How can I stay relevant—and lead—when AI is changing what it means to be a designer?

My guests this week were Elizabeth Glenewinkel and Mario Ruiz, two amazing design, innovation, and strategy leaders who have been working together for more than a decade. We discussed everything from being adaptable to change…while also being willing to step up and lead that change. 

What I loved most about our conversation is just how open and collaborative Elizabeth and Mario are. Like all great designers, they take input, co-create options (for the future), prototype everything, and launch, knowing full well that they will continue to iterate along the way. What’s more, one of my biggest ah-ha moments came from their newest project, teaching designers and teams how to employ the Jobs to be Done (JTBD) framework to help teams and companies design human-AI agent workflows (i.e., finding agency in an increasingly AI-driven world!).

In this issue/episode:

1. Designers Must Lead, Not Just Create

2. Frameworks, like JTBD, are Powerful Tools in a Designer’s Toolbelt for leading change…especially when it comes to disruptive tech, like AI

3. The Future Belongs to the Adaptive

4. Steal This Tactic! Run your own JTBD workshop for AI.

— Justin Lokitz

Insights

• 01:00 – Meet Elizabeth Glenewinkel & Mario Ruiz

• 12:00 – Human-Centered Innovation Requires Interdisciplinary Balance

• 13:30 – Structure + Speed = Scalable Innovation

• 18:00 – Innovation as a Relationship Builder, Not Just a Deliverable

• 22:30 – Jobs to Be Done as a Framework for Agentic AI

• 30:00 – AI Isn’t Just a Tool—It’s a Collaborator

• 34:30 – The Designer’s Role Is Shifting from Maker to Strategist

• 39:00 – Agentic Thinking Gives Designers a New Path to Value

• 45:00 – Workshops as a Learning and Leadership Tool

• 47:00 – To Learn AI, You Have to Use It, Teach It, and Adapt Constantly

• 49:00 – Curiosity and Cross-Disciplinary Conversations Are Key to Staying Ahead

• 53:00 – How to Connect with Elizabeth & Mario

Read on for my main takeaways from our conversation. 🎧 And…listen to the recording of our full conversation on the Design Shift podcast on Spotify and Apple and the Design Shift YouTube channel. Did you notice anything uncanny about the intro😉?

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Design Deep Design

Why Designers Must Lead, Not Just Create — and How AI + Jobs to Be Done Can Show the Way

What does it mean to be a designer when AI can write, generate, and even prototype at the click of a button? The answer, according to Elizabeth and Mario, isn’t to compete with the machines, but to lead them.

In this issue of Design Shift, we explored how AI is transforming design, what skills today’s professionals need to thrive, and how frameworks like Jobs to Be Done (JTBD) can give designers a strategic edge in an AI-first world.

Here’s what I learned while speaking with Elizabeth and Mario…

1. Designers Must Lead, Not Just Create

As AI continues to automate production tasks, the value of designers is shifting upstream — toward strategy, systems thinking, and critical decision-making. For Elizabeth and Mario, this transition started long before the rise of generative AI, during their time building Salesforce’s internal innovation team, Ignite.

“The team was dedicated — not just handoffs,” Elizabeth explained. “Designers were the advocates for the solution. Strategists were the advocates for the business. And researchers were the advocates for the user. All three disciplines had equal weight.”

This holistic, cross-functional approach allowed them to work at the intersection of customer need, business value, and technological possibility — a space designers are uniquely qualified to navigate. And…in today’s AI-driven world, that strategic positioning is more essential than ever.

Mario added, “We’ve always shared this idea of moving design as far upstream as possible. This gives designers a way to ask: where and why are we applying AI? What outcomes do we expect?”

Rather than being told to implement AI solutions, designers can shape how and why they’re deployed. That requires not only design skills but also business acumen, systems-level thinking, and a comfort with ambiguity.

2. JTBD Is a Powerful Framework for AI Integration

After leaving Salesforce, Elizabeth and Mario began exploring how the JTBD framework could help organizations design and prioritize AI-powered agents. Their insight: AI is great at completing narrow tasks — if you know exactly what you want it to do.

“Jobs to Be Done gives us a shared language to define those tasks,” Mario explained. “It helps us understand the full customer lifecycle and where agents can plug in — from marketing all the way to service.”

Elizabeth expanded on this point: “Companies are always saying, ‘We want to use AI for product development.’ But that’s way too broad. When you use a job map, you can be surgical. Maybe you build an agent just to generate campaign ideas — one small job. That’s lower risk and easier to test.”

Their workshop, which combines JTBD mapping with AI agent prototyping, helps teams identify specific opportunities for automation based on real human needs. The result is a more thoughtful, human-centered approach to AI — one that avoids hype and focuses on measurable value.

This also reframes AI from a threat into a tool that empowers designers. “You’re hiring an agent to do a job,” Elizabeth said. “But the job performer is still human. You’re augmenting them, not replacing them.”

