Expert Hub: AI, ML & Data Engineering
There’s been a lot of noise around data and AI. But for those of us building real systems and working closely with enterprise clients, what’s happening now is less about buzz and more about pressure to deliver faster and smarter.
AI adoption does not unfold randomly. It progresses through a series of development milestones. Organizations may move at different speeds, but the sequence remains recognizable.
Context is incredibly important. Goals, problems, solutions, and milestones all define what matters at any given point, and different agents need different context to operate effectively.
The demand for AI-driven infrastructure requires more than technical capacity. It requires specialized governance and architectural precision. Roman and Jeremy bring the depth needed to deliver governed, tangible outcomes for our clients building on Google Cloud, as part of DataArt's broader commitment to AI and data-driven engineering.
Most AI initiatives in retail stall before delivering real results. The issue isn’t the model, but the data behind it. Without a strong data foundation, AI remains stuck in experimentation instead of driving outcomes.
AI use cases move fast. Organizations expect delivery in days or weeks, not months. When data architecture relies on bringing everything to one centralized place, that architecture becomes a bottleneck.
The AI becomes a channel for the university's specific knowledge, not a replacement for it. A product enablement layer of shared libraries and deployment infrastructure could help turn student projects into working tools for the university itself. At that point, the institution stops teaching about AI in theory and starts operating through it.
Intelligent document processing for aviation is becoming a control layer. It turns PDFs, scans, and screenshots into auditable, system-ready data so modern platforms can run with fewer manual handoffs. Airlines, airports, ground handlers, and MRO providers have all invested heavily in modern systems. Yet many programs still slow down in the same place: documents. Critical operational and financial inputs still arrive as artifacts, and systems still can’t reliably convert them into usable data at scale.
Industry estimates put the share of AI agent projects that never reach production at around 80%. The failure almost always originates in the operational layer underneath the model, not the model itself. Standard LLM wrappers perform well enough in a proof of concept, where inputs are curated, sessions are short, and nobody depends on the output for anything consequential. Under production conditions, that changes.
I believe that many organizations underestimate how operationally demanding it is to maintain the full AI lifecycle in compliance. There are several very big groups of requirements for medical software development, AI, cybersecurity, as well as data governance and privacy.
AI in the music industry has reduced the cost of creating and distributing music to near zero. That is great for experimentation, but it creates a platform-scale problem for labels, publishers, DSPs, and rights orgs: identity, provenance, and monetization must now work across hundreds of millions of assets, in near real time.
Legacy policy administration systems answer basic questions exceptionally well, such as did we collect the premium? They calculate commissions accurately, track reserves properly, and produce financial statements that auditors approve without exception. Ask those same systems which buildings you're insuring in London, and they go silent.
Asset managers do not have an AI problem. They have a modern data infrastructure problem. Most firms can run pilots that look promising. A small team. A curated dataset. A narrow use case. Then production reality shows up: inconsistent definitions, uneven refresh cycles, unclear lineage, and governance that arrives too late to build trust. Adoption slows. Risk teams intervene. The pilot never becomes a repeatable capability.
Microsoft Fabric is often evaluated through the lens of features or migration scope. That framing misses its real significance. Fabric is less about replacing individual tools and more about redefining how data platforms are structured, governed, and operated. Fabric is best understood as a platform consolidation strategy rather than a traditional migration project.
If someone asks how much data is enough to validate AI, the honest answer is there’s no fixed number. And, more importantly, regulators don’t expect one, instead they anticipate a justified, risk-based rationale.
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