Overview

Problem: 80% of internal teams struggled to adopt AI consistently and confidently, while fragmented tools and manual workflows consumed hours across research synthesis, PRD planning, stakeholder communication, design hand-ffs, and ai-applied prototyping; slowing delivery, limiting visibility, and making it difficult to scale AI without compromising quality, accessibility, or accountability.

Solution: A human-in-the-lead agentic design operations model or Intelligent Workspace that connected research, chats, email, calendars, all critical tools like Slack, Confluence, Jira, Figma, and GitLab through MCP servers, specialized AI subagents, and chained workflow commands, transforming fragmented inputs into synthesized priorities, structured work, accelerated design outputs, and functional prototypes while preserving human judgment and final decision-making.

My Role & Impact: I introduced three significant changes to how teams understood, applied, and adopted AI:

• Connected customer insight to product definition
  I created an Intelligent Workspace connecting research insights, interactive personas, customer journeys, scenario testing, and continuous validation. It increased stakeholder engagement by 30% and made customer evidence a core input to PRDs.
• Orchestrated AI-enabled workflows
  I advocated for and architected automated workflows across research, planning, communication, design, prototyping, and engineering, while preserving human judgment, transparency, and accountability. The workflows saved participating teams an estimated three to four hours per person each week.
• Established a responsible adoption strategy
  I developed a 30/60/90-day strategy using the 5P framework: People, Process, Practice, Policies, and Posture—to move teams from experimentation to repeatable adoption through demonstrations, working sessions, governance, documentation, and role-based support.

Combined impact: 100% adoption within six months, 30% higher operational efficiency and AI engagement, PRD and tracking setup reduced from three hours to 10–15 minutes, and concept-to-prototype cycles shortened from six weeks to 10 days.

PROCESS

DISCOVERY

In this engineering-led organization, design was still an emerging discipline. Engineering had long defined the culture, operating models, and product-development processes, while designers were often engaged later as execution partners rather than positioned as strategic contributors. To remain embedded and influential, designers needed strong technical fluency, operational efficiency, and the ability to demonstrate value within fast-moving engineering environments. This made the organization’s approach to emerging technology a critical test of both design maturity and cross-functional readiness.

That challenge became especially visible through internal surveys, interviews, and ongoing engagement with design and engineering teams. We found that 80% of teams struggled to adopt emerging technologies, particularly AI, consistently, confidently, and at scale. Although awareness of AI’s potential was high, teams lacked clarity on where to begin, which workflows to prioritize, and how to integrate AI into daily work without increasing complexity or compromising quality.

Strategy

I approached the initiative as both an operational design challenge and an organizational transformation. The strategy centered on three connected questions: How could AI deepen customer understanding? How could it orchestrate fragmented workflows? And how could teams adopt it responsibly, consistently, and at scale?

Because the work was highly experimental, continuous testing, learning, and socializing outcomes became central to the approach. Within a cross-functional tiger team, I helped translate emerging AI capabilities into practical solutions for design, product, engineering, and quality teams. Together, we defined a new software development lifecycle, supporting architecture, and agentic operating model that established the foundation for integrating AI across the development process, automating manual work, improving efficiency and quality, and enabling the organization to respond more effectively to market needs and its broader mission.

Define

My team and I identified the foundational steps required for responsible AI adoption, and shaped a human-in-the-lead, AI-in-action framework to help teams apply AI with clarity, accountability, and confidence across design workflows. The framework turns fragmented requests into a structured operating model where AI helps collect signals, surface patterns, suggest experience directions, and accelerate handoffs, while humans remain responsible for verification, judgment, decision-making, and final quality.

I then architected an Intelligent Workspace that connected Slack, Confluence, Jira, Figma, GitLab, email, calendars, and research repositories through MCP servers, RAG agents, specialized subagents, and chained workflow commands.

