AI Design Strategy

AI-Powered Solution Development Platform

Transforming how businesses build custom AI solutions through strategic design leadership

AI platform entry point — users describe their business problem in natural language

The Challenge

Businesses were forcing SaaS products to fit their needs instead of getting custom AI solutions. Our platform had all the pieces — Processes, Workflows, Apps, Forms, Data, and Document Generation — but they weren't working together to give AI the business context it needed.

Role Product Design Manager
Timeline 2025 to present
Team 1 lead designer, 6 engineers, 1 PM, multiple senior leadership and C-suite stakeholders
Before — Fragmented
Processes
Workflows
Apps & Forms
Data
Document Generation

Five capabilities, no shared context. AI has no business intelligence to draw from.

After — Unified Platform
AI Solution LayerContext-aware across all capabilities
Processes Workflows Apps Forms Data Docs

All capabilities feed business context upward — AI can now generate genuinely custom solutions.

The strategic opportunity: unify six platform capabilities so AI has the business context to build solutions that actually fit

"The hardest part isn't the AI generation — it's designing the right amount of AI output that humans can meaningfully review and act on."

Strategic Approach

How I led this

1. Identifying the Market Opportunity

  • Understood gap where businesses needed custom AI solutions
  • Leveraged our platform's unique advantage: combining all capabilities to provide AI with rich business context
  • Positioned us for competitive differentiation in AI-powered business solutions
AI Solution EngineRich business context from all platform layers
Processes
Workflows
Apps
Forms
Data
Doc Generation

Our competitive advantage: no other platform combines all six. Together they give AI the business context to build solutions that actually fit.

2. Establishing Human-AI Partnership Principles

"The hardest part isn't the AI generation — it's designing the right amount of AI output that humans can meaningfully review and act on."

  • Challenged industry assumption that "AI should do everything"
  • Established principle: AI generates, humans guide
  • Designed granular interaction patterns for different personas without cognitive overload
Business User
Describes problem in plain language, reviews AI-generated solution as a whole
natural language
AI Generation Layer
Generates assets, workflows, and data structures from business context
structured output
Technical User
Validates and refines at the component level, customizes generated assets
Same AI output, different entry points. The design challenge was ensuring neither user had to work at the wrong level of granularity.

3. Leading Through Complexity

  • Stakeholder Management: Guided C-suite presentations and provided real-time meeting direction
  • Team Protection: Defended small, empowered teams to ensure streamlined leadership understanding and prevent scope creep
  • Technical Partnership: Collaborated with engineering to ensure designs matched AI capabilities possible to deliver
  • User Focus: Maintained continuous learning loop despite rapid AI technology changes
Design Process

From strategy to execution

Discovery & Alignment

  • Built continuous feedback loop with internal and external users
  • Balanced quick ideation for stakeholder alignment with engineering feasibility
  • Created shared vision across multiple senior stakeholders

Strategy to Execution

  • Elevated established senior designer to higher-visibility role, coaching them through executive presentations
  • Championed designer's ideas and created space for them to focus wholly on AI exploration
  • Mentored designer through executive stakeholder management, positioning them for promotion
  • Set team workloads and negotiated scope with PM to shape realistic roadmaps

Key Design Decisions

  • Adaptive AI interaction modes: Combined chat-style AI for open-ended exploration, inline AI for contextual assistance, and quick manual edits for finishing touches — matching users' mental models as projects evolve from ambiguous to refined
  • Progressive personalization strategy: Created different entry points for technical vs. non-technical users, with plans for role-based prompts and customizable AI behavior to meet each persona where they are
  • Human-in-the-loop validation: Designed preview and validation steps that mimic peer collaboration, allowing early error detection and correction to prevent hallucination cascades and maintain user confidence

Adaptive AI Interaction Model

Exploration
Open-ended problem framing — users describe in natural language
Chat-style AI
Refinement
AI assists in context — suggestions appear where the user is already working
Inline AI
Finalization
Precise manual control — user makes targeted edits before committing
Manual Edit

Each mode matches the user's mental state as projects evolve from ambiguous to refined — AI steps back as clarity increases

Impact & Outcomes

What we shipped and what changed

1
Designer Promoted
3
Interaction Modes
✓
Patterns Adopted
6→1
Capabilities Unified

Business Impact

  • Will shape platform product cohesion across all capabilities
  • Informs AI asset generation strategy company-wide
  • Fundamentally evolves how we communicate as an integrated platform

Team Development

  • Elevated senior designer to executive-facing role, resulting in promotion trajectory
  • Established design patterns now used across multiple teams
  • Created framework for future AI feature development
  • Built team capability in AI design through focused mentorship
Reflection

What I learned about leading AI design: Success came from protecting the team's focus while managing upward to keep stakeholders aligned. The intersection of AI capabilities and human needs requires constant recalibration, and that only works when the team has the space to actually do it.

What I'd do differently: Keep the team smaller and more focused from the start. Too many stakeholders got involved too early, making it hard to narrow scope and costing us valuable learning time through mid-development changes. Small, empowered teams with clear decision rights actually move faster.

Visual Design

What we built

Design completed under my coaching and direction by a Senior Designer who was promoted to Staff Designer.

AI solution generator start screen

AI-powered solution entry point: users describe their business problem in natural language

Generated user personas and stories

AI-generated personas and user stories based on business context

External system connections interface

Platform integration: connecting generated solutions to external systems for real-world implementation

Solution generation progress view

Progressive disclosure: showing users where they are in the AI generation process

Asset outline generation

Human-in-the-loop validation: reviewing AI-generated asset outlines before final generation

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