Despite all the attention on customer experience, it’s been stuck in a rut. Agentic AI in CX has the potential to get it unstuck.
In 2024, customer perceptions of CX quality in the US dropped to an all-time low, according to Forrester’s annual CX Index. At both the brand and industry level, declines were steeper than ever.
The problem here isn’t all that mysterious. The best customer experience is easy, personalized, and proactive. But delivering that kind of CX is hard given all the disparate systems companies have stood up over the years and manual work that goes into running them.
Artificial intelligence is helping enterprises improve efficiency and boost productivity, but AI agents—working autonomously and proactively—are expected to supercharge these benefits, making it easier to deliver the kind of stellar experiences customers expect.
What is agentic AI?
Agentic AI is a type of artificial intelligence (AI) that can work on its own to complete goals without needing you to guide every step. AI agents are the actual software programs that do this work, acting like digital helpers that understand what you want, make choices, and take real actions across different websites and apps. Unlike traditional AI that just gives information, these systems can understand what’s happening around them, figure out what you really need, and solve problems in ways similar to how humans think.
We’ve all heard a lot about artificial intelligence and generative AI, so why are AI agents such a big deal?
Agentic AI uses large language models, which give it more flexibility than other forms of AI that rely on rules and machine learning. AI chatbots use large language models, but AI agents incorporate additional technologies and memory resources that allow them to adjust to changing conditions and operate proactively.
Think of it this way: With other forms of AI, you ask it to do something specific, whether that’s answering a question or completing a task. In contrast, you can ask AI agents to do something without explicit instructions or a predetermined output and they can figure out what to do.
The vision is to have a network of AI agents working together across business operations and various systems to undertake complex tasks with little or no human intervention.
It’s still early days for multi-agent workflows, but Gartner predicts that 33% of enterprise software applications will include agentic AI by 2028.
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Benefits of AI agents for customer experience
Agentic AI promises automation breakthroughs in all aspects of business. In CX, it has the potential to streamline and speed work so that companies can reach new levels of automation and efficiency across their business for happier customers.
The convergence of AI-powered agents and new technologies that get rid of the steps between “what do I want to get done” and “what do I need to do to get it done” will enable companies “to achieve the holy grail of improved productivity, higher customer satisfaction through a superior and differentiated customer experience, and better financial performance,” BCG analysts wrote.
A 2024 Capgemini survey found that 82% of organizations plan to integrate AI agents into their business operations within one to three years.
Here are some of the top benefits of agentic AI in CX:
- Improved productivity. Streamlined processes and automation reduce manual work for faster, more efficient service and superior experiences.
- Better employee experience. Freed from time-consuming tedious tasks, employees such as sales and service reps can focus on strategic ways to help customers.
- Lower costs. By automating routine tasks and reducing the workload on human agents, AI agents can lead to significant cost savings.
- Personalized experiences. AI is already helping businesses on this front by analyzing customer data, but AI agents will be able to step it up based on context to produce real-time personalized offers and content.
- Improved decision-making. Gartner believes intelligent agents will alter decision-making in the enterprise with faster data analysis and prediction intelligence.
- Continuous improvement. AI agents learn from each interaction, improving their output and CX over time.
- Seamless CX. AI agents working together across business operations could break down silos for real-time, proactive experiences.
Examples of agentic in CX: customer service
In customer service, AI is boosting agent productivity by automating repetitive tasks and putting information at their fingertips. Gen AI-enabled service tools can summarize customer information so agents don’t lose precious time hunting through multiple systems. They also can craft personalized emails and suggest resolutions.
Customer service organizations can gain more automation benefits with new AI agent software. Working behind the scenes alongside service teams, autonomous agents simplify processes and speed resolutions.
The Capgemini study found that 64% of organizations expect AI agents to improve service significantly, driving up customer satisfaction.
For example, an AI agent can quickly classify service tickets using natural language and route them to the right team. Say a customer submits a ticket about a failed login attempt:
- The agent is able to recognize this as an account access issue
- Identifies it as high priority due to its impact on user access
- Routes it to the identity management team
- Updates the ticket category, priority, and assignment fields
When the issue is resolved, another AI agent can analyze the solution, summarize key steps, creates a structured article, and makes it available for future reference.
