About Responsible with AI
About Responsible with AI
Empowering professionals with Responsible with AI governance
Our Mission
Responsible with AI Training Platform is dedicated to making Responsible with AI education accessible to professionals across the built environment. We believe that understanding and implementing Responsible with AI practices is essential for maintaining professional standards, protecting clients, and building trust in an AI-driven world.
Innovation
Leading the future of responsible AI development through cutting-edge methodologies.
Education
Empowering professionals with comprehensive knowledge and practical skills.
Integrity
Upholding the highest standards of ethical AI practices and transparency.
Our Values
The core principles that guide our mission to democratize responsible AI education
Accessibility
Making Responsible with AI training accessible to everyone, regardless of technical background.
Professional Accountability
Ensuring professionals can confidently use AI while maintaining their professional standards and client trust.
Transparency
Promoting open, documented, and verifiable AI use in professional practice.
Innovation
Continuously evolving our framework to meet the changing needs of AI governance.
Our Impact
Learners enrolled in our course
Progressive levels from Awareness to Sponsor
Responsible with AI
Responsible with AI is our role-based learning pathway for responsible AI in professional practice. It's built on a simple philosophy: AI doesn't reduce responsibility it increases the need for judgement.
So we don't teach "prompt tricks". We teach the habits that make AI use safe, consistent, and defensible in real work.
Responsible with AI progresses in levels because organisations have different responsibilities at different points of work: the person using AI, the person reviewing AI-assisted output, the person overseeing how it's used, and the person sponsoring adoption. Each level focuses on what that role must be able to do and prove under scrutiny.
Our Approach
We deliver practical microlearning that fits around busy schedules and real projects. Lessons use built-environment scenarios, clear "green / amber / red" judgement, and simple checklists that learners can apply immediately. Every level includes knowledge checks and an end assessment so completion reflects capability, not just attendance.
(For organisations that need it, Responsible with AI can be paired with a governance framework that supports oversight and evidence.)
Micah Stennett
Founder, Feenix
Built environment professional, educator, and AI governance practitioner. Creator of the Responsible with AI programme.
Why we built this
Responsible with AI is built and operated by Feenix — a specialist education and AI governance practice focused entirely on the built environment. We created this programme because we kept seeing the same pattern: firms adopting AI tools rapidly, with no structured framework for doing so responsibly.
The consequences aren't abstract. A hallucinated regulation in a structural report. A confidential valuation brief exposed in a shared AI prompt. A disclosure obligation missed because no-one knew it existed. These aren't hypothetical risks — they're real professional liability events waiting to happen.
So we built the training we wished existed: grounded in RICS standards, aligned to ISO 42001 and the EU AI Act, and written for people who do real built environment work — not generic AI literacy content repackaged for a sector it wasn't designed for.
Our Journey
From Curiosity to Clarity
We didn't begin with a course. We began by analysing the AI landscape in the built environment the tools people use, the pressures they face, and the ways risk quietly enters everyday work.
Turning Standards into Something Usable
We mapped leading standards and guidance into one coherent overlay, so "responsible AI" is no longer abstract it becomes consistent expectations that can be trained, applied, and assessed.
Creating a Lifecycle That Works in Practice
We built an operational lifecycle that reflects how work actually flows: prompt → check → refine → review → disclose → escalate → evidence. The goal was simple: make good judgement repeatable.
Responsible with AI: Role-Based Capability
Then we built Responsible with AI to teach the lifecycle by responsibility level from safe everyday use to review, oversight, and sponsorship with assessments that confirm capability, not just completion.
Governance That Aligns, Not Distracts
Alongside training, we developed the governance layer to support the same lifecycle providing the structure and evidence habits organisations need to stay credible under scrutiny.
Ready to Get Started?
Join thousands of professionals learning Responsible with AI governance
Get in Touch
For questions about the platform, partnerships, or enterprise training options, please visit our contact page. Contact