Don’t Lose Track of Agentic AI’s Primary Purpose: Providing World-Class CX
Six of 10 customers (63 percent) expect AI-fueled technologies to become the primary mode of customer support in the years to come, compared to 21 percent just four years earlier. That’s a staggering change in expectations, but when you consider the advance of genAI and agentic AI in the past two and a half years, maybe that leap forward is not quite so surprising.
Since the advent of ChatGPT, the way humans have interacted and collaborated with AI has taken big strides forward. Just think: In the early days of ChatGPT, I was asking it things like “How many ounces are in a pint?” and “Generate a picture of my dog in space.” Fast-forward a couple years, and genAI tools can build complex user interfaces, analyze large datasets, and ensure we’re using a friendly, professional tone in the emails we send. With the more recent evolution of agentic AI, industries are taking another dramatic leap forward, too.
What does agentic AI mean in supply chain commerce? Think AI-powered supply-chain and retail specialists, available on demand, capable of proactively optimizing inventories in response to fluctuations in real-time demand; agents that can autonomously respond to consumer queries without the need for constant human guidance; or agents that can assist store operations managers by keeping tabs on store performance or sales goals.
In the context of customer service and retail, agentic AI represents a particularly interesting opportunity to improve customer experiences while also maximizing operational efficiency.
But let’s be honest; the goal of AI is not to simply assist the customer without involving a human. The goal is to provide a better experience for the customer, whether they talk to an AI agent, a human contact center representative, or any combination of the two.
At a time when customer retention is more important than ever, we cannot let the excitement of AI distract us from the primary goal—providing world-class customer experiences. Below are three important areas to consider when looking at deploying AI in the context of customer service and support:
AI Agents for Customer Support
Agentic AI will revolutionize this space, as it can autonomously provide 24/7 real-time support to customers. From basic FAQs about return policies to questions like “Where is my red sweater and when will it be arriving?” and to truly actionable flows like “My item arrived damaged. Can you send a replacement?” Or “I ordered this last week on your site, but now it’s on sale. Can you match the new price?”
Today advanced AI agents, infused with order, payment, store, and product availability information, can deliver personalized, contextual customer service on a par with what human agents can offer. These tools are becoming easier to implement and integrate, offering retailers a scalable and efficient way to quickly deflect service inquiries, increase customer satisfaction, and generate meaningful ROI.
Offering an Assist to Agents
Even with all the advances in AI, many customers will continue to get support from customer service representatives (CSRs). Thanks to AI, the service they get will be faster and more accurate, because those CSRs will be boosted by the power of genAI.
Let’s take an example: A customer chats with a CSR to ask if they can return an armchair to their nearby store. Without AI, many agents need to use multiple systems to answer this question. First, they look up the customer to find their loyalty status. Second, they look up the order to see how long ago the item was shipped and if the item is returnable at stores. Next, they might go to a knowledge management system to read about the return policy for this specific scenario.
This is an all-too-common setup, where CSRs use up to 10 different systems to service customers. I recently spoke to a new hire class of customer service agents who shared their biggest pain point: having to learn and work with too many different software systems.
But with AI, an agent can simply ask their AI assistant, “Can the customer return this chair to the Lenox Mall store?” The AI seamlessly looks up the information in the three different systems and responds with a simple yes or no.
From getting answers to common questions and troubleshooting customer issues; to having more information and context about a customer’s experience with the brand; to providing summaries of customer sentiment, past purchases, and recent conversations; to even onboarding new CSRs more quickly, AI is putting brands in the fast lane when it comes to customer support.
Better Insights Make for Better Service
One of the ways genAI shines the most for customer service organizations is by mining data across all customer chats, emails, and messages in real time, identifying behavioral trends and buying patterns.
Retailers can keep a pulse on their customers and react more quickly (even proactively in some cases) to the voice of the customer, rather than waiting for after-the-fact survey responses. For example, if CSR leadership sees an abnormally high volume of contacts related to payment issues on the website—they can detect and resolve issues proactively, reducing the chances of customer escalation and dissatisfaction. The same goes for product quality issues or problems with certain carriers: CSR leadership can react quickly when they have a real-time pulse on their customer’s conversations.
From the early days of mechanical automatons to more recent conversational AI-powered chat experiences, scientists, engineers and futurists have dreamed of a tomorrow where AI systems can work and act intelligently and independently.
Recent advances in generative and agentic AI are bringing the vision of an autonomous future a step closer to reality in supply chain and commerce. As it does, it reminds us of the increasing importance and value of the role CSRs will continue to play as part of continually evolving brand-customer narratives.
Ellie Crawford is the director of product management at Manhattan Associates. She has extensive experience in product management, research development, business analysis, and consulting. Crawford has previously worked at companies such as Anheuser-Busch InBev, Georgia Tech Intelligent Transportation Systems, and Eastman Chemical Company.