Better AI Won’t Fix Bad Company Data: Fuel Cycle’s Daryush Laqab

Better AI Alone Cannot Solve Poor Data Quality

As businesses increasingly adopt artificial intelligence, the quality of the underlying company data is becoming an important factor in determining whether AI systems can deliver useful results.

Daryush Laqab, Chief Product and AI Officer at Fuel Cycle, has emphasised that organisations cannot expect increasingly powerful AI models to compensate for weak or fragmented internal data.

The issue is particularly relevant as companies move from experimenting with AI chatbots to deploying AI agents across business functions. Recent industry data cited in the report shows that around 40% of large organisations have reported scaling AI agents across different functions.

Why Company Data Matters for Enterprise AI

AI systems depend heavily on the information they receive.

If customer records, transaction data, behavioural information and research findings are stored across disconnected systems, an AI model may not have enough context to generate reliable business insights.

Better models can improve reasoning and processing capabilities, but they cannot automatically create accurate information where the underlying data is missing, outdated or inconsistent.

This makes data infrastructure an important part of an organisation's AI strategy.

AI Agents Are Increasingly Being Used by Businesses

Companies are moving beyond traditional software automation and experimenting with AI agents that can perform multi-step tasks.

These systems can analyse information, generate reports, interact with customers and support decision-making.

However, their effectiveness depends partly on whether they can access relevant and trustworthy enterprise information.

The growing adoption of AI agents therefore increases the importance of data integration, governance and quality control.

Fuel Cycle's Approach to AI and Consumer Intelligence

Fuel Cycle describes itself as an AI-powered consumer intelligence platform that brings together owned audiences, behavioural and insights data, research methods and AI.

Its platform connects customer information with quantitative research, qualitative research and user-experience research.

The company's approach is based on giving AI access to an organisation's existing research context rather than relying only on a generic AI prompt.

Connecting Customer and Behavioural Data

Fuel Cycle's platform can connect information such as CRM records, transactions, product usage and digital behaviour with persistent customer profiles.

This can allow companies to identify specific customer groups and then conduct research with those audiences.

According to Fuel Cycle, the objective is to connect what customers do with what they say, allowing businesses to build a more continuous understanding of consumer behaviour.

AI Needs Context, Not Just More Computing Power

The discussion highlights a broader issue in enterprise AI.

Companies often focus on model capabilities, computing infrastructure and access to the latest AI systems. But AI applications also require high-quality organisational information.

For example, an AI system analysing customer churn may produce different results depending on whether it has access to accurate customer histories, product usage, transactions and previous research.

Without that context, even a sophisticated model can have limited usefulness for a specific business.

Fuel Cycle Expands AI and Product Operations in India

The company has also been expanding its technology operations in India.

Fuel Cycle recently opened a Global Capability Centre in Vashi, Navi Mumbai, with an initial planned capacity of 50 employees. The centre is expected to expand its engineering, AI and product-development teams over the coming quarters.

Fuel Cycle said the centre would work across areas including AI and machine learning, data engineering, product engineering, user experience, research technology and cloud infrastructure.

Daryush Laqab's AI Background

Laqab joined Fuel Cycle as Chief Product and AI Officer in June 2026.

Before joining the company, he held AI and product leadership roles at organisations including Google, NVIDIA and JPMorgan Chase. Fuel Cycle said his experience includes work on AI infrastructure, enterprise platforms, speech recognition and other AI products.

His role at Fuel Cycle involves leading the company's AI and product strategy as it develops its AI-native consumer intelligence platform.

From Traditional Research to AI-Native Insights

Fuel Cycle is positioning its platform around continuous consumer intelligence rather than isolated research projects.

Its current platform combines owned audiences, research methods, behavioural data and AI-powered analysis. The company says this approach can help businesses move from collecting research to continuously building and using consumer knowledge.

The strategy reflects a wider shift in enterprise technology, where AI is increasingly being integrated into existing data and workflow systems rather than being used only as a standalone chatbot.

What Businesses Need to Consider Before Scaling AI

The discussion around enterprise AI suggests that companies need to look beyond model selection when preparing for wider AI deployment.

Important areas include:

• Data quality and accuracy

• Integration between enterprise systems

• Consistent customer and product records

• Data governance and security

• Access to historical research

• Clear ownership of enterprise data

• Monitoring AI-generated outputs

• Reliable information for AI agents

Improving these foundations can be important when organisations move from small AI experiments to large-scale deployment.

Key Takeaways

• Fuel Cycle's Daryush Laqab says better AI models cannot by themselves solve poor company data.

• AI agents are increasingly being deployed across business functions.

• Enterprise AI needs access to accurate and relevant organisational information.

• Fragmented customer, behavioural and research data can limit AI's usefulness.

• Fuel Cycle connects consumer data, research methods and AI through its platform.

• The company is expanding its AI and product capabilities through its Navi Mumbai GCC.

• Laqab joined Fuel Cycle after AI and product leadership roles at Google, NVIDIA and JPMorgan Chase.

• The broader enterprise AI trend is shifting toward AI systems that can work with proprietary company data and research context.