The Confidence Cliff
The most dangerous moment in any human-AI partnership: when months of accumulated trust collapse after a single failure. Why it happens — and how to design the recovery.
Research, frameworks, and practical guidance for designers working with AI.
The most dangerous moment in any human-AI partnership: when months of accumulated trust collapse after a single failure. Why it happens — and how to design the recovery.
The Trust Journey Framework maps how trust forms, breaks, and recovers in AI systems. A practical five-stage design framework for building trustworthy AI experiences.
Master designing friction in AI systems for optimal user experiences. Learn when and how to add intentional friction to AI interfaces for safety and u
Original research into the gap between what designers say about AI and what they actually feel. Drawing on 80+ sources, 34 interviews, and nearly 100 direct quotes, this piece explores six tensions: the adoption paradox, pipeline severance, productivity paradox, homogenisation fear, identity crisis, and the irreplaceable human remainder.
A weighted, seven-dimension rubric for evaluating whether any AI-integrated product is trustworthy and human-centred, synthesised from Stanford HAI, Microsoft Research, IBM Design and the EU AI Act, and applied in practice against Miro, DeepSeek and seven leading LLMs.
Leading AI companies aren't optimising quality away. They're building in slower deliberate steps where it matters most to them.
AI burnout isn’t dramatic collapse: it’s silent erosion. Designers report feeling like project managers rather than creators, experiencing “boundless responsibility without control.” Riley Coleman diagnoses what makes AI burnout different from regular burnout and offers evidence-based strategies for protecting the pause where creative value lives.
This week, I’m sharing a deep dive into one of the most instructive case studies in AI trust I’ve seen – Figma Make Designs crisis and recovery. It matters because the frameworks they used (whether th
G’day, I learned this lesson the hard way at Telstra, and I’m watching design leaders repeat the same mistake with AI right now. We were rolling out Future Way of Working – a dozen major software and
I accidentally leaked company data to AI. Here are the 5 questions I wish I’d asked first. A framework for when NOT to use AI. By Riley Coleman.
G’day Gang, I wanted to quickly share with you the webinar I hosted yesterday on Design AI Users Actually Trust. Why this topic matters Put simply – AI Ethics is fundamentally about designing AI produ
The Quiet Revolution in Design There’s a quiet revolution happening in design. You might have felt it already – that subtle shift where our relationship with technology has begun to change in ways tha
How do design teams transform their practice with AI? Key Insights for Design Leaders The Crisis: When Design Consistency Meets AI Chaos By summer 2023, IBM faced a paradox that would challenge their
AI is Breaking Traditional Learning Paradigm That sick feeling when another AI breakthrough hits LinkedIn while you’re still processing last month’s announcement. That moment when you realise the gap
Key Insights for Design Leaders Workflow Shift: Design teams moved from creating mockups to collaborative prompt design New Design Paradigm: “Non-deterministic design style orchestrated by the AI” Ado
How to use AI for project preparation and design kickoffs while protecting confidential information, using the Golden Rule test and three practical AI tools.
Build the business case for trustworthy AI design with proven ROI frameworks. Master ethical AI implementation strategies and competitive advantages
How new US AI policy, stripping misinformation, DEI and climate references from NIST frameworks, is reshaping international AI cooperation.
Lessons from a year of teaching AI adoption to design teams: a three-stage framework for building real capability, not just tool familiarity.
Navigate AI design transformation with expert guidance. Proven strategies for leading creative teams through technological change and innovation.
The Goldilocks Formula for AI prompting: a two-step abstraction method and five-step privacy check for using AI without leaking corporate IP.
Five critical moments that build or break user trust in AI design, from first impressions through to how a system handles its own errors.
A tiered framework for effective human oversight of AI systems, designed to combat automation bias rather than assume it away.
Why AI ethics can't wait: AI systems are learning systems, and bias trained in early is far harder to remove later.
Build the business case for trustworthy AI design with proven ROI frameworks. Master ethical AI implementation strategies and competitive advantages f
Which major AI companies are removing ethical guardrails, from OpenAI's military use policy to Meta's content moderation cuts, and what design leaders can do.
Three Hollywood AI predictions that became reality in 2024: emotional dependency on AI companions, predictive policing, and workplace surveillance.
What autonomous AI agents, systems that think, decide and act without waiting for commands, mean for how designers work.
Developing judgment about when human decision-making should supersede AI recommendations. Features the Human Wisdom Check Framework with five reflection categories and three strategic shifts for organizations.
Four science-backed habits to prevent unhealthy over-reliance on AI tools, including a 24-hour cooling-off rule between AI generation and use.
How Riley Coleman went from design leader to founder of AI Flywheel, and what research with working professionals revealed about the AI adoption gap.
A reusable 7-dimension framework for evaluating whether any AI-integrated product is trustworthy and human-centred, worked through in full against Miro's Innovation Workspaces as a case study.
Six practical strategies for building transparent AI systems, from progressive disclosure and confidence indicators to natural language explanations.
Why AI ethics is a personal, practical concern, not an abstract debate: how algorithmic decisions affect your job, credit and security clearance.
Why UX research matters more, not less, as AI enters the design process: the human understanding gap AI can't close on its own.
A personal, unfiltered look at the opportunities and risks of generative AI: what excites Riley Coleman about AI, and what keeps them up at night.
Mental frameworks for individuals and organizations to thrive in AI-enhanced workplaces while maintaining human agency. Features the most powerful question for continuous learning with AI.