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Innovation Lessons from Nike and Amex
Season 3 · Episode 99

Innovation Lessons from Nike and Amex

In this episode of The Bridgecast, host Scott Kinka sits down with Elaine Barsoom — Venture Partner at Silicon Foundry and former Global Head of Tech Innovation, Partnerships, and Strategy at Nike — to explore what purposeful AI adoption actually looks like inside complex, global enterprises. Elaine challenges the all-too-common "AI mandate" problem head-on, urging leaders to start with the right questions: What problem are we trying to solve, and can AI actually help? Drawing on her experience at Nike and American Express, she shares battle-tested frameworks for AI governance, cross-functional rollout, and building champion programs that drive sustainable adoption from the bottom up — not just the top down. From Lowe's Milo to Wingstop's Smart Kitchen, this conversation is packed with real-world proof that the most effective AI strategies are designed around humans first, tools second. Essential listening for CIOs, IT leaders, and anyone trying to navigate the gap between AI hype and measurable business impact. To find out how Bridgepointe Technologies helps businesses make IT decisions faster with world-class engineering support and ongoing guidance, head to https://bridgepointetechnologies.com/

The Bridgecast with Scott Kinka · Bridgepointe Technologies

March 24, 202638m 52s

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Show Notes

In this episode of The Bridgecast, host Scott Kinka welcomes Elaine Barsoom — Venture Partner at Silicon Foundry and a recognized leader in enterprise AI and corporate innovation — for a wide-ranging conversation on what it actually takes to build an AI strategy that sticks.

With 20 years of experience bridging strategy and next-generation technology across Nike, American Express, and multiple sectors including retail, fintech, and hospitality, Elaine brings a rare lens: the view from inside the machine. Her defining insight? Most organizations are asking the wrong question. Instead of "How do we use AI?", the right question is "What problem are we trying to solve, and can AI help?" — a subtle but transformative shift that changes everything about governance, adoption, and ROI.

What you will learn:

  • Why most AI mandates are set up to fail — and how to reframe the right question
  • How to map current-state workflows and redesign around human needs before selecting tools
  • Why experienced employees with tribal knowledge outperform younger workers when using AI
  • How Nike's GitHub Copilot rollout almost stalled — and what a champions program did to fix it
  • The top-down mandate + bottom-up build model for sustainable AI adoption
  • Why most AI governance frameworks are "theater" and what to do instead
  • Real-world CX wins: Lowe's Milo, Wingstop's Smart Kitchen, and what they teach about ROI
  • The bold prediction that AI agents will soon outnumber humans in enterprise org charts

Elaine Barsoom is a Venture Partner at Silicon Foundry and a globally recognized leader in AI strategy and corporate innovation. She spent 20 years helping Fortune 500 organizations marry strategy with next-generation technologies, most recently serving as Global Head of Tech Innovation, Partnerships, and Strategy at Nike. Her career also includes pivotal leadership roles at American Express, where she built one of the company's first e-commerce ventures in the years following September 11th. A first-generation daughter of Egyptian immigrants and a systems thinker at her core, Elaine now works with mid-market organizations and leaders to navigate AI transformation with rigor, empathy, and measurable results. 

Episode Highlights:

  • [09:52] Ask the Right Question First

Elaine's most foundational piece of advice for AI-overwhelmed CIOs reframes the entire conversation. At Nike, before touching any tool or vendor, the team asked: "What problem are we trying to solve, and can AI help?" — not "How do we use AI?" This single reframe changes how friction points get identified, how workflows get redesigned, and ultimately whether AI gets used at all. Elaine is unambiguous: "Just putting a tool on top of a tool just incurs more technical debt. Don't just do AI for AI's sake." She also challenges the very structure of governance conversations, arguing that IT leaders must walk in with systems and frameworks already in place — not reinvent the wheel for each vendor conversation. A pre-agreed risk framework between IT and legal isn't bureaucracy; it's the only way to move fast and stay protected.

  • [14:30] The Champions Program: Nike's Secret to GitHub Copilot Adoption

When Nike rolled out GitHub Copilot, leadership assumed flipping the switch would be enough. It wasn't. Adoption stalled. What unlocked it? Training, targeted use-case discovery, and — most critically — a "champions program" that activated the engineers who were already experimenting on their own. Elaine connects this directly to the shadow IT problem: fear of displacement drives people to go rogue. Organizations that surface those early adopters and give them a sanctioned role in the rollout don't just solve a security problem — they create a self-sustaining adoption engine. Her formula: top-down mandate gives permission, bottom-up champions give momentum. "Build community structures that understand and operate. Then, all of a sudden, you're not just pushing a literacy program; it's creating itself. That's sustainable."

  • [19:15] Tribal Knowledge Is the Real AI Advantage

Elaine lands one of the episode's most counterintuitive insights: it's not Gen Z leading AI adoption in the enterprise — it's the seasoned operators with years of context. AI tools are only as powerful as the prompts fed into them, and great prompts require deep domain knowledge. "Intelligence lives outside the tool," she says. "You can't just apply an agent and think you're diving into the workflow. You have to learn about all the tribal knowledge gained over the years." This reframes the entire upskilling conversation. The goal isn't to teach experienced workers to be tech-savvy; it's to help them see that what they already know is the most valuable input to any AI system — and then invest in developing that capability intentionally.

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