The Spark AI Maturity Model™

What each stage looks like in practice – and the AI tools you'll see at each one

Stage 1

Experimentation

Stage 2

Adoption

Stage 3

Optimisation

Stage 4

Innovation

Increasing Maturity →

People

Individuals experiment in isolation

Shared training and language

Role-specific application

AI-native team culture

Process

No standards or governance

Acceptable use policies in place

AI embedded in workflows

Continuous process innovation

Data

No structured data use

Basic data literacy across roles

Data informs decisions

Predictive, real-time data use

Tools

Scattered, personal tool use

Approved tool stack defined

Tools integrated into delivery

Custom builds and automation

Strategy

No AI strategy exists

AI on leadership agenda

AI drives commercial decisions

AI is a competitive differentiator

AI tools
you'll see

ChatGPT / Claude / Gemini / Copilot in blank chat windows
Midjourney / Nano Banana experiments
Fireflies / Otter for transcription

Projects with brand voice & docs
Custom GPTs / Gems for tasks
Shared prompts on Slack
AI for research & briefs

Team-wide Skills & Projects
Scheduled tasks (Cowork / Make / Zapier)
Figma Weave for creative
CRM connected to AI

Multi-agent systems (Claude Code / Codex)
Automated content & production pipelines
Custom-built client tools
AI informing strategy

How Spark
helps

AI Fundamentals

Half-day workshop

AI Accelerator

3-month programme

Strategic Coaching & Quarterly Horizons

Ongoing advisory, custom tools, agent builds

Where agencies are today (Spark Report, April 2026)

~70%

~15%

~8%

~7%

Experimentation Adoption Optimisation Innovation

Works across: Claude ChatGPT Gemini Copilot + Make / Zapier / Power Automate / n8n / Google Workspace automations

The Spark Report – AI in Agencies: From Activity to Advantage
Publishing 14 April 2026. Your practical guide to moving through the Spark AI Maturity Model™.