We are excited to welcome Elaine Barsoom, former Global Head of AI & Tech Innovation Partnerships at Nike, who recently joined as our newest Venture Partner. Spanning over 20 years of her career, Elaine has been operating at the forefront of the AI revolution, moving fluidly from enterprise strategy to start-up collaboration to help global organizations adopt and integrate emerging technologies. As a Venture Partner at Silicon Foundry, Elaine will collaborate with members and internal teams to bring clarity to AI strategy, draw on her network across enterprises and startups, and help organizations move confidently from experimentation to adoption.
Tanya Privé had the pleasure of sitting down with Elaine to discuss how AI is reshaping corporate innovation, from governance and operating models inside global enterprises to the leadership, trust, and organizational alignment required to scale emerging technologies responsibly.
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TANYA PRIVÉ: Elaine, welcome! It’s great to have you with us. You’ve spent over two decades in the innovation space. As technologies like AI, advanced hardware, and automation have shifted from experimentation to core enterprise infrastructure, how has that changed the way you think about corporate innovation?
ELAINE: Tanya, thank you so much for having me! To start off, I’d say that transformation inside legacy brands is hard. I say that with a lot of respect for the people inside them. Throughout my career, I’ve seen a consistent push and pull. Leaders want to move quickly, while organizations are often structured to protect what already works. Innovation teams are encouraged to experiment, yet kept at a distance from the core business. This tension often leaves innovation teams at arm’s length, well intentioned and interesting, but ultimately siloed and disconnected from how core decisions get made.
As AI and automation move from experimentation to core infrastructure, that separation is no longer sustainable. Innovation can’t live at the edges anymore; it must be woven into the very fabric of how the business runs. This realization shifted my perspective: innovation is not primarily a technology challenge—it is an alignment challenge. People, processes, and decision rights must move together. Without that alignment, even the most advanced technology struggles to create durable value.
TANYA: You touched on the tension between urgency and durability, which many leaders are wrestling with right now. Given the pressure companies feel to “do something” in AI, how should leaders distinguish between moves that build long-term positioning versus those that simply respond to short-term market noise?
ELAINE: The pressure to “do something” in AI is understandable, but it is often the wrong starting point. When leaders begin with urgency, they tend to optimize for visible action rather than durable advantage. Long-term positioning starts by slowing the question down. Instead of asking, “What tool should we deploy?” the more important questions are, “What problem are we truly solving?” and “What advantage are we trying to create?”
Short-term moves typically layer tools on top of existing processes to plug immediate gaps. While that creates activity, it rarely changes the trajectory of the business. Long-term moves reshape how the organization works, learns, and competes. I have seen repeatedly that redesigning how information flows across teams, rather than simply automating a single workflow, is what turns today’s solution into a compounding, sustainable advantage.
TANYA: How does the way large enterprises operate differ from scale-ups when it comes to modernizing the business, particularly around speed, scale, and brand risk?
ELAINE: Scale-ups operate in a phase where speed is existential. Product-market fit exists, but its durability, economics, and scalability are still being proven. Modernization is about removing friction fast enough to learn, adapt, and strengthen the business before competitors and market dynamics catch up.
Large enterprises operate from a very different starting point. They already have embedded relationships with millions of customers, where trust, loyalty, and brand reputation have been built over decades. This changes the equation entirely. For me, modernization is not just about what is technically possible; it is about what can be introduced without eroding customer confidence or breaking the promises they already rely on.
Inside global brands, every change carries a significant downstream impact, including regulatory exposure, brand risk, and employee readiness. This naturally introduces friction, and in many cases, I believe that friction is appropriate. The real risk occurs when that friction becomes silent resistance because teams are not aligned on the “why” or the trade-offs being made.
What I have learned is that speed inside enterprises is absolutely possible, but it looks different. It comes from clarity rather than pure urgency. It requires clear priorities, cross-functional alignment, and a leadership commitment to shared outcomes. Without that, teams often retreat into functional silos to protect their lanes. The most successful modernization efforts I have seen are anchored inside the business units, with technology acting as an enabler rather than the primary driver. That is how large organizations move forward without losing the trust that made them successful in the first place.
TANYA: You’ve also had a front-row seat to this inside one of the world’s most iconic brands. Specifically, during your time at Nike, what were the key lessons around governance, data readiness, and operating models when scaling AI across the organization?
