If you’re a leader, you’ve likely heard the same topic come up again and again in meetings–what are we doing with AI? And with good reason. Artificial intelligence has the potential to change nearly every aspect of work and business. 

But with new tools launching daily and endless headlines about breakthroughs, it’s easy for leaders to feel both the urgency to “do something with AI” and the uncertainty of where to start.

On the Creative Confidence Podcast, AI strategist and former IDEO partner Justin Massa joined host Mina Seetharaman to cut through the noise. Through his consultancy Remix Partners, Justin helps executives and entrepreneurs make smarter AI investments by focusing not on chasing every new tool, but on finding the few places where AI can truly move the needle for their business.

Below are some of his biggest takeaways for building an AI strategy for leaders that’s grounded, practical, and creative.

Listen on Spotify or Apple Podcasts

 

Start with strategy, not technology

It’s tempting to get swept up in the hype of what AI might be able to do. But Justin argues that leaders should start by looking at what it can do right now—and match that to real business problems.

“When I think about what you should be doing with generative AI right now, it's really about leveraging what the technology can do today, not what it can do tomorrow,” Justin says of where to start. 

Rather than experimenting at random, Justin encourages leaders to identify their biggest bottlenecks—the slow processes, repetitive tasks, or missed opportunities that hold their teams back—and explore where AI might relieve those specific pain points.

“This isn't technology in search of a problem,” he notes. “We need to start with understanding the problems.”

This shift reframes AI adoption from a race to keep up into a strategic design exercise—something leaders already know how to do well.

Use the AI Opportunity Matrix to focus your business bets

To help leaders prioritize, Justin built what he calls the AI Opportunity Matrix, a simple 2x2 framework that maps the importance of a capability to your business differentiation against its risk of AI disruption.

The idea builds on Roger Martin’s Playing to Win strategy framework, the focus of our Designing Strategy course, which asks leaders to define where to play and how to win based on their organization’s distinctive capabilities.

The result? Four quadrants: Maintain, Automate, Augment, and Transform.

The real danger zone, Justin says, is the Transform quadrant—when something core to your differentiation could be easily replicated by off-the-shelf AI. “Any business on Earth can buy your business's competitive differentiation for as cheap as $20 a month per employee, and that absolutely should freak out every single leader.”

But the biggest missed opportunity lies in the Augment quadrant, where humans and AI combine to create something better together.

“I fear many organizations find the automate quadrant and then stop looking for opportunities,” he says. “Augment is where you have your real opportunities to drive and extend your differentiation.”

Augmenting human capabilities with AI: an example

Justin shared the story of a branding firm whose unique capability was their ability to build long-term, trust-based relationships with Series A founders, helping to shape their brands as each business evolved. That human connection was their edge, but their approach had become mostly reactive– responding when a founder reached out rather than anticipating needs.

By using generative AI to track funding news and founder milestones, the team could now anticipate when clients might need brand support and reach out with timely, tailored ideas.

“You’re not replacing the relationship—you’re strengthening it,” Justin said of the impact of augmenting human capabilities with AI tools. “AI helped them show up earlier and more thoughtfully, in ways that made their human connection even stronger.”

Four universal entry points for AI innovation

Even with a clear strategy, many leaders still ask, ‘Where do we begin?’ Justin points to four practical places every organization can start—no custom models or massive data sets required.

1. AI-generated transcripts of human conversations

Capturing full transcripts of meetings, client calls, or brainstorming sessions creates a searchable, sharable knowledge base. Teammates who wouldn’t normally get to speak to customers firsthand can hear pain points and needs in their own words. Plus, it frees you up to be more engaged in the moment. 

“There is so much incredible nuance communicated in a transcript of a meeting that many generative AI models lose when they create a set of notes,” Justin said.

Tools like Gemini for Meet or Teams, or Granola can automatically record and organize these transcripts, turning conversations into insight goldmines. AI can highlight recurring themes across conversations or pull direct quotes from customers to inspire marketing, product, or service design work. Just make sure you’re following your organization’s policies and local laws on recording—especially in regions that require consent from all participants. Most built-in AI assistants will notify attendees when a meeting is being transcribed, but it’s still best practice to be transparent.

2. Onboarding and offboarding with generative AI tools

Every organization loses time and institutional knowledge when people join or leave projects. Generative AI can summarize documents, capture “tribal knowledge,” and even conduct exit interviews.

“Almost every organization I've worked with has realized pretty quickly that generative AI is great at onboarding and offboarding,” Justin says of this universal use case. 

Justin described clients using AI to preserve the wisdom of retiring team members, building what he calls virtual mentors future employees can learn from.

3. AI as a debate tool

AI doesn’t need to be a yes-man. In fact, Justin warns leaders to watch out for what he calls “AI sycophancy”—the tendency of models to be overly complimentary.

“Everyone using generative AI, no matter what you are using it for, ask it to debate and push back,” Justin says of how this feedback can be easier to accept via AI as well. “Through that Socratic back-and-forth, your work is going to get better.”

By explicitly inviting AI to disagree, leaders can uncover blind spots, stress-test strategies, and spark fresh perspectives they might otherwise overlook. 

4. Rapid scenario planning with AI

When the future feels unpredictable, AI can help teams explore multiple “what-if” futures quickly. Because it has access to so much historical data, AI can rapidly build out detailed, deeply specific future scenarios that feel tangible and grounded. AI can quickly generate multiple variations on a theme—optimistic, pessimistic, and everything in between—or adjust scenarios as new inputs emerge.

