When people talk about AI adoption, the conversation often drifts toward hype: promises of exponential change, or fears of runaway automation. But to make sense of how AI actually works inside organizations, we need a grounded framework.
Our maturity model builds on three sources: Zapier’s AI adoption playbook, which treats AI fluency as a baseline for every new hire, the Gen-AI Applications course at Singapore Management University (SMU), which outlines how organizations evolve from using AI tools to managing them at scale, and Coen Tan’s psychology of AI adoption, which highlights the different human responses to AI, from denial and anxiety to balance and creation.
Together, these perspectives show how teams move from ignoring AI entirely to embedding it in strategy, governance, and long-term management, and how individuals travel their own psychological journeys at the same time.
At Zapier, AI fluency is already a hiring requirement. Other companies are moving in the same direction. The question is: where are you on this maturity curve?
Why This Matters
Every team has a different relationship with AI. Engineers see it as an extension of their development cycle. Marketers treat it as a campaign amplifier. Educators wrestle with the balance between efficiency and ethics.
What unites them is the arc of maturity:
Unacceptable: Resistance and skepticism
Use: Hands-on experimentation
Create: Building AI into daily work
Deploy: Launching AI features and workflows safely
Govern: Setting policies, guardrails, and ethical standards
Manage: Operationalizing AI at scale across the organization
This is not a linear checklist. Teams move at different speeds, sometimes jumping ahead in one area while lagging in another. What matters is having a shared language to describe where you are, and where you want to go.
Lessons from Zapier
Zapier’s playbook shows that AI fluency is already expected. They assess candidates across levels, from Unacceptable (resistant to AI) through Transformative (rethinking strategy with AI).
Hiring: AI is now part of recruiter screens and skills assessments
Onboarding: New hires learn to build AI-powered workflows from day one
Culture: Build the robot is no longer just automation, it is about AI-powered impact
What this tells us: maturity is not just about tools. It is about people, culture, and process.
The Gen-AI Maturity Model
Unacceptable
At this stage, teams resist or ignore AI. Engineers call coding assistants “too risky” and rely only on Stack Overflow. Product managers dismiss AI as hype and draft PRDs without AI input. Support staff handle tickets manually with no automation. HR screens resumes one by one, distrusting AI tools. Marketing ignores AI-driven testing or personalization. Educators rely fully on manual lesson prep, ignoring emerging AI support.
1. Use
Here, teams start experimenting. Engineers use ChatGPT or Copilot for code snippets and unit tests, explaining how they validate outputs. Product managers draft PRDs, story maps, and synthesize user-interview notes. Support staff summarize tickets and draft replies faster. HR drafts interview guides and uses AI to summarize resumes. Marketing drafts headlines and customer stories with AI assistance. Educators explore lesson prep, quizzes, and learner Q&A with basic tools.
2. Create
At the creation stage, AI becomes part of building. Engineers develop prompt libraries, scripts, and test generators. Product teams build prototypes with AI features, choosing models based on cost, latency, or accuracy. Support staff design workflows in Zapier/Capabara/Make to triage tickets and auto-tag CRM records. HR teams create onboarding documents and pilot AI resume-screening with bias checks. Marketing runs AI A/B tests, generating content variants and campaign ideas. Educators generate differentiated lesson outlines, quizzes, and feedback personalization.
3. Deploy
This stage is about scaling safely. Engineers chain models with fallback logic and add evaluation tests to catch hallucinations. Product teams ship AI-powered features with human-in-the-loop checks, tracking ROI such as reduced time-to-insight. Support integrates AI triage in production to cut response times, with escalation pathways. HR integrates AI pipelines with human approval, achieving faster shortlists. Marketing deploys personalization engines that improve CTR. Educators build structured AI workflows for lesson planning, feedback, and simulations, integrated into coaching and assessment.
