At Growth Tribe, we’ve identified five distinct stages of AI Maturity, each requiring a different strategic approach. Our learning paths are designed to help organisations move confidently from one stage to the next—faster, smarter, and with measurable impact.
Table of Contents
Which AI maturity stage is your organisation in?
Figure 1: The five stages of AI Maturity—from Skepticism to full Maturity (Growth Tribe framework)
Stage 1: AI Skepticism
In the beginning, AI scepticism is the norm. Teams, for example in sales, marketing, and customer engagement are sceptical or even dismissive of AI’s potential. They question its relevance and worry about disrupting their tried-and-true methods. At this stage, it’s crucial to break down initial resistance by exploring how AI might fit into your commercial strategy.
For example, a sales team might doubt the effectiveness of AI-driven lead scoring, unsure
if it can really outperform traditional methods. This is where education comes into
play—attend webinars, consult with AI vendors, and begin to gather information that can
help ease these concerns.
⚠️ Importantly, AI literacy isn’t just a competitive advantage, it’s now a compliance requirement under the EU AI Act. That makes building foundational literacy not just a smart investment, but an essential first step.
👉 With that foundation in mind, start small. Identify one area where AI could deliver clear, visible benefits—such as improving lead quality or automating routine tasks. Then, use data insights or case studies to build a convincing business case and win broader support from your team.
Stage 2: AI Activation
Here, teams start launching pilot projects to test AI’s capabilities in a controlled environment. These small-scale experiments offer valuable hands-on experience.
Example: A marketing team tests an AI-powered email platform that personalises campaigns based on customer behaviour. This pilot helps them understand how AI can boost campaign ROI and customer engagement.
Choose a low-risk, high-impact project for your first AI pilot. For example, test AI tools to improve asset creation or copywriting. Establish clear metrics for success and share early results widely to build momentum.
👉 The challenge? Leaders must ensure pilots link to strategy and measure results clearly—otherwise, they risk staying stuck in “interesting experiments.”
Stage 3: AI Experimentation
With some early successes, organisations gain confidence and begin expanding AI use across multiple functions. At this stage, experimentation becomes more strategic, aligning projects with broader business goals and managing data effectively.
Example: Customer service integrates AI-driven sentiment analysis to classify and respond to tickets faster.
Example: Sales teams use AI to personalise outreach at scale, increasing efficiency and conversion rates.
As these experiments start showing tangible results, collaboration and knowledgesharing become essential. This is the moment to turn scattered initiatives into a coordinated learning process.
👉 Prioritise projects that align with your key business objectives, and ensure ongoing training so your teams can keep pace with rapid AI advancements.
Stage 4: AI Scaling
By this stage, AI is not just an experiment—it’s embedded into operations and decision-making. It transforms how the organisation works, boosting efficiency across workflows.
Example: Retailers integrate AI into CRM, automating segmentation, recommendations, and pricing strategies for seamless customer experiences.
Example: HR scales AI in recruitment, cutting time-to-hire while improving candidate fit. As adoption spreads, consistency and governance become critical. This is when leadership must ensure that new AI systems are robust, ethical, and measurable—laying the groundwork for sustainable growth.
👉 Action Point: Invest in strong infrastructure and governance frameworks. Monitor performance continuously and review results regularly to ensure that scaling AI consistently supports your strategic business goals.
Stage 5: AI Maturity
At the pinnacle of adoption, organisations achieve AI excellence. AI is now fully embedded into culture and operations, shifting towards an AI-first mindset: for every challenge, the question becomes “how can AI support this?”
Example: A global e-commerce giant uses AI to constantly optimise the entire customer journey—from ads to post-purchase support.
Example: A logistics firm leverages AI for real-time route optimisation, cutting costs and reducing emissions.
As these advanced applications scale, continuous improvement becomes the new standard. Mature organisations treat AI not as a finished project but as an evolving capability—combining innovation, governance, and strategic foresight.
👉 Action Point: Focus on innovation and longterm optimisation. Explore emerging applications like predictive analytics, generative AI, or industryspecific models, and review your AI roadmap regularly to keep it aligned with business goals, market shifts, and upcoming regulations.
From AI skepticism to AI maturity
Figure 2: Mapping Skills to AI Maturity – illustrating how key capabilities evolve across the five AI Maturity stages.
Where organisations stand today
A recent Growth Tribe community poll shows that most organisations are still in the Aware or Active stages—far behind the 78% of global enterprises already deploying AI at scale (Stanford AI Index, 2025).
This gap highlights a crucial reality: while interest in AI has skyrocketed, structured implementation and upskilling are still lagging.
Figure 3: Results from Growth Tribe’s AI Maturity poll.
Why Advancing AI Maturity is Urgent in 2025
- Adoption accelerates: Generative AI adoption has doubled year-over-year for two years (Microsoft & McKinsey 2024).
- Scaling is the new normal: 78% of organisations deployed AI at scale in 2024 (Stanford AI Index 2025).
- Compliance risk: EU AI Act requires workforce literacy—many firms are non-compliant.
⚠️ But now there’s urgent compliance pressure. The EU AI Act requires organisations to demonstrate that their workforce is AI literate. The February 2025 deadline has already passed, and many companies are still catching up.
👉 Growth Tribe offers a 2-hour AI Literacy Compliance training to help your teams meet EU AI Act requirements and kickstart your AI maturity journey.
👉 Get compliant with our AI Literacy course
- Productivity impact: AI boosts efficiency by 20–40% across tasks.
- Talent advantage: AI-skilled organisations attract and retain top talent.
How Growth Tribe Accelerates Every Stage
Growth Tribe supports companies at every level of AI Maturity.
We guide organisations through each stage with tailored learning paths:
- AI literacy foundations (compliance-ready)
- Custom activation pilots with measurable impact
- Structured experimentation frameworks
- Scaling strategies & governance
- Embedding AI culture & leadership for maturity
👉 Boost your team with Growth Tribe and connect with our experts to accelerate your AI journey.