I’ve had a number of conversations recently with clients and prospects about their AI maturity levels. What strikes me is that many organisations are trying to run before they can walk, leaving behind many team members in the process. Too often, the cultural foundations needed to successfully adopt AI are overlooked, leading to resistance, confusion, and missed opportunities.
This is particularly true with AI implementations being led at the organisation level, much like traditional digitalisation projects.
According to research by McKinsey, while 92% of companies are increasing AI investments, only 1% consider themselves fully “AI mature” with AI being integrated into workflows at scale.
The key differentiator isn’t the technology, it’s organisational readiness. That’s why we created the AI Readiness Diagnostic: a streamlined tool designed to surface your team's strengths, gaps, and strategic priorities.
What Does "AI Readiness" Really Mean?
Readiness isn't just about writing a few prompts into ChatGPT - it’s about building the right foundation across people, processes, and purpose. Inspired by leading frameworks from Gartner, McKinsey and Deloitte, we developed our own simple diagnostic, breaking AI readiness into five key pillars.
1. Leadership, Culture, and Talent
The most sophisticated algorithm won’t save a team without the leadership to steer it.
This pillar evaluates executive sponsorship, organisational clarity, and the skills embedded within your team. Does your leadership speak with a unified voice on AI’s role? Are teams empowered, or hesitant, to experiment? Are your commercial functions hiring or upskilling talent with the data and digital fluency needed to thrive?
Great AI adoption stories often begin with culture. Leaders must model curiosity, reward experimentation, and promote a shared sense of purpose. When teams trust the process, feel safe to experiment, and see AI as an enabler rather than a threat, true transformation happens.
As I discussed in the recent blog Governing AI Responsibly, leadership and governance are critical to successful AI implementations and core elements of the ISO 42001 AI Management System.
The success of Promega’s OpenAI adoption is largely down to the foresight of Promega’s CEO, Bill Linton, and their VP of R&D, Poncho Meisenheimer. The organisation has an established AI Advisory Council that includes senior leaders from different departments and functions who spearhead initiatives to encourage AI adoption and experimentation.
2. Technology and Data Foundations
AI thrives on data, but only when the plumbing works. Even the best technical plumbing can falter without the cultural mindset needed to embrace AI’s potential.
This pillar assesses your technical infrastructure, data governance, and lifecycle maturity. It asks: Do you have scalable platforms that support AI deployment? Is your data accurate, accessible, and ethically sourced? Can your models evolve without breaking the system?
Teams often underestimate the gap between digital maturity and AI maturity. Just having data isn’t enough, it’s about readiness to use that data responsibly and effectively. In other words: do your foundations help AI thrive, or merely survive?
3. Risk Tolerance and Innovation Appetite
AI is inherently experimental. It’s about pattern recognition, predictive risk-taking, and strategic iteration. But how comfortable is your organisation with uncertainty?
This pillar examines your organisation’s ability to test, learn, and scale without over-engineering every move. Are you sandboxing new ideas with smart guardrails? Can your teams innovate without fear of failure?
AI readiness depends on building systems that reward insight and learning over perfection.
4. Ethical and Responsible AI
AI is powerful - but power must be principled.
This pillar explores your governance model: are fairness, transparency, and accountability woven into your development cycle? Are there safeguards to prevent bias and ensure compliance with emerging regulations?
Trust is no longer a soft metric – when it comes to AI, it’s a strategic differentiator. That’s why I was so excited to complete BSI’s AI Management Standards training course and gain my AI Management Practitioner professional qualification.
Teams that embed ethical thinking from the start are better positioned to sustain long-term impact and avoid costly missteps.
5. Commercial Value and ROI Alignment
Finally, the most important question: Why AI?
This pillar evaluates how tightly your AI initiatives are linked to real business outcomes. Are you chasing hype or solving real problems? Can you measure the impact on revenue, efficiency, or customer experience?
Too often, organisations jump into AI without a clear use case. Our diagnostic helps align innovation with intention, ensuring your AI investments fuel growth, not confusion.
From Insight to Action in Minutes
Each section includes a few sharp, focused questions. Your responses generate an intuitive maturity score and a radar chart that visually maps your organisation’s readiness across all five pillars.
It’s quick. It’s clear. And it’s built to guide your next step - whether you’re just getting started or scaling your AI ambitions.
Ready to Find Out How You Rank?
Becoming an AI-enabled organisation isn’t just about tools - it’s about readiness and maturity across leadership, culture, tech, ethics, and value. The Strivenn AI Readiness Diagnostic is your first step to transforming AI from a buzzword into a business advantage.
Before AI can transform your business results, you need to nourish a culture of AI-experimentation and exploration. That's where readiness begins.
