Design has always been under pressure to move at the pace of engineering — to speak its language, to match its speed. With the rise of AI-native tools like GitHub Copilot, Cursor, and Figma’s AI integrations, design can now deliver real code and real value in hours—not weeks.
Here’s how I help organizations integrate AI into their design practice to drive speed, clarity, and business impact:
- Moving a design org from “AI curiosity” to AI-integrated workflows.
- Shifting product roadmaps from AI-as-a-feature to AI-as-a-business-driver.
- Redesigning team skill sets for AI-native UX.
The next few months will be a whirlwind. Immature software development lifecycles (SDLCs) will become even less mature. Well-run product organizations will either experience a small speed bump… or leap ahead of their competition. AI has completely transformed the output speed of engineering — but it hasn’t magically matured product innovation.
The playing field for ideation and execution is now open to every PM, designer, and engineer in your organization.
The upside: More ideas, faster than ever before.
The downside: Without a clear product strategy, your org risks chasing ghosts.
What needs to happen now
Open the sandbox
Give product and design teams access to your full GitHub codebase.
Yes, this may require some DevOps effort — but will fuel innovation.
Rethink your product workflows
You likely now have multiple parallel tracks:
Standard: PRD → Design → Engineering
Engineering-first: Eng → Design → Eng
Design-first: Design → Eng
AI acceleration means these streams may collapse or overlap — you need clarity on how they work together. Without this clarity your estimation process will become guesswork.
Assess AI literacy across teams
Evaluate your PMs, designers, and engineers not just on tool usage, but on their ability to integrate AI into problem framing, prototyping, and delivery. Provide Github access to product and design to your full codebase.
Bolt, Lovable, Cursor, Replit, Langchain, Claude, et al... Tool chasing isn't going to cut it.
AI Tools Can Build — But Only People Can Direct. Every week, there’s a new AI tool promising to design your interface, write your code, and deliver your product in record time.It’s a thrilling shift — but also a dangerous illusion.
Because at their core, every one of these tools still relies on human direction. Without a clear understanding of what to build and why it matters, the output is just noise.
AI accelerates execution — but leadership defines the target.
To turn raw AI output into product value, organizations still need people who can:
Frame the right problems
AI is only as good as the prompts it’s given.Leaders must define problems in business and customer terms, not just feature requests.
Tune output to fit the business model
An AI-generated design might look polished, but if it doesn’t fit your revenue strategy or market positioning, it’s wasted effort.
Apply market alignment and competitive insight
AI can’t assess whether your solution resonates with your specific audience or differentiates from competitors. That’s human work.
Bring design taste and brand stewardship
AI can propose layouts and flows, but discerning what feels right — and what builds long-term brand equity — is a uniquely human skill.
AI has accelerated how products are built — but it’s also fractured the way teams work. What used to be a single, structured product development process has splintered into three parallel workstreams:
1. Traditional Workflow (Waterfall/Agile Hybrid)
The familiar process still exists: PM drafts a PRD or one-sheeter. Design asks clarifying questions, creates the feature or product experience. PM and Engineering review for feasibility, code is written, tested, and launched.
This flow is deliberate, documented, and predictable — but also slower.
2. Engineering-Led AI Builds
Now, engineers often use design system components to quickly ship AI-driven features with little to no design input. Speed is prioritized over experience.
New AI behaviors (like unpredictable outputs) create flow and user journey issues.
Design is pulled in last-minute to “fix” things — but without dedicated time, most improvements are shelved.
Features ship untested and often miss user expectations.
3. AI-Generated Designs & Code
PMs or designers use AI tools to spin up entire interfaces and code. Output is handed to engineering for refactoring. There’s no research, no user intent, no design review. Engineers inherit code they didn’t write and lack context for why decisions were made.
The result: inconsistent, fragile experiences.
The Core Problem
We’ve introduced two additional, chaotic workstreams when we rarely had the first one running efficiently. Without strong leadership, this results in: Misaligned features that don’t serve the business. Burnout across teams constantly “fixing” each other’s work. Lost market opportunities as competitors ship with clarity and purpose.
How I Solve This as a Design Leader
Unify the Workstreams Under One Product VisionEstablish a single, shared source of truth for product direction — tying every build (AI-assisted or not) to clear business outcomes.
Integrate Design Earlier in All Workflows
Build processes where design has input before engineers code or AI tools generate output. Introduce lightweight, rapid design validation loops to maintain speed without sacrificing quality.
Create AI-Literate Teams & Guardrails
Train PMs, designers, and engineers to use AI effectively and responsibly.
Define where AI accelerates — and where human judgment is non-negotiable (research, intent, brand).
Redefine "Done" in the AI Era
Shift the definition of "done" from “code shipped” to “solution validated against user needs and business goals.”
