Agentic AI is the next frontier of artificial intelligence. Improving customer service interactions has been an early popular use case, where AI agents are deputized as part of a company’s workforce, intended to personalize customer service beyond standard conversational AI.
Using an example of a customer initiating a return, a GenAI-based chatbot interaction might involve asking how to print out a prepaid mailing label, inquiring about options for how to apply a balance, or other straightforward questions that can be answered without the model having to know much, if anything, about the customer posing the question. Agentic AI essentially dials it up a notch, automating more complex tasks. A returns inquiry, for example, might entail the agent providing a customer with options, waiting for the customer’s decision, and then scheduling a pick-up time. Or letting the customer know the closest drop-off location that also has the product the customer wants in stock. The agent might then help the customer complete the transaction, while also arranging with the store to retrieve the item for pick-up.
Agentic AI Depends on High-Quality Customer Data
As agentic AI becomes more enmeshed in customer service, companies will begin to entrust more processes to agents, transitioning from live agents whenever possible. When agentic AI becomes part of the everyday fabric of how customers engage with brands, customers will naturally have higher expectations for AI agents – viewing transfers to live agents as a dreaded last resort.
Heightened expectations will require that AI agents know everything there is to know about a customer. To schedule a pick-up for a product return, for instance, the agent should have a customer’s updated address. To help the customer apply her new balance, it should have access to real-time product inventory. A comprehensive, real-time understanding of a customer will also include a customer’s payment information, her preferences, her website activity and other behaviors – even social media. For an AI agent to provide a relevant experience, it will require real-time updates to a unified customer profile as well as real-time access to the unified profile. An AI agent’s overall effectiveness, in other words, is highly dependent on the data it’s fed being complete, accurate and timely.
The “Double Agent” Concept: Agents Speaking with Agents
Expanding the concept of a company having its data ready for agentic AI, it might soon become commonplace for brands and customers to be mutually transparent for how agents are involved in CX. It will be an open secret, in other words, that a brand is using agentic AI almost as a personal concierge to help guide an individual customer journey. The customer, in exchange for receiving a more personalized experience, will provide an agent with what it thinks the agent needs to meet the customer’s expectations. Taken to the extreme, agentic AI as a CX tool could even involve a customer creating their own personal agents for different brands – with the more trusted brands receiving more detailed data and preferences from the customer’s agents.
Agentic AI is poised to redefine customer engagement, shifting from basic chatbot interactions to fully autonomous AI agents that manage complex tasks and anticipate customer needs. As brands integrate these advanced AI systems, the key to delivering seamless, personalized experiences will be real-time, high-quality customer data.
In this “double agent” concept where the consumer gives its own agents permission to interact with a brand’s agents, an extendable use case may even evolve to a customer using an agent to start a customer journey. In lieu of a product search on a website, for example, a customer might set rules for its agent and then send it off to negotiate with one or multiple agents for the best deal.
Perhaps a customer is interested in booking a family vacation. Because it’s a loyal customer of one travel brand in particular, the customer has shared personal data with the brand’s agent – the ages of the traveling children, the type of accommodation needed, the preferred on-site activities, and other personal details that the agent uses to create a personalized experience. The customer initiates the entire experience through its agent. Data from one engagement is fed back into the agent to improve future experiences.
Agentic AI’s Sidekick: Data Readiness
A consistent data feedback cycle that creates a more robust agent – i.e., it continually expands its understanding of a customer – has the potential to all but eliminate common CX friction.
As a digital stand-in for a customer, an AI agent becomes a customer’s biggest champion. It will advocate for and anticipate a customer’s needs as it works with a brand’s agent to optimize the customer journey. Real-time relevance will depend largely on the quality of the data used to feed the agent – the brand’s and the customer’s – underscoring data readiness as a foundational requirement.
Data readiness takes a holistic approach to data quality as an enterprise principle, a foundational requirement of a data-driven approach to accomplish business and CX goals. Data readiness ensures that enterprise customer data is right (it is complete, accurate and timely), and is fit-for-purpose (actionable, trusted and compliant).
The Future of AI-Driven Customer Experience
Agentic AI is poised to redefine customer engagement, shifting from basic chatbot interactions to fully autonomous AI agents that manage complex tasks and anticipate customer needs. As brands integrate these advanced AI systems, the key to delivering seamless, personalized experiences will be real-time, high-quality customer data. The “double agent” concept – where both businesses and consumers deploy AI agents – will further shape a future where interactions are more efficient, transparent, and tailored than ever before.
To stay ahead, companies must prioritize data readiness, ensuring AI agents have the insights needed to drive meaningful, frictionless customer experiences. Those that embrace this shift will set a new standard for customer engagement, turning AI-driven interactions into a strategic advantage.