A case classification agent has helped Bosch Power Tools replace hundreds of static routing workflow rules to reduce manual work, accelerate response times, and boost first-time resolution.
AI agents and intelligent sales
Sales is another area where agentic AI holds a lot of promise. In B2B sales, AI agents could improve efficiency and effectiveness in everything from lead generation and sales forecasting to personalized outreach, pipeline management, and churn prediction.
One way AI agents can help sellers is by accessing information much faster, giving them more time to focus on customers. Instead of searching through multiple systems, they can ask the agent questions using natural language and receive accurate, sourced answers instantly.
The agent understands the intent and context of the seller’s question, searches internal sources, evaluates potential answers for relevance, and provides responses with sourced references.
For example, if a sales rep asks for pricing information:
- The agent analyzes the question to understand what specific product line the seller is asking about and the need for pricing structure information
- It searches across relevant sources including price sheets, policy documents, similar queries
- The agent provides a structured response with clear tier breakdowns and source details
In the future, AI agents won’t need direct input from sellers or sales managers to monitor opportunities and surface contextual information to help them reach sales goals. Sellers will be able to interact with an AI copilot that operates as an orchestrator, which brings together AI agents from multiple business functions to perform complex workflows.
Transforming e-commerce through intelligent automation
E-commerce is another area where agentic AI could ramp up CX. AI agents can dramatically change how customers discover products, receive support, and make purchase decisions while helping businesses manage product data more effectively.
Instead of forcing customers to navigate rigid category structures or use precise keywords, intelligent agents can understand natural language requests like “comfortable office chair for someone with back problems” and intelligently match products to customer needs.
These agents comprehend complex shopping intent, explore product catalogs intelligently, and present relevant options with helpful comparisons.
For example, when a customer looks for a product:
- The agent analyzes the request to understand specific requirements, preferences, and use cases
- Searches across product categories, considering attributes that might not be explicitly mentioned
- Presents options with visual comparisons and explains why each might suit the customer’s needs
When a customer ask for help, AI agents can respond immediately via websites and portals. Unlike basic chatbots, these agents deliver true service resolution by understanding context and accessing order history.
- When customers ask about order status, the agent can provide real-time tracking information
- For return inquiries, it explains personalized policies based on purchase history
- If products are unavailable, it suggests alternatives based on shared attributes
Behind the scenes, intelligent agents automate the tedious work of maintaining product information across thousands of items. This ensures customers always see accurate, consistent details while reducing the manual effort required from merchandising teams.
Future of agentic AI in CX
Multiagent AI, where AI agents collaborate across business operations, is quickly moving from a vision to reality. With this type of capability, the possibilities for customer experience and the entire enterprise are endless.
For instance, AI agents working across manufacturing and supply chain operations could help analyze data to forecast costs and track potential issues. This information would be passed along to customer service agents, who can inform customers about the status of their order, alert them to delays, and provide personalized offers to mitigate any problems.
By continuously monitoring and analyzing data across the supply chain, agentic AI could optimize logistics to avoid stockouts that frustrate customers.
Or an AI agent could automatically detect customer engagement signals, such as a person clicking on a website, and work with a supply chain agent to determine if the person meets the criteria for a promotional offer. Another agent could recommend the type of incentive while another sets aside the inventory.
Collaborative agents could also help organizations work proactively to help prevent customer churn with a promotional campaign or offer.
Getting started
While agentic AI is still developing, organizations should start incorporating it into their strategic plans and not be afraid to think big. Consider what you’re doing now to automate processes how AI agents could make that easier.
Gartner says organizations also will need advanced tools and strict guardrails to manage agentic AI risks, including:
- Proliferating without governance or tracking
- Making untrustworthy decision
- Relying on low-quality data
- Smart malware
- Employee resistance
The Capgemini study found that enterprises recognize the need to establish a governance framework and safety mechanisms for AI agents. More than half of organizations surveyed said they need robust controls before integrating AI agents into their operations.
With the right data foundation, controls, and oversight, agentic AI will help transform the way teams work so that businesses can reach their CX goals and make truly seamless experiences a reality.
