Despite years of artificial intelligence (AI) investment, most customer experiences still remain fragmented, reactive and heavily dependent on disconnected systems behind the scenes.
While 88% of businesses now use AI in at least one function, nearly two-thirds remain stuck in pilot phases, according to McKinsey, highlighting the gap between AI adoption and meaningful operational transformation.
VP for EMEA North at Genesys.
Large Language Models (LLMs) have helped businesses create more responsive and personalized interactions, but many organizations are beginning to recognize that conversational AI alone is not enough. As organizations increasingly compete in the experience economy, the focus is shifting toward technologies capable of coordinating actions, workflows and decisions across the entire journey in real time.
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That shift is helping drive a new era of agentic customer experience orchestration, where AI systems can move beyond simply responding to requests and instead help execute tasks, resolve issues and coordinate outcomes autonomously across the enterprise.
Large Action Models (LAMs) are emerging as a key part of that transition, helping organizations move beyond conversational intelligence to real-time operational execution.
Moving AI beyond conversation
That shift matters because it fundamentally changes what AI tools can deliver within customer experience.
LLMs brought conversational intelligence into the enterprise, helping AI understand intent and generate more natural interactions. LAMs build on that foundation by turning intent into action: determining the next best steps and executing multi-step workflows in real time, within enterprise-defined guardrails.
Importantly, the rise of LAMs does not signal the end of LLMs. The two technologies work side by side. LLMs remain critical for conversational understanding and contextual reasoning, while LAMs connect that intelligence to coordinated action. This moves AI beyond simply responding to requests, toward orchestrating outcomes across the customer journey.
For example, take a disrupted airline journey in peak holiday season. Until now, even advanced AI agents could usually only explain the delay or point customers toward another support channel.
Agentic virtual agents built by LAMs change that dynamic entirely. These virtual agents can authenticate the customer, rebook flights, update seating, process compensation, coordinate workflows across systems, and proactively send updates before the customer even asks.
That’s the real transformation taking place today: moving from AI that generates responses, to AI that helps orchestrate meaningful outcomes for customers.
The shift toward agentic orchestration
This marks the beginning of a broader shift toward autonomous customer experience driven by agentic orchestration. As AI systems become increasingly capable of reasoning and acting across systems, organizations are beginning to rethink the operating model behind customer experience itself.
Most enterprises were not designed to deliver the seamless, proactive and context-aware experiences we all increasingly expect. We believe closing that gap requires a new operating model for customer experience, one built on orchestration rather than isolated automation. One that can connect journeys end-to-end with shared context, continuity and coordinated execution across channels, systems, teams and AI agents.
This shift is particularly significant, because businesses today no longer compete solely on products or services. Increasingly, they compete based on experience.
Historically, organizations often faced a trade-off between operational efficiency and customer empathy. Improving one frequently came at the expense of the other. AI-powered experience orchestration has the potential to fundamentally change that equation by enabling experiences that are simultaneously efficient, proactive, personalized and emotionally intelligent.
We are already beginning to see early examples of this in practice. Utility Warehouse, one of the first organizations to deploy agentic virtual agents powered by LAMs, has used the technology to support complex customer journeys including billing support and service restoration.
By simplifying its experience architecture and better connecting front- and back-office workflows, the company has more than doubled containment rates while improving both customer and employee experiences.
Organizations best-positioned to succeed in the next era of customer experience will be those that are not simply deploying more AI, but those capable of orchestrating intelligent, connected experiences at scale.
Why governance is no longer optional
However, autonomy without governance creates the potential for risk.
Recent headlines of AI agents deleting databases, misinterpreting instructions and operating outside approved parameters have exposed a growing challenge for companies. The more capable AI becomes, the more important trust and accountability are.
Governance can no longer be treated as something layered on after deployment. As AI systems become more capable of reasoning and acting independently, governance must evolve from static policy into operational architecture embedded directly into orchestration layers.
This is where governance-by-design becomes essential. AI systems require enterprise-grade guardrails and clear operational boundaries to ensure autonomous actions remain trusted and aligned to business policies.
We expect open interoperability standards such as Model Context Protocol (MCP) and Agent-to-Agent (A2A) will also play an increasingly important role in enabling responsible agentic orchestration across the enterprise.
MCP is designed to act as a secure connective layer between AI systems, enterprise tools, data and workflows, helping provide the real-time context and controls AI systems need to operate safely and effectively. A2A can enable AI agents to securely communicate, collaborate and coordinate actions across different platforms and systems.
Together, these standards can help create the foundation for multi-agent orchestration, where AI agents and human teams can work together with shared context, governance and operational oversight to deliver more seamless, outcome-driven customer experiences. For organizations scaling agentic AI across customer experience, we believe this trust will increasingly become a competitive differentiator.
Human oversight remains essential As AI becomes more embedded into everyday work – with 36% of people already using AI tools in the workplace in the UK – conversation is shifting from what AI can automate, to where human judgement matters most.
AI is becoming more effective at handling routine and multi-step processes autonomously, but these systems still require human oversight. As AI takes on more operational responsibility, people will continue to play a critical role in designing the systems, handling exceptions, guiding decisions and stepping in during moments that require empathy and nuance.
We expect that balance will become increasingly important as organizations move toward more autonomous customer experiences. The goal is to enable humans and AI to operate as a coordinated system – each contributing where they are most effective.
The next chapter of autonomous customer experience
As competition increasingly shifts toward the experience economy, customer loyalty is shaped less by products or services alone and more by the quality of the overall experience they deliver. The challenge is no longer simply introducing AI into customer experience, but using it to remove friction, coordinate journeys and deliver outcomes more effectively across the enterprise.
LAMs are helping accelerate that transition by enabling AI systems to autonomously take action across workflows, channels and operational processes in real time – moving beyond reactive support toward more proactive and connected customer experiences.
It is vital that organizations can successfully combine AI, people and operations in ways that make experiences feel effortless for customers in order to compete. That ability to orchestrate intelligent, connected experiences that drive outcomes at scale will become a far more important differentiator than AI adoption alone.
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