Getting Ready for AI: The Interconnection Between AI Maturity, Digitisation Maturity, and Innovation Maturity
Introduction
Artificial Intelligence (AI) is rapidly transforming industries, but successful AI adoption isn’t just about implementing algorithms—it requires a foundation of digitised processes, structured data, and a culture of innovation. Organisations that attempt to integrate AI without first establishing digital maturity and fostering innovation often struggle with inefficiencies, inaccurate insights, and failed implementations.
To navigate this complexity, businesses leverage three key maturity models:
AI Maturity Matrix: Assesses an organisation’s readiness to adopt and scale AI.
Digitisation Maturity Matrix: Evaluate the level of digital transformation and data readiness.
Innovation Maturity Matrix: Measures an organisation’s ability to experiment, adapt, and innovate.
These three maturity models are interconnected, forming a strategic framework that ensures businesses are ready for AI and automation. In this blog, we explore how they relate and why they must be used together for AI success.
1. AI Maturity Matrix: Understanding Readiness for AI
The AI Maturity Matrix helps organisations assess where they stand in their AI adoption journey, from early experimentation to full-scale enterprise AI. It typically includes stages such as:
Awareness & Experimentation: Organisations explore AI use cases but lack structured data or governance.
Operationalisation: AI is integrated into certain processes, but scalability remains a challenge.
Optimisation & Expansion: AI is systematically embedded across business functions.
Autonomous & Scalable AI: AI operates at scale with advanced automation, self-learning systems, and decision-making capabilities.
Why AI Maturity Depends on Digitisation and Innovation
AI cannot function effectively without data and digital infrastructure. If a company lacks digitised processes, AI has no structured data to learn from. Likewise, AI thrives in an innovation-friendly environment, where teams experiment, iterate, and refine models based on business needs.
Thus, AI maturity is directly tied to the level of digitisation maturity and innovation maturity—without these, AI projects remain stuck in the experimental phase.
2. Digitisation Maturity Matrix: The Foundation for AI and Automation
The Digitisation Maturity Matrix evaluates how well an organisation has transformed its processes, data, and technology. It typically progresses through these stages:
Paper-Based & Manual Processes: Heavy reliance on legacy systems, spreadsheets, and physical records.
Partial Digital Adoption: Some processes are digitised, but there are data silos and inconsistent integration.
Fully Digital Processes: End-to-end digital workflows, cloud adoption, and connected data systems.
Data-Driven Organisation: AI-ready, analytics-driven, with real-time decision-making capabilities.
Why Digitisation is Essential for AI
AI requires structured, high-quality data to generate meaningful insights. If a business still relies on manual processes or fragmented data systems, AI will struggle to provide accurate and actionable outputs.
Key digitisation areas that impact AI adoption include:
✔ Cloud & Data Infrastructure – Ensures scalable AI capabilities.
✔ Automation & IoT Integration – Enables real-time AI-driven decisions.
✔ Enterprise Data Governance – Maintains data quality, security, and compliance.
In short, without digitisation maturity, AI remains ineffective.
3. Innovation Maturity Matrix: The Culture that Drives AI Success
The Innovation Maturity Matrix assesses an organisation’s ability to experiment, adapt, and innovate. The stages often include:
Reactive & Risk-Averse: Innovation is limited to necessity-driven changes.
Structured Experimentation: Teams explore new technologies but lack systematic execution.
Culture of Continuous Innovation: Employees are encouraged to test, iterate, and refine ideas.
AI-Driven Innovation: The organisation continuously evolves with AI, adapting new business models.
Why Innovation Maturity is a Game-Changer for AI
AI is not a plug-and-play technology—it requires constant experimentation, iteration, and adaptation. Companies with a low innovation maturity often struggle because:
They lack a risk-taking culture, slowing down AI adoption.
They fail to iterate AI models, leading to outdated or irrelevant solutions.
They resist organisational change, preventing AI from scaling effectively.
Businesses that foster innovation maturity create an environment where AI and automation can evolve with market demands.
4. How These Maturity Matrices Work Together
A Holistic Approach to AI Readiness
For AI to succeed, businesses must align their AI, digitisation, and innovation maturity levels. Here’s how they interconnect:
Real-World Example: AI in Customer Experience
Imagine a retail company wants to use AI-powered customer personalisation. If they lack digital maturity (e.g., their customer data is stored in disconnected legacy systems), AI won’t function effectively.
Even if they have digitised data, but lack innovation maturity, they may fail to test and refine AI-driven recommendations, leading to poor customer engagement.
However, if the company has strong digitisation and innovation maturity, AI can dynamically analyse customer preferences, test new engagement strategies, and continuously refine experiences based on real-time feedback.
5. Steps to Align the Three Maturity Models for AI Success
✔ Assess Your Current Maturity Levels: Use AI, digitisation, and innovation maturity matrices to identify gaps.
✔ Prioritise Data & Digitisation First: Build the necessary digital foundation before scaling AI.
✔ Foster a Culture of Innovation: Encourage experimentation, cross-functional AI teams, and iterative improvements.
✔ Invest in Scalable AI & Automation: Use AI tools that integrate seamlessly into digital workflows.
✔ Continuously Measure & Optimise: Treat AI adoption as a long-term transformation, not a one-time project.
Conclusion
AI adoption isn’t just about deploying models—it’s about building the right foundation. AI maturity depends on digitisation maturity to ensure structured data availability and innovation maturity to drive continuous improvement.
Organisations that align these three maturity models are better positioned to scale AI successfully, drive automation, and create real business value.
To get AI-ready, start by evaluating your digitisation and innovation gaps—because without these, AI remains just an idea rather than a game-changing reality.