This helps people picture possibilities more clearly and make those imagined futures easier to discuss and plan around.

“Generative AI is amazing at coming up with future scenarios,” he says. “The level of rigor and depth that we get without increasing the time or complexity of the work is the thing that generative AI is so incredibly good at.”

He shared how the Northern Illinois Food Bank used AI to model shifts in government policy and prepare for potential funding changes. 

Rethink the ROI of AI: from efficiency to augmentation

When new technologies emerge, leaders often default to measuring ROI through cost reduction: fewer hours, fewer people, faster output. Justin sees that as a trap.

He tells the story of 19th-century breweries adopting steam power. Some used it to cut labor costs. Others, like Guinness, used it to scale their reach without reducing their people. Guess which one became a global brand?

“If that is the only move you make, you are putting a ceiling on your addressable market,” Justin says of focusing only on efficiency and cost reduction. “Most people are focusing primarily on the efficiency. They're focusing on the automate, and they are missing the augment. And I have to tell you, in the organizations where they're leaning into the augment, that's where they're seeing the real growth.”

A modern AI strategy for leaders balances both—using automation to free up time and using augmentation to create new forms of value.

A Human-Centered Approach to AI Adoption

Rolling out new technology is never just a technical challenge—it’s a cultural one. Justin has seen over and over that the success of an AI adoption plan depends on how leaders model curiosity and experimentation. “When the CEO and the C-suite directly use LLMs themselves, things go fairly well. When they don’t, it’s much more challenging.”

Lead with mindset, not mastery

Justin emphasized that mindset beats tech fluency. You don’t need to be a coder to lead with AI. You just need to be curious. He shared the story of a 65-year-old engineer at a transformer manufacturing company who, despite limited experience with traditional software, became one of the most creative AI users on his team. He wasn’t afraid to think outside of the box because he didn’t know there was one.

His success came from approaching the tool with openness rather than expertise—an example of the kind of beginner’s mindset that fuels AI innovation.

Normalize AI experimentation and reflection

In most organizations, AI use already exists quietly behind the scenes. People are experimenting on their own devices or using AI to save time without telling anyone.

Justin suggests creating spaces where people can safely share what they’re trying—Slack channels, brown-bag demos, or short team huddles dedicated to “show and tell.” Encourage teams to share small wins, talk about what didn’t work, and invite debate. When employees feel supported to experiment, hidden innovation surfaces, and teams learn faster together.

Support hesitant AI adopters and celebrate the AI-curious

Not everyone will dive in right away. Justin sees a familiar pattern: about a third of employees use AI regularly, a third dabble, and a third haven’t touched it yet. Leaders should celebrate early adopters while offering training and encouragement to those who feel unsure.

“Not everyone's gonna pick this technology up right away. Having clear training and education programs for folks, especially the folks who are hesitant, is really important.”

Shorten your time horizons

Finally, Justin reminds us that the pace of change in AI means strategy cycles must get shorter. What used to be a year-long plan now needs to fit into a few months. “Think about a six-month implementation strategy,” he encourages as a starting point. Test, learn, and iterate on shorter cycles so that your AI use cases aren’t immediately outdated. 

His closing advice: Start small, stay curious, and use AI to amplify what makes your team—and your business—uniquely human.

“You can read a million articles about bike riding then get on a bike and fall. The only way to learn to ride a bike is to use it, and generative AI works in the same way.”

Key Takeaways

• Start with strategy, not technology. Focus on the problems your organization needs to solve today—not the futuristic promises of tomorrow’s AI.
  

• Use the AI Opportunity Matrix. Identify which capabilities to maintain, automate, augment, or transform. The real opportunity lies in augmentation—combining human creativity with AI capability.
  

• Begin with four universal entry points. Capture meeting transcripts for insights, streamline onboarding and offboarding, use AI as a debate partner to avoid “AI sycophancy,” and generate detailed scenarios to stress-test strategies.
  

• Rethink ROI. Look beyond efficiency and cost savings. Measure success through opportunities created, experiments run, and growth in capability and differentiation.
  

• Take a human-centered approach to AI adoption. Leaders must model curiosity and experimentation, create safe spaces for sharing, and support hesitant adopters so teams can learn faster together.
  

• Shorten your time horizons. The AI landscape shifts quickly. Plan in 6-month cycles, test small, and learn fast.

 

Explore More

Related Courses from IDEO U

• AI x Design Thinking Certificate — Build your own AI adoption plan and learn tools for creativity, strategy, and responsible innovation.
  

• Designing Strategy — Learn Roger Martin’s Playing to Win framework, which inspired Justin’s AI Opportunity Matrix.
  

• Storytelling for Influence — Turn complex ideas, like AI strategy, into compelling narratives that inspire teams to act.
  
  

More from Justin Massa

• Previous Creative Confidence Podcast Episode: How to Tell Stories with Data
  

• Read the episode recap on IDEO U

• Listen on Apple Podcasts

• Remix Partners — Justin’s consultancy helping small and mid-sized businesses develop practical, high-impact AI strategies.
  

• Justin Massa on LinkedIn — Follow Justin for weekly insights from his AI for SMBs Weekly newsletter.

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