Meet Your Sprint Leads
Expert guidance combining design systems mastery with responsible AI practice
Mark Reynolds
Design Systems Expert
Mark is a design systems strategist and the founder of Atomle, with deep expertise in building scalable design infrastructure for enterprise organisations. He has worked with many of the fortune 500, scaling design for the likes of Qualcomm and Volvo Cars. Mark brings a practical, architecture-first approach to preparing design systems for AI-assisted workflows.
Sara Portell
Human-Centric Responsible AI Expert
Sara is a behavioural scientist and AI ethics practitioner with over 17 years of experience in product strategy and innovation at global technology organisations including Shopify and Expedia. She holds an MSc in Strategy and Management (ESSEC), an MSc in Behavioural Science (LSE), and is completing doctoral research in Psychology and AI. An Oxford-certified AI ethicist and ISO/IEC 42001 auditor, Sara founded HCRAI to help organisations understand how people interact with AI systems and make informed decisions around safety, ethics, and governance.
Who It's For
This sprint is designed for design system teams, design platform teams, product design leaders, and digital transformation teams.
Ideal organisations:
- Have an established design system
- Support multiple product teams
- Are exploring AI for design or UI generation
- Care about brand consistency and accessibility
What We Analyse
Design System Structure
Token architecture, component coverage, variant and state modelling, pattern documentation
Governance and Accessibility
Brand rule enforcement, accessibility standards, consistency across teams, design decision ownership
AI Readiness
Machine-readability of assets, compatibility with RAG pipelines, code alignment with design components
Design system AI readiness quiz
How ready is your design system for AI?
Take our 2-minute assessment to discover where your design system stands on the path to AI-native workflows. Get instant insights across component coverage, design-dev parity, governance, and automation readiness.
What percentage of your product UI uses shared components?
Design Systems for Responsible Human-AI Interaction
A scalable control layer for human-AI interactions that reduces product, regulatory, and reputational risk while enabling teams to ship faster. This is how organisations maintain control, accountability, and safety as AI behaviour becomes adaptive and agentic.
Reduce Risk Exposure
Enforce human-AI interaction safeguards aligned with evolving requirements like the EU AI Act and duty of care regulations.
Prevent Harm at Scale
Detect and prevent silent interaction-level harm before it becomes a reputational or regulatory incident.
Ship Faster
Replace repeated debates with pre-approved interaction patterns and clear escalation paths.
Interface-agnostic
UI, voice, agents, ambient
Model-agnostic
Any vendor or architecture
Tool-agnostic
Figma, no-code, AI copilots
Regulation-adaptive
Principle to governance mapping
What Responsible HAI Design Covers
Beyond traditional design systems to address human-AI interaction challenges
Design Principles
Principles that incorporate human-centricity, responsible AI practices, and safety including user agency, non-deceptive interaction, and safety under uncertainty.
AI-Ready Foundations
Foundational rules for tone, feedback, and timing that apply consistently across AI responses, system-initiated actions, and handovers.
Component Library
Components for AI suggestions including review-before-apply flows, pause/stop/undo automation, and representation of confidence or uncertainty.
Interaction Patterns
Patterns that preserve user agency, make errors recoverable, prevent silent harm, and remain safe under repeated use and system change.
Content Standards
Standards governing wording, presentation, and attribution of AI-generated content to prevent misleading confidence or user misinterpretation.
Governance Layer
Design checkpoints for HAI with defined escalation points, cross-functional oversight, and versioning for AI behaviour changes.
Sprint Timeline
A focused 2-week engagement to assess your AI readiness
Discovery and System Review
- Stakeholder kickoff call
- Design system audit
- Token and component review
- Documentation assessment
Analysis and Roadmap
- Architecture mapping
- Gap identification
- Design OS readiness scoring
- Roadmap creation
What You'll Receive
At the end of the sprint, you'll have a clear AI readiness assessment of your design system, a gap analysis for machine-readability and governance, and a prioritised roadmap to activate conversational UI generation.
- AtomleOS Readiness Report
- Design System Gap Analysis
- Reference Architecture Overview
- Activation Roadmap
Why Teams Choose This Sprint
- Avoid AI experimentation risks
- Prepare design systems for scale
- Reduce manual UI work
- Create a foundation for creative acceleration
This gives teams a concrete path forward without committing to a full implementation. There is no obligation to continue beyond the sprint.
Why Outsource to Us?
We deliver in 2 weeks what typically takes organisations 6 months internally
Typical timeline
Up to 6 months
Minimum investment
$250,000+ USD
£200,000+ GBP
€230,000+ EUR
Challenges
- -Requires hiring or reallocating specialist resources
- -Steep learning curve for AI + design system integration
- -Risk of costly trial-and-error approaches
- -Opportunity cost of delayed AI adoption
Delivery timeline
2 weeks
Fixed investment
$22,999 USD
£16,799 GBP + VAT
€19,399 EUR
Why we can deliver faster
- Design system experts with Fortune 500 experience
- Proven AI readiness assessment methodology
- Responsible AI governance expertise
- Focused engagement with no distractions
Save over 90% in time and cost. Our combined expertise in design systems and AI allows us to deliver comprehensive assessments and actionable roadmaps at a fraction of what it would cost to build this capability internally.
Register Your Interest
Fill in your details below and we'll be in touch to discuss how the Readiness Sprint can help your team.
Format: Remote | Duration: 2 weeks