By: Terence Chesire

Generative AI is no longer a concept on the horizon. With 77 percent of customer service (CX) leaders either already using or planning to deploy generative AI solutions to improve customer experience processes, it’s clear we've reached a critical turning point. The real question has shifted from "can we adopt generative AI" to "how can we make the most from our AI investments?" The potential of AI is clear, but realizing that potential is the next challenge.

CX leaders need results they can take to the boardroom — clear metrics tied to revenue growth, cost reduction, and customer retention. Fortunately, generative AI and the AI Agents it can power, are already proving their worth for many CX teams by delivering tangible outcomes from day one, from faster issue resolution to predictive customer support that cuts costs and drives customer loyalty.

While many organizations have already started their journey into AI adoption, an intentional, strategic approach that leverages AI’s capabilities across the entire customer journey is critical to maximizing the potential impact of the technology. As the capabilities of generative AI expand, so does its autonomy. Agentic AI, or AI systems that can take autonomous actions based on real-time data, is becoming a critical component of this transformation. From driving ROI to overcoming legacy systems and aligning AI efforts with key business goals, generative AI is poised to transform the customer experience from the ground up.

Thinking Past Point Solutions

As organizations race to adopt AI, many fall into the trap of cobbling together point solutions — individual AI tools designed to tackle specific problems in isolation. While these solutions can deliver short-term benefits, they often lead to fragmented experiences, inconsistent data handling, and operational inefficiencies. A chatbot might improve response times, but without integration with backend systems, it can’t provide meaningful resolutions. An AI-driven analytics tool might surface insights, but if it doesn’t connect to action-taking workflows, those insights remain just that — data without execution.

Instead of treating AI as a collection of separate add-ons, businesses need to take a platform approach. A unified AI-powered CX platform enables seamless data flow, intelligent automation, and a more holistic view of the customer journey. By consolidating AI capabilities into a single, interoperable framework, organizations can ensure that AI-driven interactions are consistent, intelligent, and capable of driving meaningful business outcomes.

For example, rather than deploying separate AI tools for omnichannel interactions, technical troubleshooting, and appointment scheduling for a technician visit, a platform-based approach integrates end-to-end customer resolution under a single AI framework. This ensures that organizations can scale AI more effectively, as updates and improvements can be applied across the entire system rather than requiring piecemeal upgrades.

AI is most powerful when it’s connected, adaptive, and continuously learning from every interaction. A well-integrated AI platform does more than solve isolated problems — it transforms customer experience end-to-end, turning AI from a collection of tools into a true business accelerator.

Driving Immediate ROI

Envision customer service evolving from a time-consuming, fragmented process into a seamless, near-instantaneous experience. Generative AI is key to moving from potential to reality. Traditional support models used to involve lengthy investigations, multiple handoffs, and in some cases, mountains of paperwork. Now, AI-powered systems analyze complex problems in seconds, deliver immediate resolution, and generate concise wrap-up notes with minimal human intervention.

This change isn’t just about better customer service — it’s reshaping how businesses operate and manage costs, even in field service. Take, for example, a field technician dispatched to repair a malfunctioning HVAC system at a commercial building. Traditionally, they might need to consult manuals, call support for guidance, or wait for parts after diagnosing the issue onsite. This outdated approach often leads to delays, repeat visits, and higher operational costs.

With generative AI, the process looks entirely different. Before the technician even arrives, AI analyzes historical repair data, equipment specifications, and error codes from IoT-connected devices to predict the root cause of the failure. Not only does it offer insights, but it can autonomously generate step-by-step repair instructions and order the necessary parts, all without human intervention. The system can even schedule logistics to ensure that the parts arrive on time, streamlining the entire process.

Once on-site, the technician is guided by an AI Agent assistant, which autonomously provides troubleshooting resources, visual aids, and instructions tailored to the real-time situation. If the technician needs additional assistance, AI-powered tools can initiate communication with human support agents, but it also ensures that issues are dealt with independently, wherever possible. By integrating generative and agentic AI into field operations, organizations can shift from reactive to predictive strategies, ensuring issues are addressed before they escalate into larger disruptions.