Not all forms of artificial intelligence are created equal. Where analytical AI excels at drawing conclusions from raw data, and generative AI at creating content from a set of prompts and models, the tech behind today's most sophisticated customer experiences is agentic AI. That's AI capable of making a plan and executing it, without human prompts along the way.
For anyone in CX, agentic AI is where the promise of AI really starts to be fulfilled. Traditional chatbots can answer customer questions; AI agents can anticipate them. Robotic processing automation can complete tasks on a to-do list; AI agents can write their own to-do lists, break those tasks down into smaller goals, and dynamically rewrite their plan as they learn more and conditions change.
No wonder Qualtrics reports that agentic AI represents an annual opportunity of $440 billion for customer-facing businesses. If, that is, those business can meet the same challenge required by any other form of AI: to integrate it into human experiences.
When generative AI first made its way into the marketplace, companies rushed to release anything AI-related for clout chasing to capture some buzz. After the initial dust settled, the resulting customer experiences led to disenchanted, confused customers forced into friction-heavy experiences with little payoff.
The stakes with agentic AI are arguably much higher. As AI agents assist with sets of more complex tasks, there is greater potential of damaging the customer relationship when they miss the mark. Earlier this year, Salesforce released findings from a CRM study revealing LLM agents were successful only 35% of the time as tasks required multiple steps. If you're going to get the most out of the investment in agentic AI, you can’t afford a scatter-shot approach, simply hoping to hit something - anything. You have to target your efforts by knowing where, when, and how it will actually help your customers.
Detailed customer journey maps keep the needs and goals of real, human customers at the heart of your CX strategy and reveal opportunities to channel the capacities of AI agents to where they matter most. Let's look at some examples.
Of course, no form of AI can actually feel pain. But AI agents can understand customer pain points, emotional peaks and valleys, and common service gaps, and act as a sympathetic helper. You can't eliminate customer service issues. You can keep momentary dissatisfaction from ending the relationship.
Imagine a customer troubleshooting a service outage. They're irritated at first, then frustrated, and finally, when nothing works, full-blown angry. If this emotional profile is captured in a service map, an AI agent can offer step-by-step support with reassurances - and even faster escalation pathways and other remedies for customers whose history shows they're particularly at risk of leaving.
Journey maps also provide a comprehensive picture of tasks (primary, conditional, and edge case) to guide judgement for AI decision-making. Armed with this context, AI agents can look ahead at potential trouble spots for each customer and get out in front of them with personalized suggestions.
Let's say you have a problem with users abandoning checkout, some due to confusing payment options, others because of uncertainty with your return policies. Agentic AI could detect hesitation, recognize which bucket this customer is likely to fall into, and proactively offer real-time tailored help, like “Would you like me to split this payment into installments using your preferred card?” or "Remember, you have 30 days to return items for a full refund."
Omni-channel continuity is more crucial than ever as customers use multiple devices and platforms to orchestrate both digital real-world experiences. Journey maps provide vital context for when and why users may move across channels and systems to accomplish a goal.
In a travel booking journey, data might reveal that customers often feel stressed before departure due to missing travel documents or unclear itinerary details throughout their trip. An AI assistant can decide to send reminders, set calendar appointments, download digital boarding passes, organize ground travel, make dinner reservations, and adjust point-of-interest recommendations based on weather, transforming anxiety into assurance.
Ultimately, even the most advanced AI can't completely replace human beings. 74% of customers prefer phone contact for high-stakes interactions. Person-to-person contact will always be essential for handling your most complex, individual customer issues.
Unlike rudimentary chatbot logic, though, agentic AI can make much finer distinctions about when to move a customer to live intervention. It's a great example of how the agentic process loop of Perception, Reasoning, and Taking Action works best when fed with comprehensive journey maps.
An AI agent can perceive that a customer has tried to find answers about a billing issue with their account, and previous customer behavioral data around this particular issue. It can reason through the likeliest paths to resolution as well as the emotional stakes of each possible next step. And it can take action based on that judgment, connecting this customer more quickly to a representative if that's the best course of action.
Hypothetical scenarios can only provide the roughest sketch of agentic AI's potential. Which is exactly the point: every case is as unique as your company and diverse as your customers. But customer journey maps remain the touchstone for effective AI, because effective AI always centers on the needs of the humans who use it.
That's what WNDYR has done for a decade now. We've helped organizations of all sizes, from healthcare to financial services, implement enterprise tech solutions that put people first. Contact our CX practice to start mapping out your agentic AI strategy. It's the future of your industry, whatever industry you're in.