Building Compliance-Aware AI for Regulated Industries: A Data Infrastructure Leader’s View
The emergence of AI in enterprise applications has been nothing short of explosive. According to a recent article by McKinsey (2024) on the State of AI report, 72 percent of organizations now utilize AI in one or more business functions, a significant increase from 55 percent the previous year. By 2026, global spending on AI systems is projected to exceed $300 billion. In 2024 alone, venture financing for AI startups reached over $50 billion, with real estate technology (proptech) emerging as one of the fastest-growing sectors.
However, the implementation of AI poses significant challenges, particularly in regulated industries. Many builders are discovering the harsh reality that while integrating AI may seem straightforward, ensuring that it does not impose legal liability or regulatory risk is complex. The gap between a prototype and a compliant, enterprise-ready system is where many promising AI projects fail. This issue highlights the need for individuals who can navigate both rigorous enterprise data infrastructure and agile startup product development.
The Role of Data Infrastructure
One key figure in this landscape is Piyush Tiwari, a seasoned expert with over 10 years of experience in data engineering, infrastructure, and AI platforms. As the Senior Manager of Engineering at Wayfair, Tiwari oversees a team of 16 engineers managing data infrastructure that processes more than 20 terabytes per day across 80+ Kafka clusters. His expertise is now directed towards ResidenceHive, a proptech initiative aiming to introduce compliance-aware AI into real estate brokerages.
ResidenceHive addresses two critical trends: the influx of AI tools into regulated sectors and the growing realization that most of these tools are not designed with regulatory realities in mind. The real estate sector, along with healthcare and finance, faces stringent regulations. In the U.S., fair housing laws prohibit discrimination based on various factors including race and religion. AI systems that suggest neighborhoods or filter leads can inadvertently violate these laws, creating significant legal risks for brokerages.
Compliance Blind Spots
Tiwari emphasizes that the industry suffers from a compliance blind spot. As businesses rush to leverage AI for lead conversion, few consider the ramifications of AI providing incorrect information. The notion that “the AI said it” is not a valid defense in regulated industries. Organizations must establish auditable systems with guardrails to track every recommendation and interaction.
At Wayfair, Tiwari’s teams have developed reliable systems that prioritize data governance and lineage tracking, ensuring that data integrity is maintained while supporting one of the largest e-commerce operations in home goods. The same principles are applied to ResidenceHive, which serves as a first-line layer between lead reception and CRM systems. Upon receiving a new lead from sources such as Zillow or Realtor.com, ResidenceHive qualifies the lead and interacts via a chatbot designed with compliance guardrails.
Real-World Applications
ResidenceHive is currently operational in pilot projects across Massachusetts and Ontario. The feedback received highlights not just the sophistication of the AI but the critical element of trust. Agents have reported previous experiences with chatbots that led to potentially damaging replies, emphasizing the importance of compliance in their interactions. This platform aims to save agents time while ensuring that their operations remain within regulatory frameworks.
The Future of AI in Regulated Industries
Tiwari advocates for embedding compliance within AI systems rather than treating it as an afterthought. The next era of AI in regulated industries will favor teams that view compliance as a product feature. Many current AI deployments are either overly cautious or dangerously naive regarding regulatory complexities. Future success will belong to systems that can navigate these nuances, providing helpful responses while maintaining necessary guardrails and generating audit trails expected by regulators.
In conclusion, the transition from enterprise infrastructure to startup innovation is not a departure but a direct application of experience. The principles guiding data systems at scale are equally applicable to AI systems in regulated industries. Amidst a flood of AI tools promising transformation, the foundation of trust may be the most valuable resource of all.