3. The Future Belongs to the Adaptive

Both Elizabeth and Mario emphasized that staying relevant in a fast-moving world requires constant experimentation, not perfection. Designers who wait to master AI before using it will be left behind. Those who learn by doing, teaching, and adapting will thrive.

“We always had a test-and-shape mentality at Ignite,” Elizabeth said. “Don’t just talk about it — try it. Teach a workshop. Prototype something. Get it into people’s hands.”

She recounted how she now uses AI to rapidly prototype curriculum. “I mapped out a class in Miro, then asked GenAI to redo it using different design methods — double diamond, IDEO’s kit, John Kolko’s approach. I got three different versions and compared them. Some were great. Some were overstuffed burritos.”

The key, she said, is critical thinking. AI can generate — but it can’t evaluate. That’s the designer’s job.

Mario reinforced the value of experimentation, especially for career growth: “You don’t need to know where it’s going. I talked to a friend who left a CTO role and is building a product with another engineer. He said, ‘Even if this fails, I’ll be a better leader because I understand the tools.’”

They also recommended surrounding yourself with diverse perspectives — from technologists to venture capitalists to fellow designers. Read, listen, build, and share. The world is changing fast, but if you stay curious and adaptable, you’ll be ready.

Justin’s Notes

If you think the age of agents is about replacing designers, it’s time to shift your perspective. By doing what you do best – having empathy, bringing people together, and co-creating The Future – you can lead the conversation about (and design of) how an organization employs AI. By pairing strategic frameworks like JTBD with an experimental mindset and critical collaboration with AI, designers can move from makers to leaders. This is how WE take agency in the AI conversation!

Or as Elizabeth put it: “You still have to be able to evaluate what AI is doing for you. You have to critique it. And that’s what designers do best.”

Want more? Connect with Elizabeth and Mario and tell them Justin sent you!

Resources

• Learn more about the Jobs to be Done framework

• The Jobs to be Done Toolkit

• Run your own JTBD for AI Agents Workshop

Subscribe to Design Shift for more conversations that help creative professionals grow into strategic leaders.

Steal This Tactic!

Want to run your own Jobs to Be Done (JTBD) workshop designed to help teams identify where AI agents can meaningfully fit into a workflow? Here’s a structure that blends human-centered design principles with a practical, agentic AI integration strategy.

Here’s a condensed, narrative-driven version of the 5-step workshop process, reframed to feel more fluid and engaging — perfect for presentation, facilitation, or inclusion in a guide:

Step 1: Assemble the Right Team and Set the Stage

Before anything else, bring together a cross-functional group that represents the real complexity of the system: product managers, designers, engineers, operations folks, and customer-facing teammates. As you well know…the magic happens when diverse perspectives converge.

Kick things off by framing the opportunity:

• What part of the workflow or customer journey are we focused on?

• Who is the job performer (the person doing the work)?

• Why now? Why AI?

This sets shared expectations and gives the group a north star.

Step 2: Break the Work Down with JTBD

With your focus area and job performer in mind, map out the work using a JTBD lens. Start with the main goal—the primary job to be done—then break it into sub-jobs using familiar patterns like Mario and Elizabeth do here…

Tip: You could certainly use AI to do this for you, too! Just make sure you also review and update the results with the team.

Step 3: Spot the Agent Opportunities

Now, scan your job map and ask: where might an agent reasonably step in to reduce the burden, improve consistency, or speed things up? Often this will be found at the very bottom…the micro-jobs or tasks.

Tag each sub-job using:

• H (Human-led)

• A (Agent-ready)

• C (Co-performed)

Discuss where AI might bring the most value, without overengineering or risking trust. Look especially for high-friction, low-creativity tasks or things that break under scale. You might even start with ways you could scale design output by working hand-in-hand (or circuit) with an AI agent.

Step 4: Prototype Agent Concepts

For those who know me, this is perhaps my favorite step (and my life-long mantra 😉)! Once you’ve identified the most promising opportunities, turn them into tangible agent ideas. For each one:

• Define what the agent is hired to do.

• Describe inputs, outputs, constraints, and possible failure modes.

• Consider how the agent collaborates with humans (or doesn’t).

You might use a simple formula like:

“An agent that helps [job performer] [complete sub-job] by [key tasks], resulting in [benefit].”

Sketch it, describe it, or even prompt it using GenAI. Keep it fast and lo-fi.

Step 5: Prioritize and Plan Experiments

Wrap up by evaluating your agent concepts based on impact and feasibility. A simple 2x2 matrix works wonders here. Ask:

• Will this meaningfully improve the workflow?

• Can we build or test this with the resources we have?

• Are there ethical, data, or user trust concerns?

Select 1–2 concepts to take into low-fidelity prototyping or pilot testing. The goal isn’t perfection—it’s momentum.

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