Fuel Cycle Taps AI Heavyweight in Bid to Remake Market Research

LOS ANGELES, CA – June 04, 2026 – In a move that signals a seismic shift in the world of consumer intelligence, Fuel Cycle has appointed enterprise AI veteran Daryush Laqab as its new Chief Product & AI Officer. The strategic hire, effective June 1, is less a simple executive shuffle and more a declaration of intent: to transform the slow, methodical world of market research into an automated, always-on engine for business decisions.

Laqab, who brings two decades of product leadership from AI powerhouses like Google, NVIDIA, and JPMorgan Chase, is tasked with accelerating Fuel Cycle’s evolution from a research platform into what it calls a “Market Research AI.” This appointment is a high-stakes bet that the person who helped launch Google’s Contact Center AI and guided AI infrastructure at NVIDIA can build the definitive operating system for consumer insights.

“This is a pivotal moment for Fuel Cycle as we expand our technology and deepen our investment in AI-driven consumer intelligence,” said Bahram Nour-Omid, CEO of Fuel Cycle, in the official announcement. “Daryush has built and scaled products at some of the most advanced technology organizations in the world, and his experience will help drive the next phase of growth and innovation across our platform.”

The AI Talent Grab

The hiring of Laqab is emblematic of a broader trend where specialized industries are poaching top-tier talent from big tech to lead their AI transformations. His resume reads like a blueprint for building enterprise-grade AI. At Google, he was instrumental in launching large-scale products like Contact Center AI and Speech-to-Text. At JPMorgan Chase, he led the OmniAI platform, applying artificial intelligence within the complex and highly regulated financial sector. His time at NVIDIA provided him with deep expertise in the foundational hardware and infrastructure that powers modern AI.

This background is precisely what Fuel Cycle needs as it moves to commercialize its ambitious vision. The company is not just adding AI features; it is re-architecting its entire platform around a suite of “purpose-built AI agents.” Laqab’s experience sits at the critical intersection of enterprise-grade AI, complex data systems, and real-world business workflows—the very nexus where Fuel Cycle is planting its flag.

“What drew me to Fuel Cycle was the opportunity to redefine how enterprises understand and act on consumer intelligence,” Laqab stated. “Research and decision-making have historically been too slow, fragmented, and difficult to scale. AI fundamentally changes that equation, and Fuel Cycle is uniquely positioned to lead the shift toward AI-First, always-on, intelligence-driven organizations.”

Beyond Surveys: The Pivot to 'Market Research AI'

For years, market research has been characterized by one-off studies, lengthy timelines, and manual analysis. Fuel Cycle’s strategic pivot is a direct assault on this status quo. The company is developing an “Autonomous Insights Platform” that uses what it calls an “agentic AI architecture” to manage the entire research lifecycle, from designing a study to delivering a board-ready presentation.

The goal is to provide continuous, actionable insights at a speed and scale previously unimaginable. The platform aims to deliver an 85% faster time to insight, collapsing research projects that once took weeks down to under 48 hours. This is achieved through specialized AI agents that handle specific tasks. The “AI Presentation Designer,” for example, automates the creation of PowerPoint decks from raw data, a notorious bottleneck that can consume days of a researcher's time. Another feature, “AI-Powered Tags,” automates the laborious process of cleaning and analyzing qualitative data, cutting analysis time by up to 90%.

This “purpose-built” approach is Fuel Cycle’s key differentiator in a market where competitors like Qualtrics and SurveyMonkey are also integrating AI. Instead of offering a generic AI chatbot layered on top of existing tools, Fuel Cycle is building a system where AI is calibrated for specific market research methodologies, grounded in a client’s proprietary data, and focused on surfacing verifiable insights. This system is designed to provide what one industry insider called “research rigor at the speed of AI.”

The Road Ahead: Promise and Pitfalls

The demand for this kind of solution is clear. Fortune 500 brands are struggling to keep pace with rapidly changing consumer behavior and are often making critical decisions based on outdated information. The promise of a 10x lower cost per study and the ability to run 5x more research projects without increasing headcount is a powerful value proposition for over-burdened insights teams.

However, the path from prototype to profit is fraught with challenges. The technical complexity of building a reliable, autonomous research platform is immense. The system must not only perform sophisticated quantitative and qualitative analysis but also adhere to the highest standards of data security and privacy. Fuel Cycle appears to be addressing this head-on, promoting its SOC 2 Type II, HITRUST, and GDPR compliance, and assuring clients that their data is isolated and never used to train its AI models. Furthermore, all AI-generated outputs are designed to cite their sources, allowing for a “human-in-the-loop” to verify findings and prevent the “hallucinations” that plague some generative AI models.

Perhaps the biggest hurdle is not technical but cultural. A late 2023 survey revealed that AI adoption among corporate researchers remains low, with many expressing skepticism. Building trust is paramount. By hiring a leader with Laqab’s credibility and focusing on transparency and methodological rigor, Fuel Cycle is making a calculated investment to overcome this inertia. The company is betting that by augmenting, not replacing, human researchers—freeing them from tedious tasks to focus on strategic analysis—it can turn skeptics into evangelists and establish a new industry standard for turning consumer data into profitable decisions.