Our Approach

Making AI work for everyone requires more than just good technology—it needs the right mix of motivation, capability, and opportunity. We've discovered a powerful new approach that combines the best of human-centered innovation with structured collaboration to create lasting positive change.

We call this approach Collective Design. It's a practice that unites the human-centered, iterative innovation of Design Thinking with the structured, large-scale coordination of Collective Impact.

The AI Innovation Challenge

Creating ethical AI solutions that serve society requires breaking through fundamental challenges around innovation and adoption:

What We Need

  • Deep understanding of diverse community needs and contexts
  • Ways to build motivation and capability together
  • Safe spaces for experimentation and learning
  • Systematic approaches to scaling what works
  • Methods to build and maintain trust across stakeholders

What Often Happens

  • Solutions designed without real community input
  • Promising pilots that never scale
  • Fragmented efforts across sectors
  • Loss of momentum after initial excitement
  • Eroding trust from failed collaborations

Breaking Through Inertia

Traditional approaches to AI development and governance get trapped in catch-22s that make progress difficult:

The Innovation Cycle

When innovation happens far from impact, we create a cycle where:

  • Taking up new technologies takes motivation and learning new skills
  • To commit time and energy to new skills, most people need to understand the opportunities for them
  • But to build relatable solutions, creators need to understand the needs of communities
  • Breaking this inertia takes new ways to bring creators and communities together

The Governance Cycle

High-level principles prove hard to implement, creating a cycle where:

  • Principles-based standards and obligations must be translated into real-world, context-relevant practices
  • Shaping and refining real-world practices takes real-world experimentation
  • But many are waiting for clear, reliable practices before they consider seriously experimenting with AI
  • Breaking this inertia takes new ways to shape, test, refine and share practices safely

Two Powerful Approaches

Two transformative approaches have evolved to address different aspects of these challenges:

Design Thinking

A human-centered approach to innovation that draws from the designer's toolkit to integrate human needs, technological possibilities, and requirements for success.

Key Elements

  • Empathize with users to understand real needs
  • Define problems worth solving
  • Ideate possible solutions
  • Prototype to make ideas tangible
  • Test and refine through feedback

Strengths

  • Keeps innovation grounded in human needs
  • Makes complex problems manageable
  • Enables rapid learning and adaptation
  • Creates buy-in through participation

Collective Impact

A structured form of collaboration that brings organizations together around a common agenda to solve complex social problems at scale.

Key Elements

  • Common agenda across stakeholders
  • Shared measurement systems
  • Mutually reinforcing activities
  • Continuous communication
  • Backbone support infrastructure

Strengths

  • Enables coordination at scale
  • Creates sustainable change
  • Aligns diverse stakeholders
  • Builds lasting infrastructure

But Each Has Its Limits

When applied to our context, both approaches hit critical limitations:

The Innovation Reality

  • Real innovation increasingly requires multiple perspectives
  • Complex challenges need diverse capabilities
  • Solutions must work across different contexts
  • Individual organizations can't drive systemic change alone
Design Thinking's Challenges
  • Can struggle to maintain momentum beyond initial sprints
  • Hard to coordinate across multiple independent teams
  • Lacks structure for sustained, multi-stakeholder efforts
  • Solutions can remain localized without scaling infrastructure

The Community Reality

  • Community needs often require new solutions
  • Change must happen at both local and systemic levels
  • Solutions need to evolve with community feedback
  • Impact requires coordinated innovation across sectors
Collective Impact's Challenges
  • Can become overly process-focused and bureaucratic
  • Often struggles to maintain genuine community connection
  • Lacks practical methods for rapid innovation
  • Can be slow to adapt to changing needs

Introducing Collective Design

We've discovered that these approaches are complementary in a profound way. Design thinking provides the "how" of collective impact, while collective impact creates the infrastructure that makes design thinking work at scale. Together, they create something we call "Collective Design" - a powerful new approach to ethical innovation that combines the best of both worlds.

Design Thinking as the 'How'

Human-Centered Innovation

Design thinking provides the practical methods and mindsets needed to create AI solutions that truly work for people.

Common Agenda

  • Empathy research reveals real needs and opportunities
  • Journey mapping shows systemic pain points
  • Co-creation aligns diverse stakeholder goals
  • Synthesis finds patterns across perspectives

Shared Measurement

  • User research identifies what matters
  • Prototyping reveals what to measure
  • Testing validates measurement approaches
  • Iteration improves metrics over time

Reinforcing Activities

  • Design sprints coordinate diverse actions
  • Prototyping aligns different workstreams
  • Testing shows how efforts combine
  • Iteration helps activities evolve together

Continuous Communication

  • User stories create shared understanding
  • Visual tools make complex ideas tangible
  • Prototypes facilitate meaningful dialogue
  • Regular testing builds trust through transparency

Collective Impact as the Infrastructure

Making Design Thinking Work at Scale

Collective impact provides the sustained structure and support that allows design thinking to work beyond individual products - creating lasting change across entire ecosystems.

Steering Committee

  • Ensures strategic direction for design efforts
  • Aligns innovation with systemic goals
  • Maintains focus on human impact
  • Coordinates resources across initiatives

Action Clusters

  • Create spaces for collaborative design
  • Enable cross-sector prototyping
  • Facilitate shared learning
  • Scale successful approaches

Backbone Support

  • Provides design thinking tools and methods
  • Facilitates collaborative sessions
  • Captures and shares learning
  • Maintains momentum between sprints

Partner Network

  • Brings diverse perspectives to design
  • Enables testing across contexts
  • Provides implementation pathways
  • Creates feedback loops

Collective Design

Human-Centred Innovation, Community-Wide Impact

When combined, these approaches solve each other's limitations. Design thinking makes collective impact more human-centered and adaptive. Collective impact makes design thinking more systematic and sustainable.

Shared Principles

  • Start with real human needs
  • Bring diverse perspectives together
  • Learn through practical action
  • Evolve based on feedback
  • Create lasting systemic change

Mutual Reinforcement

  • Structure enables sustained innovation
  • Human focus keeps structure relevant
  • Coordination amplifies individual efforts
  • Rapid learning improves the system
  • Success breeds deeper collaboration

Why This Matters for AI Innovation

Making AI work for everyone is a big challenge. Our approach helps solve this by:

  • Connecting Big and Small: We link individual projects to bigger changes. When someone solves a problem locally, we help turn that into something that can help many others.
  • Making it Easy to Help: Whether you're an expert or just getting started, we create clear paths for you to contribute in ways that match your skills and interests.
  • Growing and Learning Together: We move quickly to test new ideas, on foundations for collaboration that lasts. This means we can adapt fast while keeping what works.
  • Building Real Trust: We combine expert knowledge with real community input. This means our solutions aren't just technically sound - they actually work for the people who'll use them.

This approach guides everything we do - from how we plan to how we work each day. We're not just another network. We're building an alliance of action where everyone from tech experts to community leaders can work together to make AI better serve society. It's about combining deep technical expertise with real-world understanding to create solutions that work for everyone.