Unified AI Product Strategy
Establishing Northstar vision, design governance, and scalable ecosystem for an AI-first platform.
About the project
Company
Level Access
Timeline
2026
My role
Product strategy, Product designer, AI UX
Challenge
Context
As AI capabilities were rapidly integrated into the platform, teams were building in silos without a defined "AI-first" standard. There was no unified strategy dictating how these features should coexist, scale, or look, creating ambiguity and a fragmented user experience.
Design governance
Establishing the rules for cross-functional teams building AI features.
Ecosystem vision
Defining the Northstar strategy for how distinct AI agents interact.
Ambiguity management
Validating theoretical strategy through tangible, interactive prototypes.
Long-term AI design governance foundation
To transform isolated AI initiatives into a unified platform layer, I designed a scalable long-term AI governance framework. My focus was establishing a shared cross-functional foundation to align product and engineering teams on what, how, and why we build AI.
Aligning product concept
Developed an experience vision defining the Identity, Positioning, Principles. Interaction model, Scalability, Risks and more as the foundation for the strategy.
The AI product glossary
Standardized naming conventions for all agentic interaction modes so teams could communicate without friction when working with AI as well as when building AI features.
A shared design system for AI
Created reusable component guidelines and explicit behavior rules (such as Transparent context use, Human-in-the-loop triggers, and Output anatomy) to eliminate fragmented experiences.
The vision execution
Due to confidentiality, I can only share a limited view of the project. Here is how I structured the ecosystem.
Unified AI platform architecture
The realization of the unified AI vision strategy is an architected agentic framework that consolidates all intelligent workflows into a single Level AI experience. Powered by platform-wide context grounding, it delivers highly relevant assistance directly within the user's active environment without forcing context switches. Rather than scattering outputs across modules, Level AI generates and routes all artifacts to a centralized hub, creating a seamless loop between conversation, creation, and long-term retrieval.
Platform-wide search
AI integrated experience
Powerful context capabilities
Platform-wide context grounding
Unified agentic workspace
Instead of separate interfaces for every AI capability, Level AI acts as a single orchestrator. It dynamically routes user requests to specialized agents behind the scenes, providing a seamless, unfragmented multi-agent experience.
What I did
Architected the agentic framework as the core of the product strategy, integrating multi-agent workflows into one continuous Level AI experience.
Implemented platform-wide context capabilities, enabling the AI to read and adapt to the user’s active workspace and tasks.
Designed a centralized artifact hub directly in the platform as a single, systemic destination for generating, accessing, and managing all AI-generated assets.
Impact
Eliminated UI fragmentation and reduced cognitive load by providing a single, predictable interaction model for all specialized AI tasks.
Dramatically improved the relevance of AI interactions by anchoring conversations in deep, system-wide context.
Validated the vision strategy in practice, establishing this flow as a foundational, scalable blueprint for all future AI integrations.
AI-Driven onboarding and setup
To ensure a smooth transition to the new AI-first platform, onboarding needed to deliver immediate utility. I designed a hybrid setup flow that combines traditional inputs with conversational AI, using system context to minimize manual entry and cognitive load. This flow acts as a guided introduction to the platform's core Find-Fix-Prove loop, helping users execute their first real task during setup, proving product's value from day one.
What I did
Prototyped a guided, personality-driven AI setup flow that collects role-based focus areas and organization data during initialization, giving users immediate onboarding context.
Built interactive prototypes of a dynamic, AI-driven onboarding workflow to demonstrate a seamless transition to the new UI.
Impact
Reduced estimated user setup time by 50% during usability testing, using personalized progressive disclosure to validate and launch high-impact product experiences with ease.
Proved and close the Find-Fix-Prove loop to users from day one.
Considerations
Mitigating 'Black box' anxiety with UI scaffolding
Users mistrust AI when outcomes feel unpredictable. I created a strict principle of Understand - Recommend - Preview - Act - Learn across the platform, forcing the system to explicitly preview expected changes, assume permissions, and require explicit confirmation for consequential actions.
Navigating ambiguity and stakeholder alignment
To eliminate ambiguity and fragmented UX when building AI products, I authored a centralized AI glossary and governance framework. By defining strict design standards and a shared vocabulary, this core hub provided the necessary guardrails for Engineering, Product, and Design to execute fast, independently, and cohesively.
Results
Metrics
Roadmap approval
Successfully translated abstract platform architecture into a tangible business case, securing executive buy-in for the upcoming product cycle.
Cross-functional alignment
Next steps
Global governance rollout
Systematically deploy the authored AI design standards across all remaining product teams.
Deepen multi-agent orchestration
Expand the Northstar strategy to define exactly how specialized AI agents collaborate on complex, cross-document accessibility audits.
Key takeaways
Governance is the bedrock of scale
Authoring clear standards and product glossaries proved just as critical as the UI itself. Without rules of engagement, multiple pods building AI features will inevitably create a fragmented UX.
Systems thinking beats isolated features
Designing for an AI-first platform means orchestrating continuous flows, agent hand-offs, and capabilities across the entire ecosystem, not just polishing single touchpoints.
Prototypes secure executive buy-in
Translating abstract architectural strategies into tangible, interactive workflows provided the hard usability data needed to validate the Northstar vision with leadership.
Ambiguity requires defined constraints
In a rapidly shifting AI landscape, creating a shared glossary and unified design standards gave cross-functional teams the confidence to innovate quickly without breaking the overarching platform strategy.