4. Govern
Now organizations set safeguards. Engineers define privacy rules, review processes, and documentation for AI use. Product teams align AI features with UX, user consent, and data-retention policies. Support monitors tone and bias while ensuring PII compliance. HR formalizes AI hiring policies, monitoring fairness and legal compliance. Marketing audits language for bias and brand safety while ensuring IP compliance. Educators create ethical and privacy safeguards, aligning AI with pedagogy, privacy laws, and education standards.
5. Manage
At the highest stage, AI is embedded across the organization. Engineers run full AI SDLC pipelines with monitoring, dashboards, and continuous improvement. Product teams own AI roadmaps, launching proprietary models and driving pricing innovation. Support teams run AI dashboards balancing cost and customer experience, retraining prompts regularly. HR trains business partners on safe AI use and manages time-to-hire dashboards. Marketing builds full AI campaign engines, leads quarterly training, and sets strategic roadmaps. Educators run LMS-integrated AI learning systems and develop sustainable strategies for scale, human-AI collaboration, and long-term inclusion.
AI Adoption is Not Just About Technology, It is About Psychology
When people first encounter AI, they do not just ask “Can this tool help me?” They ask, often unconsciously, “What does this mean for my identity, my skills, and my place in the future of work?”
That is why understanding AI maturity requires two lenses:
Organizational Maturity: how teams and companies adopt AI across roles
Human Psychology: how individuals respond emotionally and behaviorally to AI
When we put these together, we get a fuller picture of why adoption succeeds in some places, stalls in others, and transforms only when both sides align.
The Human Side of AI Adoption
As Coen Tan has pointed out, people do not fall neatly into pro or anti AI camps. Instead, they travel through different psychological states of adoption, which map surprisingly well onto the maturity model.
AI Ostriches (Denial): They bury their heads in the sand, dismissing AI or avoiding it altogether. In maturity terms, they are in the Unacceptable stage. Their risk is being left behind.
Anxious Chasers (Anxiety): They try every tool without strategy, driven by fear of irrelevance. This lines up with early Use, lots of activity but little structure.
Early Adopters (Excitement): Tech-savvy and curious, quick to try tools. They sit at the boundary of Use and Create, but risk shiny-object syndrome.
AI Braggarts (Excitement): They signal adoption loudly, “Look what I built in 5 minutes!”, but may lack depth. They also sit in Use/Create, sometimes surrendering agency to the tools.
Conscious AI Adaptors (Future-Proof): Balanced and intentional. They integrate AI with ethics and reflection. This matches Deploy/Govern, building responsibly, not just fast.
AI Architects (Creation): Builders, researchers, and leaders shaping AI ecosystems. They represent the Manage stage, aligning policy with innovation and designing for long-term impact.
Bringing Both Lenses Together
What does this mean in practice?
A company may say it is at the Deploy stage, rolling out AI workflows, but if half the team are still Ostriches or Anxious Chasers, adoption will stall.
An individual may self-identify as an Early Adopter, but unless the organization has Govern structures in place, their enthusiasm will not translate into scalable transformation.
Real maturity happens when roles and psychology align, when leaders create space for Conscious Adaptors and AI Architects to thrive, while helping Ostriches and Anxious Chasers move forward without shame or fear.
Your Reflection
Where is your team on the maturity roadmap?
Where are you on the psychology spectrum?
And most importantly, how do those two interact?
Because AI maturity is not just about deploying models. It is about guiding people through denial, anxiety, excitement, and towards balance and creation.
Conclusion
Gen-AI adoption is not a single leap. It is a series of steps.
Understanding where your teams sit, and how to move them forward, is what separates experimentation from transformation.
AI maturity is not about keeping up with the latest model release. It is about building reliable systems, empowered teams, and sustainable processes that make AI an ordinary, trusted part of work. And it is about helping people move from Ostriches and Anxious Chasers to Conscious Adaptors and Architects.
This maturity model draws from Zapier’s real-world playbook, Singapore Management University’s Gen-AI Applications course, and Coen Tan’s psychology of AI adoption. Together, they provide a grounded way to understand where you are today, and what it takes to move forward.
That is the real challenge, and the real opportunity.
