Artificial intelligence (AI) has quickly evolved from experimental technology into core business infrastructure. Globally, nearly nine in ten organisations report using AI in at least one business function. Large Language Models (LLMs) have powered much of that transformation, enabling organisations to create more personalised and responsive customer interactions at scale.
However, despite significant investment in AI, many customer experiences remain fundamentally reactive because of fragmented systems, disconnected workflows and siloed data.
While AI can surface information and assist with simple tasks, too often it still stops short of resolving a customer’s issue or orchestrating the broader journey around them. As organisations increasingly compete in the experience economy, where loyalty is shaped by the quality of every interaction, that reactive approach is beginning to show its limits.
Large Action Models (LAMs) represent the next major shift in enterprise AI, moving organisations from conversation toward autonomous, governed and outcome-driven execution across systems and workflows. More importantly, they are also accelerating a broader transition toward agentic orchestration at scale.
From conversation to execution
That shift matters because it fundamentally changes what AI 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 rise of agentic experience 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, 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 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.
Organisations 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.
Governance becomes foundational
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 organisations scaling agentic AI across customer experience, we believe this trust will increasingly become a competitive differentiator.
Why the human role is more important than ever
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 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 road toward autonomy
We believe the next era of customer experience will not be defined by who has the most AI, but those that can orchestrate AI, people, systems and data most effectively.
LAMs represent a shift away from disconnected automation toward coordinated, outcome-driven experiences – where journeys feel more seamless, issues are resolved faster, and organisations can scale both operational efficiency and customer loyalty simultaneously. But autonomy alone is not enough.
As AI becomes increasingly capable of acting independently across the enterprise, the organisations positioned to stand out will be the ones that combine intelligent agentic orchestration with strong governance, human oversight and trusted execution.
We believe the future of customer experience will depend not only on smarter AI, but on how effectively organisations combine technology, trust and human collaboration to orchestrate connected, outcome-driven experiences at scale.
