based on the entire customer story. 

Conclusion

The evolution of customer experience is clear. We are moving beyond simple automation and into the era of intelligent, proactive partnership. Agentic AI is a practical, powerful tool that is already delivering loyalty and enhancing overall customer experience for businesses.

The question for every business leader is no longer if AI will transform your customer experience, but what kind of AI you will choose. Will you settle for a system that simply answers questions, or will you invest in a partner that solves problems?

AUTHOR

Vishwajeet Srivastava

Salesforce Data Cloud, AI Products, ServiceNow, Product Engineering

Co-founder and CTO at Cyntexa also known as “VJ”. With 10+ years of experience and 22+ Salesforce certifications, he’s a seasoned expert in Salesforce Data Cloud & AI Products, Product Engineering, AWS, Google Cloud Platform, ServiceNow, and Managed Services. Known for blending strategic thinking with hands-on expertise, VJ is passionate about building scalable solutions that drive innovation, operational efficiency, and enterprise-wide transformation.