ELAINE: The un-obvious lesson was that governance, when designed correctly, accelerates innovation rather than slowing it down. Early on, we made a deliberate choice to treat governance as an operating capability, not a checkpoint.
We built shared playbooks and clear risk thresholds to create what I think of as ‘governance by design.’ This provided teams an accelerated path to test and move into pilots without getting stuck in prolonged legal review cycles. On the data side, I saw that AI stalls where data ownership is fragmented. Therefore, I realized that treating data as a shared enterprise asset was foundational. Without that shift, no amount of tooling mattered. Speed at scale does not come from bypassing controls. Rather, it comes from designing them with intention, so innovation and trust advance together.
TANYA: What were the key takeaways from the session you led at Futureproof Project regarding how AI can shift the way brands communicate and engage with consumers, not just in efficiency, but also in creating meaningful experiences that drive trust?
ELAINE: The conversation consistently came back to trust and intent. What became clear is that AI is not changing brand engagement simply by making it faster or more efficient. It is changing how brands are experienced. I saw that increasingly, consumers do not encounter AI as a tool—they encounter it as behavior, tone, and presence. They respond emotionally to it.
One of the central ideas I shared is that AI allows brands to move from reacting to customers to anticipating them, not in a surveillance-driven way, but in a way that feels thoughtful and human. When a brand understands intent, context, and timing, interactions stop feeling transactional and start feeling supportive.
The most compelling examples were not about campaigns, but about continuous learning systems. Experiences that adapt over time, conversations that get smarter, and moments where automation actually creates more space for empathy rather than less. The brands that stand out are using AI to show up better, not louder. More consistent. More relevant. More human in moments that matter. When AI is used to deflect responsibility, consumers feel it immediately. When it is used to remove friction and add care, trust deepens.
From that conversation, the core takeaway was that efficiency is expected now, and trust is where differentiation begins. I also emphasized that AI does not ultimately replace the human relationship between a brand and its customers. It amplifies it, for better or worse.
TANYA: Over the next few years, where do you see the biggest opportunities for you and Silicon Foundry to shape how corporates engage with frontier technologies and innovation ecosystems?
ELAINE: I see a meaningful opportunity to help companies make better, earlier decisions as frontier technologies, especially AI, move faster than most organizational models can keep up. Silicon Foundry is in a rare position. It sits close to emerging technologies and venture ecosystems and is deeply grounded in the realities of running large enterprises. That vantage point allows us to do more than introduce innovation. It allows us to help leaders interpret signals, understand trade-offs, and make decisions that hold up over time.
From my experience inside large organizations, the hardest part is rarely discovering what is new. It is knowing what to do with it. How to align technology bets with business strategy. How to structure partnerships that actually scale. And how to design governance and operating models that allow teams to move quickly without eroding trust.
I am excited to work alongside Silicon Foundry to help companies engage with innovation ecosystems in a more intentional, repeatable way. One where technology, partnerships, and people are designed to work together as a system.
TANYA: Finally, looking ahead to the next five years, where do you think large enterprises are most likely to misjudge the complexity of AI and emerging technologies?
ELAINE: Many large enterprises will underestimate how fundamentally organizational this transformation is. While the technology itself will continue to improve at an extraordinary pace, those advances often mask where the real complexity lives: in people, incentives, and decision-making. The truth is, AI does not fail because of technical limitations; it fails when organizations try to layer it onto operating models designed for a different era.
The most common misjudgment is treating AI as a technical upgrade rather than a shift in how work actually gets done. In reality, it changes who makes decisions, how fast those decisions are made, and what skills matter most. This requires rethinking roles, incentives, and leadership behaviors, not just deploying new software. AI also has a way of exposing misalignment early. Where data is fragmented or priorities are unclear, AI amplifies those weaknesses. Conversely, where teams are aligned around shared outcomes, it compounds their strengths.
The organizations that succeed will be the ones that approach AI as a system-level change—aligning people, technology, and governance together. Success won’t come from chasing the latest tools, but from redesigning how the organization learns and adapts over time.
TANYA: Elaine, thank you for such an informative conversation. I’m excited to see how your perspective influences the next wave of practical, human-centered AI adoption.
