Strategic & Foundational
AI-augmented UX practice
An AI-augmented product design practice that combines research, experimentation, analytics and design into a repeatable operating model, reducing delivery effort while improving decision quality.
Improved productivity - Faster research, design and documentation cycles
Research, testing, analytics and iteration in one loop
AI accelerates insight generation, validation and documentation
Problem
As product complexity increased and organisational priorities required teams to do more with existing resources, traditional UX workflows became slower, more fragmented, and increasingly difficult to scale.
Research, design, content creation, stakeholder alignment, analytics, experimentation, and development handover were often treated as separate activities, creating delays, rework, and inconsistent outcomes.
The challenge was not simply improving design efficiency.
The challenge was creating a repeatable system that could improve decision quality, increase delivery speed, and maintain human-centred design principles while leveraging AI responsibly.
My role
I developed and continuously evolved an AI-augmented product design practice that integrates research, content strategy, UX design, analytics, experimentation, and development workflows.
Rather than treating AI as a replacement for design expertise, I positioned it as an accelerator for insight generation, validation, documentation, and continuous optimisation.
This enabled me to operate across the responsibilities typically shared across UX researchers, product designers, content specialists, analysts, and experimentation teams.
The challenge
Create a repeatable system that could improve decision quality, increase delivery speed, and maintain human-centred design principles while leveraging AI platforms.
What I did
Research Acceleration
Faster insight generation
Customer synthesis
Competitor analysis
AI-Assisted Content Design
UX writing
Content frameworks
Documentation
Decision Support Analytics
Behaviour analysis
Experimentation
Measurement
Toolchain integration
Connected research, design and analytics workflows
Reduced manual effort
Faster delivery cycles
Design system readiness
AI-ready design standards
Improved consistency
Better developer handover
Governance & Validation
Research-backed recommendations
Analytics validation
Human decision-making oversight
Design process evolution
Through experimentation and real-world delivery, the practice evolved from a sequential design process into a continuous learning loop. The objective was never speed alone, it was improving decision quality while reducing unnecessary effort across the product lifecycle.
Before
3-4 days
Research
Design
Documentation
Handover
After
1 day
AI-Integrated workflow
AI across the product lifecycle
Research
AI-assisted synthesis, competitor analysis and behavioural evidence.
Explore
Decision framing, content strategy and quick concept generation.
Validate
User testing, prototype review and product alignment loops.
Refine
Analytics, Hotjar evidence and experiment recommendations.
Build + deploy
Handover, design system standards and continuous optimisation.
Tools
Outcome
The result was a repeatable operating model that reduced delivery effort, improved decision quality, and created a stronger culture of experimentation across research, design, analytics and development.
All managed and coordinated as one UX Designer across 23 Developers, 4 Product Owners, 300 people across the company.