AI, Large Action Models and customer experience

Artificial intelligence has transformed how organisations engage with customers, enabling faster responses, more personalised interactions and always-on digital support. Yet behind many of those experiences, the operational reality remains fragmented. Systems are disconnected, workflows are siloed, and too many customer journeys still rely on manual intervention to reach resolution.

 

That gap is becoming increasingly significant as organisations compete in an experience economy where customer loyalty is shaped by how seamless and effortless interactions feel. While Large Language Models (LLMs) have helped businesses improve conversational engagement, attention is now shifting toward technologies capable of coordinating actions and outcomes across the wider customer journey.

 

This is where Large Action Models (LAMs) are beginning to reshape enterprise AI. Rather than simply generating responses, LAMs enable AI systems to take action across workflows, channels and enterprise systems in real time – helping organisations move closer to autonomous, connected customer experiences.

 

 

Moving beyond reactive CX

This marks a broader shift in how AI supports customer experience – from isolated interactions toward connected execution.

 

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.

 

 

Why orchestration matters

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, organisations 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 autonomous agentic 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, organisations 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, personalised and emotionally intelligent.

 

We are already seeing early examples of this in practice. Utility Warehouse, one of the first organisations 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.

 

Organisations best positioned to succeed in the next era of customer experience will be those not simply deploying more AI, but those capable of orchestrating intelligent, connected experiences at scale.

 

 

Governance is essential

As AI systems gain greater autonomy, governance is becoming vital.

 

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 the Model Context Protocol (MCP) and Agent-to-Agent (A2A) to 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 organisations scaling agentic AI across customer experience, we believe this trust will increasingly become a competitive differentiator.

 

 

Where human judgement leads  

As AI becomes more embedded in everyday work – with 36% of people already using AI tools in the workplace in the UK – the 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 handling exceptions, guiding decisions and stepping in during moments that require empathy and nuance.

 

We expect that balance will become increasingly important as organisations 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 frontier of customer experience

AI is quickly becoming standard across customer experience. What will separate organisations in the years ahead is not who has the most AI, but who can orchestrate it most effectively to drive outcomes its customers and business depend on.

 

LAMs are helping push customer experience in that direction by enabling AI systems to move beyond isolated automation toward autonomous execution across the enterprise. The businesses that get that balance right will be best placed to build loyalty in an increasingly experience-driven market. 

 

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Stuart Templeton is VP EMEA North at Genesys

 

Main image courtesy of iStockPhoto.com and bombermoon