Work

Not simply how designers use AI, but how design organisations should work when AI becomes part of the delivery infrastructure.

My work explores how complex design activities can be decomposed into reusable skills, specialized agents, orchestration layers, validation mechanisms, and human decision points.

The result is systems that carry teams from ambiguous requirements to implementation-ready product experiences with greater speed, consistency, and traceability.

I work across design, code, and AI orchestration while remaining design-led: product direction, experience architecture, quality, governance, and team enablement, rather than positioning myself as a full-stack engineer.

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Focus

  • 01

    Design Strategy & Systems Thinking

    Working beyond individual screens to define the systems, principles, workflows, and experience models that let products and teams scale.

    • Translating ambiguous business and technical problems into clear design direction
    • Establishing reusable product and UX frameworks
    • Connecting experience decisions across journeys, platforms, markets, and systems
    • Balancing user experience, technical feasibility, business priorities, and delivery constraints
    • Creating clarity for designers, product managers, engineers, and stakeholders

  • 02

    AI-Native Design Systems & Orchestration

    Designing agentic systems for design work, where AI capabilities are structured rather than used as isolated prompts.

    • Multi-agent UX orchestration and reusable design skills
    • Governed AI pipelines with persistent and hybrid context
    • Human-in-the-loop workflows and quality gates
    • Evaluation rubrics, self-critique, and iteration mechanisms
    • Design-to-code workflows and implementation fidelity validation

  • 03

    Organizational Leverage

    Identifying where AI creates leverage across a design organisation, not just personal productivity.

    • Reducing repetitive design work and codifying reusable design knowledge
    • Improving consistency across teams; accelerating exploration and validation
    • Creating shared AI-enabled ways of working
    • Establishing governance around generated outputs
    • Moving teams from prompting toward structured orchestration

  • 04

    Enterprise Product Leadership

    An AI-native practice grounded in real enterprise product delivery, usually where requirements and constraints conflict.

    • Global consumer platforms; multi-brand, multi-market ecosystems
    • Transactional commerce, loyalty and engagement, warehouse management
    • Financial and institutional platforms; CMS-driven experiences
    • Design systems across responsive web and native mobile
    • Emerging platform capabilities: iOS Live Activities, Android live updates

Method

In general

  1. 01 Understand the system
  2. 02 Frame the problem
  3. 03 Create direction
  4. 04 Enable execution
  5. 05 Evaluate outcomes
  6. 06 Codify what should become reusable

In AI-native work

  1. 01 Context
  2. 02 Decomposition
  3. 03 Orchestration
  4. 04 Generation
  5. 05 Evaluation
  6. 06 Human judgment
  7. 07 Iteration
  8. 08 Delivery

I deliberately separate automation from delegation.

  • Automate Some problems should simply be automated.
  • Delegate Some should be delegated to specialized AI agents.
  • Collaborate Some require collaboration between humans and AI.
  • Decide And some decisions should remain explicitly human.

Design leadership in an AI-native organization requires knowing the difference.

Enterprise Ground

The delivery experience the AI‑native work stands on.

At Restaurant Brands International I led and contributed across major initiatives spanning Burger King, Popeyes, and Tim Hortons international digital experiences, work that required coordination across designers, product teams, engineering, architecture, platform constraints, global markets, and multiple value streams.

Initiatives

  • Prepaid Offers discovery
  • Core Front-End Design System V2
  • CMS-driven App Takeovers
  • Homepage and loyalty discovery
  • iOS Live Activities
  • Android live-update notifications

Contribution

  • Creating frameworks that others can execute
  • Resolving ambiguity before it reaches delivery
  • Establishing design direction
  • Helping less experienced designers navigate complex constraints
  • Reviewing and improving design quality
  • Aligning design decisions with engineering realities
  • Building reusable systems instead of project-specific solutions

At level

At Principal / Staff Level

  • Operating across multiple product and organizational boundaries
  • Systems thinking beyond feature-level design
  • Comfort with technically complex and ambiguous problem spaces
  • Establishing direction without requiring complete requirements
  • Frameworks and reusable systems that increase other designers’ leverage
  • Design judgment spanning product, interaction, systems, delivery, and implementation quality
  • Connecting emerging AI capabilities to practical enterprise design problems
  • Influence through clarity, frameworks, facilitation, critique, and technical credibility

As a Design Manager

  • Establishing strong design direction while letting designers own execution
  • Coaching designers through unfamiliar or technically complex problems
  • Building repeatable practice rather than dependence on individual expertise
  • Raising quality through frameworks, critique, governance, and clear expectations
  • Building AI literacy and AI-native working practice within teams
  • Connecting capability development to real delivery opportunities
  • Creating room to experiment without compromising product quality or accountability

Open to conversation

Let’s talk AI-native design.

I am targeting Principal Designer, Staff Designer, and Design Manager roles where senior designers shape systems, standards, team capability, and the mechanisms through which design itself gets delivered.

Enterprise product design + design leadership + systems thinking + technical fluency + agentic AI orchestration

avadhesh.ux@gmail.com Download resume See the work