Applying AI and GenAI

AI-Enabled Service Design (AI-SERVD) is the application of a range of established and emerging AI, ML, GenAI and multi-agent system tools to identify change candidates as we define and optimise capabilities, processes, policies, systems, touchpoints, people’s roles and the operations that deliver innovative, intelligent services.

Applying the Stage Model to define CX and Platform Capability using service, systems, and AI thinking to identify change candidates across the end-to-end value chain.

AI and GenAI in the Experience Economy

Globally, AI and GenAI are reshaping the experience economy, driving efficiency, personalisation, and new service models. In the UK, the AI market is expected to contribute over £200 billion to the economy by 2040. Across Europe and the USA, organisations are rapidly embedding AI and GenAI to create predictive, adaptive, and autonomous services, leveraging not only machine learning but also increasingly sophisticated agentic systems.

This mirrors earlier shifts from products to services, as AI now drives a further evolution: from static services to continuously learning, self-optimising experiences.

A Triptych for Intelligent Design

Just as Service Thinking placed people, networks and experiences at the centre, AI-Enabled Service Design extends this by integrating AI Thinking—embedding data, predictive insights, generative creativity and autonomous decision-making directly into services.

Service Design ensures the offering aligns with human needs and system goals.
UX Design crafts the micro-interactions and interfaces.
AI Thinking powers the data-driven, adaptive, and generative aspects that continually evolve the service.

Together they form a new triptych for designing in the AI-powered experience economy.

Where to Use AI-Enabled Service Design

According to global best practice and studies by the Design Council, McKinsey and Gartner, AI-Enabled Service Design adds significant value when applied to:

Discovery: using GenAI for rapid synthesis of research, pattern identification and opportunity mapping.
Design: employing AI to generate, test and iterate service concepts, scenarios, content, and UI flows at speed.
Delivery: embedding ML models, generative pipelines, and multi-agent systems to operate, adapt and optimise live services.

AI-SERVD is especially powerful for:

Developing predictive or adaptive CX platforms.
Automating complex operational processes.
Creating hyper-personalised journeys.
Building agent-based ecosystems that manage themselves within defined guardrails.

AI-Enabled Tools & Methods

As a service and UX designer, there are a growing range of AI, ML, GenAI and agentic tools that I leverage, often woven into traditional design approaches. Here are a few examples:

AI Actors Map

Like a classic actors map, this explores the relationships between human and non-human agents—users, staff, and autonomous systems or AI agents. It maps out decision nodes, data flows, and responsibilities to provide a systemic view of the service ecosystem.

Intelligent Service Blueprint

An evolution of the service blueprint that overlays where AI, ML and GenAI components make decisions, generate outputs, or drive automation—showing clearly what is done by humans vs by machines, and how data is exchanged. This is critical for aligning the line of visibility with transparency, trust and accountability in AI-enabled services.

Experience Prototyping with GenAI

This involves simulating not just the user journey but also how AI responds in real-time. Tools like conversational simulators, agent-based scenario players and GenAI content engines are used to create ‘live’ prototypes that adapt based on user input, enabling richer testing of the service before building.

GenAI in the toolchain

In discovery, GenAI can analyse transcripts, cluster insights and even draft opportunity statements. In design, it can generate interface variants, service scripts, or test scenarios. In delivery, ML and multi-agent systems underpin automated processes and CX orchestration, ensuring continuous learning and improvement.

The Shift to Intelligent Operations

As one of the early adopters of combining service design with digital systems in the 1990s, I now see the same transformation as organisations embed AI deeply into operations. Today, it’s no longer just about optimising touchpoints or systems—it’s about orchestrating AI-enabled platforms that are predictive, generative, and often self-managing.

This shift demands a new mindset: treating AI not as a bolt-on, but as a co-designer and co-operator of services.

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