At Avasant’s 2026 Empowering Beyond Summit, themed around “Future-Proofing the Enterprise,” Gamiel Gran, Chief Commercial Officer at Mayfield, delivered a compelling perspective on the role of AI in shaping the next generation of organizations. Drawing from his experience in venture capital and close engagement with leading startups and global CIOs, Gran challenged executives to move beyond incremental adoption and instead embrace AI as a catalyst for enterprise reinvention. His message was clear: future-proofing is not about keeping pace with technology, it is about fundamentally reimagining how organizations are designed to compete.
Artificial intelligence is often framed as the next wave of enterprise technology, but that framing misses the magnitude of what is unfolding. The organizations that treat AI as another tool or incremental upgrade, risk falling behind those that recognize it for what it truly is: a catalyst for reinvention.
As Gamiel Gran puts it, the shift is not primarily technological— “it’s maybe not so much about technology… there’s some radical rethinking about your entire organization.”
That distinction defines the emerging competitive divide.
The scale of investment and activity in AI reflects a deeper structural transformation. This is not just another innovation cycle—it is, in Gran’s words, “a once in a generation opportunity,” with the potential to remake “the entire IT stack… from the bottom all the way through the top.”
Yet the implications extend well beyond infrastructure, models, or applications. What is being rebuilt is the mechanism through which organizations create value. Legacy systems were designed to capture, store, and process information. AI changes that equation entirely.
“Systems of record where your users are entering data no longer is the objective,” Gran explains. “The objective is outcome.”
This seemingly simple shift has far-reaching consequences. It challenges the relevance of established technology architecture, traditional operating models, and even the economic foundations of enterprise software.
For decades, enterprises have optimized efficiency and standardization. Workflows were designed to ensure control, predictability, and scale. But these same structures now act as constraints.
Gran is explicit, “You need to radically redesign your organization to be AI first.”
This is not about layering AI onto existing processes. It is about questioning whether those processes should exist at all. The organizational silos, handoffs, and rigid workflows that once drove efficiency now inhibit speed, learning, and adaptability.
The uncomfortable reality is that many organizations are structurally misaligned with the demands of the AI era. What once created advantage is now creating drag.
A critical barrier to transformation is how organizations define value. Traditional ROI frameworks anchor decision-making in cost savings and incremental gains. In the context of AI, this lens is insufficient.
Gran reframes the conversation around “return on intelligence”—a concept that shifts focus from efficiency to capability.
Every enterprise already possesses deep institutional knowledge: proprietary workflows, domain expertise, customer insight. These assets represent a form of embedded intelligence that AI can unlock and amplify. The question is whether organizations are prepared to operationalize that intelligence at scale.
Treating AI as a productivity enhancer yields marginal returns. Treating it as a mechanism to rewire how intelligence is created and deployed unlocks exponential value.
In this environment, speed is no longer an operational metric, it is a strategic one. Organizations must move faster not only in execution, but in learning, experimentation, and adaptation.
Gran captures this shift directly: competitiveness will be defined by “speed of learning, speed of insights, speed of deployment, speed of testing.”
This redefines what it means to be a high-performing organization. It is no longer about scale alone, but about how quickly an organization can evolve.
Traditional hierarchies, designed for stability, struggle to deliver this level of responsiveness. The implication is clear: structure must change.
The most forward-looking organizations are beginning to move away from static org charts toward more dynamic, purpose-driven structures. Instead of organizing around functions, they are organizing around outcomes.
Gran challenges leaders to confront this directly: “What if you blew it up? What if the organization looked more like this?”—a model centered on the customer rather than internal structure.
This is not a superficial change. It requires rethinking roles, incentives, and accountability. It demands that organizations continuously reconfigure themselves in response to changing needs, rather than enforcing stability for its own sake.
In this model, value is determined not by position within a hierarchy, but by contribution to outcomes.
AI is also reshaping how organizations compete. The advantage no longer lies solely in proprietary assets or scale, but in the ability to access and apply innovation faster than competitors.
Gran poses a critical question: “why would they pursue you?”—referring to startups and emerging innovators.
This question exposes a growing gap. Many enterprises still operate with processes that are too slow, too complex, and too rigid to effectively engage with the pace of innovation in the market.
Organizations that streamline how they evaluate, onboard, and scale new ideas will gain disproportionate advantage. Those that do not risk becoming isolated from the very ecosystem driving change.
Ultimately, the transition to an AI-native enterprise is not a technical challenge, it is a leadership one. Transformation at this scale requires clarity of vision, alignment of incentives, and a willingness to dismantle entrenched norms.
Gran underscores the urgency of this shift, noting that adaptation is not optional: “we won’t survive” without fundamentally changing how organizations operate.
This places a new burden on leadership. It is no longer sufficient to sponsor innovation initiatives or invest in new technologies. Leaders must actively reshape the organization to enable continuous reinvention.
That includes creating space for experimentation, redefining success metrics, and signaling that change is not only accepted but expected.
A final—and often overlooked—dimension of AI transformation is its impact on growth. Many organizations introduce AI with a focus on cost reduction. While efficiency gains are real, they represent only a fraction of the opportunity.
Gran is direct in his warning: “if you’re looking at cost cutting… you’re probably already behind.”
The true advantage lies in using AI to create new value—entering new markets, redefining customer experiences, and challenging existing business models. This requires a shift in mindset from optimization to reinvention.
The implications of AI are clear. This is not a gradual transition that organizations can navigate through incremental change. It is a structural reset that demands bold action.
“AI should not be about technology,” Gran emphasizes, “but it should be about the outcome.”
The organizations that internalize this idea will move faster, learn more quickly, and create more value. Those that do not will struggle to keep pace.
The innovation advantage will not come from adopting AI, it will come from reimagining the enterprise itself.
Watch the full presentation that inspired this article.
By Tarah Lachmandas, Avasant & Gamiel Gran, Mayfield Fund
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