Core components included:

• MCP servers for tool coordination and shared workflow context
• RAG agents that brought research insights to life
• Cursor and Ask for synthesis, planning, and task generation
• Jira for automated epic and ticket creation
• Slack, email, calendar events, and relevant news for daily summaries and priority signals
• Figma plugins for faster low- to high-fidelity wireframing and design-system outputs
• GitLab for rapid prototyping and tighter design–engineering collaboration
• Momentum for tracking progress, reviewing outcomes, and measuring impact

TESTING & SOCIALIZATION

Securing adoption required more than introducing AI tools. my team and I created a structured enablement process that helped teams experiment safely, build confidence, and adopt new ways of working at their own pace. The process was first tested with small groups and supported through monthly learning sessions, daily working sessions, 1-on-1 coaching, shared instructions, and reusable documentation. Every process, installation step, and example was made accessible so people could reproduce the work, develop their own agents, and build solutions independently. This support model helped us measure confidence, interest, and readiness while encouraging teams to see AI as a co-partner, one that could help them think, connect work, challenge assumptions, and create beyond the limits of a traditional search tool.

I had quietly hoped to explore this direction for a long time. With this initiative, I took it further to inspire others. What did I do?

I created a new surface, a new way to experience research.

This became especially important in research, where valuable insights were often gathered, documented, and gradually lost across repositories, reports, tickets, and static artifacts. I developed an intelligent workspace that brought years of research into one connected environment and introduced an interactive layer across personas, user journeys, A/B testing, and real workflow scenarios.

Instead of asking stakeholders to read research after decisions had already been made, the system allowed them to engage with evolving, research-based personas they could question, test, and challenge as new data and market needs emerged. Researchers began installing the system and building their own agents, opening a path toward something I had hoped to explore for year, transforming research from a static deliverable into a living part of product development. The people we design for are more than data points, quotes, and journey maps. They represent real lives, constraints, motivations, and hopes. AI gave us a way to make that human context more visible, more actionable, and harder to ignore.

Validation with internal teams revealed that AI adoption did not progress at the same pace for every user group. Nearly 10% of teams were not ready to independently configure Cursor, connect MCP servers, integrate Figma, or apply the new workflows without additional support. Weekly podcasts, demos, and working sessions kept teams and stakeholders engaged, made the learning visible, and positioned the Intelligent Workspace as the central hub for operations, communication, effectiveness tracking, and experimentation.

 

The Intelligent Workspace system enabled teams to:

• Centralize the research insights to build an interactive workplace
• Automated Jira epics and tickets from meeting inputs and discussions
• Summarized daily communication and surfaced what mattered most
• Turned fragmented conversations into structured action view agentic ai outcomes 
• Accelerated wireframe creation and reusable design system patterns
• Shortened the path from design concept to simulated prototypes

More broadly, it showed how agentic AI could support both team operations and design execution when applied with clear intent and workflow structure.

While power users adopted the model quickly, other teams required step-by-step guidance, working sessions, documentation, and repeated follow-up. Their adoption took three to four months rather than one week, reinforcing that successful AI transformation depends as much on enablement and confidence as it does on tooling.

This learning shaped my 5Ps adoption model:

People: Understand readiness, confidence, and support needs
Process: Define how AI fits into existing workflows
Practice: Create repeatable opportunities to learn through use
Policies: Clarify what teams can use, when to use it, and under what conditions
Posture: Socialize proven practices, get more buy-in, share outcomes, and continuously improve through feedback

The 5Ps helped us create a more inclusive adoption path that supported both advanced users and teams that needed more time, structure, and reassurance.

Beyond the measurable gains, the work also created momentum around experimentation and practical adoption. It showed how agentic AI could be applied in a repeatable and team-centered way to improve operations, strengthen execution, and support broader organizational learning.

A meaningful outcome was how the team approached transparency. Several designers began introducing “co-authored by AI” disclosures in Jira tickets and generated content to make AI involvement visible to stakeholders. Instead of positioning AI as an author, the team standardized on “AI-assisted” attribution, paired with a brief explanation of how AI contributed (e.g., synthesis, structuring, or generation). This aligned with industry guidance that maintains human ownership while ensuring transparency and accountability.