I’m thrilled to sit down with Dominic Jainy, a seasoned IT professional whose deep expertise in artificial intelligence, machine learning, and blockchain has positioned him as a thought leader in reimagining data architecture for enterprise AI. With a passion for applying cutting-edge technologies across industries, Dominic offers unique insights into how businesses can overcome the hurdles of scaling AI in complex data environments. In our conversation, we explore the critical mismatches between traditional data systems and AI needs, the importance of real-time access and contextual understanding, and the future of collaborative, AI-ready architectures.
How do you see the fundamental differences between the data needs of AI agents and human analysts in enterprise settings?
The core difference lies in speed and scope. Human analysts typically work within defined domains, using curated datasets and dashboards at a pace that aligns with business cycles—think daily or weekly reports. They can wait for scheduled data updates. AI agents, on the other hand, operate in real-time, often needing to pull data from across the entire enterprise to make split-second decisions. A customer service bot, for instance, can’t pause a conversation because the data warehouse hasn’t refreshed. AI also lacks the inherent business knowledge humans bring, so it requires structured access to context to avoid missteps. Traditional architectures just weren’t built for this level of immediacy or breadth.
What makes real-time data access so critical for AI agents compared to the batch processing common in most enterprise systems?
Batch processing, where data is updated hourly or daily, works for humans because we can plan around it. But AI agents need to act instantly. Imagine a fraud detection system that only gets updated transaction data once a day—it’s useless for stopping a suspicious payment in progress. AI is expected to deliver on-demand responses, much like consumer-facing tools we use every day. If a system can’t provide fresh data right when it’s needed, the AI’s effectiveness plummets, and trust in its outputs erodes. Real-time access isn’t a luxury; it’s a baseline requirement for AI to function in high-stakes environments.
Can you elaborate on what you call the ‘context chasm’ and how it impacts AI performance in enterprise data environments?
The context chasm refers to the gap between raw data and the business meaning behind it, which AI agents often can’t bridge on their own. Human analysts know, for example, that ‘revenue’ might mean different things in different departments or that certain datasets have quirks. AI just sees tables and numbers. Without access to business glossaries, lineage, or domain knowledge, it can produce what I call ‘confident hallucinations’—answers that sound right but are dangerously wrong. For instance, an AI might misinterpret sales data by ignoring seasonal trends it wasn’t explicitly taught. This lack of context leads to flawed decisions that can cost businesses dearly.
How can enterprises start to address this gap when business context is scattered across various tools and systems?
It’s about creating a unified layer of contextual intelligence. Enterprises need to pull together metadata, business definitions, and tribal knowledge from wherever it lives—whether that’s in data catalogs, documentation, or BI tools—and make it accessible to AI in real-time. This isn’t just a one-time fix; the context has to evolve as business rules or data sources change. Think of it as a dynamic knowledge base that grounds AI outputs in reality. It’s a heavy lift, but without it, you’re essentially letting AI guess at critical interpretations, which is a recipe for disaster.
What challenges do AI agents and business users face with self-service in traditional data systems, and why does the old model fall short?
Traditional self-service in BI tools is built on a single-shot, request-response model. You ask a question, get an answer, and that’s it. This works for humans in discrete sessions but fails for AI-driven workflows that need iterative, high-speed interactions. AI agents and business users often require follow-up questions to refine insights, and they need to collaborate with data teams to ensure accuracy. Without a multi-step, collaborative approach, the analytics lack depth and reliability. Current systems aren’t designed for this back-and-forth, leaving both AI and users frustrated by incomplete or untrustworthy results.
Could you walk us through what a collaborative, iterative self-service model looks like for AI and business stakeholders?
Picture a workflow where an AI agent starts by answering a business user’s query, say about sales trends, using all available enterprise data. The user then asks a follow-up to drill deeper into a specific region. The AI refines the output, pulling in more data, while simultaneously looping in a data analyst to validate assumptions or add context like a recent market shift. This back-and-forth continues until the insight is solid, and the final ‘data answer’ includes not just numbers but the reasoning and lineage behind them. It’s a dynamic process where AI, users, and data teams build on each other’s input, creating trusted outcomes at machine speed.
What core principles do you believe are essential for building a data architecture that supports AI at scale?
I see three pillars as non-negotiable. First, unified data access—AI needs real-time, federated access to all enterprise data without moving or duplicating it, ensuring speed and security. Second, unified contextual intelligence, which equips AI with the business and technical understanding to interpret data correctly, preventing errors. Third, collaborative self-service, moving beyond static reports to dynamic data products that AI agents, users, and teams can build and share together. These principles shift the focus from human-centric to AI-ready systems, enabling scalability and trust in automated insights.
Looking ahead, what is your forecast for the evolution of data architectures as AI continues to transform enterprises?
I believe we’re heading toward more open, flexible data architectures that prioritize interoperability over monolithic platforms. As AI use cases grow, enterprises will demand systems that connect seamlessly with diverse tools and data sources without forcing everything into a single vendor’s ecosystem. We’ll see data fabrics become the backbone, supporting real-time access, rich contextual layers, and multi-agent collaboration. The focus will shift to adaptability—architectures that can evolve with AI advancements rather than constrain them. Those who invest in this flexibility now will be the ones leading the AI revolution in the next decade.