‘Simplicity Is What Lets You Be Brave’: Options Technology VP Says Streamlined Stacks are the New Competitive Edge

As enterprise leaders push to scale AI, modernize data platforms, and meet rising performance and regulatory demands, many are discovering that complexity, not capability, is their biggest constraint. Enterprises are pouring billions into AI infrastructure, yet for many organizations, this spending isn't translating to innovation. Legacy infrastructure models built around prediction, over-provisioning, and static capacity are colliding with a world that now demands speed, elasticity, and constant experimentation. The solution for enterprises in this environment requires a radical focus on implementing simplicity.

We spoke with Andrea Moccia, Vice President of AI and Data at Options Technology, a global provider of IT infrastructure for capital markets. Moccia is a seasoned infrastructure expert with a background in infrastructure leadership and years of experience supporting some of the world’s most demanding capital markets environments. He has been instrumental in building high-performance, low-latency systems that prioritize reliability, simplicity, and predictability at scale turning storage from a bottleneck into a growth engine. Moccia's take on what other enterprise leaders can learn as they navigate an increasingly volatile AI-driven market offers a sharp counterpoint to the industry's obsession with "complexity for its own sake."

"Simplicity isn't just about operational efficiency. Simplicity is what lets you be brave. When your infrastructure is simple, experimentation becomes reversible, fast, and cheaper than the complex alternative," says Moccia. For him, simplicity has become a strategic advantage to achieve faster innovation, more resilient operations, and a solid foundation for AI-driven services without the drag of legacy constraints.

In his experience, spinning up a new environment, testing a hypothesis, and piloting an AI use case "just happen" in a simplified environment. "When your infrastructure is complex, every change is a risk. Every pilot needs a committee. And innovation dies in the approval process," he argues. For Moccia, the danger of overly complicated systems is that their cost is hidden; because many leaders can’t easily see it on a balance sheet, they may not realize they’re paying it. That lack of visibility can lead them to fall into three main traps that undermine their own success: issues that are not just technical, but represent foundational flaws in strategy and talent management.

• A broken foundation: Without unified, accessible data and a foundation built for change, even the most advanced models struggle to deliver meaningful outcomes. From Moccia's perspective, "Enterprises are layering AI on top of a broken foundation and expecting magic. They prioritize compute and talent, but their data is still trapped inside of silos where it can't feed the models." This creates the potential for AI strategy to be reduced to nothing more than "An expensive science experiment, disconnected from any real business application," he adds. For example, an MIT study reported most GenAI projects fail, but Moccia believes "it's not because the AI isn’t good, they fail because the data is scattered across six systems, and by the time you stitch it together, it’s already stale."

• Complexity's corrosion: As AI, data, and infrastructure stacks expand, many organizations mistake architectural sprawl for progress. "The real mistake is confusing complexity with capability. Complexity isn’t just inefficient. It’s corrosive. Teams keep layering tools until they’re running 15 platforms that each deliver a fraction of the value, with none of them truly integrated." What looks impressive on paper often masks fragility, rising costs, and systems that are increasingly difficult to operate, govern, or scale. "The pilot might look great in a demo, but the moment you try to scale it into a real workload, the infrastructure collapses under its own complexity." Moccia adds that 83% of technology leaders believe AI-driven demand will cause their data infrastructure to fail within 24 months.

• Losing the talent war: As infrastructure stacks grow more complex, the impact extends beyond systems and budgets—it directly affects an organization’s ability to attract and retain talent. As Moccia notes, "If your infrastructure requires a PhD to operate, you have lost the talent war before it even started, because you are building dependencies on people you cannot hire."

To navigate these obstacles in an environment where AI demands are in constant flux, Moccia’s strategy prioritizes adaptability. He explains the goal of CIOs, CISOs, and other leadership is to play the long game like a chess grandmaster, whose skill lies in making moves that consistently keep the most options open. The sheer volatility of the AI market—where a standard like MCP can be celebrated one year and its enterprise readiness questioned the next—validates Moccia's argument for building for agility over rigidity. It's a philosophy that has directly influenced how Options evaluates and partners with infrastructure vendors, like Pure Storage, that are constantly innovating their own offerings.

• The simple philosophy: Moccia believes enterprise leaders should reframe simplification as a core leadership competency rather than a mere technical chore. "You can't retrofit simplicity; it's a trap. It's either a day-one design decision or an innovation project you never finish." He points to the philosophies of Colin Chapman, founder of Lotus Cars, and French writer Antoine de Saint-Exupéry, to illustrate that perfection is achieved not when there is more to add, but when there is nothing left to take away—a principle that increasingly defines how modern infrastructure must be designed to scale. "Start with the data layer, every workload and every AI model touches data. If you simplify there, the benefits cascade across the entire organization."

At Options, that philosophy translates directly into speed. Moccia says time-to-value often "dies in the plumbing," buried in technical and organizational friction long before a user ever sees an application. Before shifting to a flexible, as-a-service model, the company was trapped in a familiar dilemma. "We were forced to make a difficult choice: either over-provision massively and bleed money on idle capacity, or right-size for today and then scramble to meet demand the moment a client scaled." The result, he explains, was capital tied up in defending hypothetical growth instead of fueling innovation, resilience, or customer value.

• A familiar story: Moccia warns that without a focus on predictable costs and data control, the industry is poised to repeat its past mistakes. "It's the cloud story all over again. A decade ago, everyone rushed to public cloud because it looked cheap, only to be hit with massive bills that led to repatriation. We're watching the exact same cycle with public LLMs," he says.

By removing unnecessary friction in their infrastructure layer, Options was able to shorten development cycles and respond to market opportunities without waiting on long procurement timelines. The launch of its internal AI platform, PrivateMind, was enabled by a simplified foundation that eliminated the traditional dependency on pre-purchased capacity.

• Eliminating procurement bottlenecks: An unanticipated benefit of leveraging Evergreen//One was the decoupling of the hardware delivery from the subscription commitment. In his words, "The new model allows hardware to be deployed immediately, letting teams build right away, while the financial commitment only begins with active consumption."

Ultimately, the lesson for enterprise leaders isn’t about chasing the next technology wave. It’s about building foundations that can absorb change without forcing reinvention. Options’ experience underscores how decisions made to solve today’s operational challenges can quietly determine whether an organization is ready for tomorrow’s disruption. As Moccia puts it, "When we chose our foundation seven years ago, we weren’t predicting the rise of AI—we were solving a storage problem. But because we chose a foundation that could change with us, we didn’t have to rebuild when the AI wave hit; we just extended what was already there." In a market defined by uncertainty, attempts to forecast what comes next often lead to rigid, overbuilt systems. The more durable strategy, Moccia argues, is to design for adaptability so when the next inflection point arrives, readiness is already built in.