At the Databricks Data + AI Summit, Artefact brought a clear message to enterprise AI leaders: the next phase of AI adoption will not be won by models alone. It will be won by organizations that can build the trusted data, governance, and semantic foundations required to move agents from pilots into production.
Artefact, the AI-native company that fuses engineering-grade delivery with consulting-level thinking for proven high-impact results, showcased production-oriented agentic architectures with live agents, real data, and Databricks-powered demos across marketing measurement, financial services, beauty, CPG, and logistics.
A central theme emerged from Artefact’s presence at Databricks – enterprise AI cannot scale on retrieval alone. While RAG, metadata catalogs, and copilots help companies experiment quickly, production-grade AI agents require something deeper: an ontology layer that can resolve business meaning, determine authority, and reason across relationships.
That is where lessons fromArtefact, become especially relevant. Their view is that many organizations are discovering the same hard truth: AI agents can only act with confidence when the enterprise has clarified what its data means, who governs it, and how business concepts connect across systems.
Why RAG and metadata catalogs are not enough
For many companies, retrieval-augmented generation has become the default starting point for enterprise AI. Artefact argues that RAG has structural limits when it comes to governed, production-ready decision-making.
Akhilesh Kale (AK), Partner at Artefact, explains:
“A vector search returns passages that look like your question. It has no view on whether the number in that passage is the one your CFO signed off, or how ‘active customer’ joins to ‘contract’ three systems away. Those are the two things RAG structurally cannot do: resolve authority and reason across relationships. An ontology fixes both. It ranks definitions by who authored them, how often they are used and how close they sit to certified assets, and it encodes the relationships that turn a question into a computed answer rather than a retrieved guess. Catalogues inventory your data. Ontologies adjudicate it. That gap is the difference between a demo and something that can function in a regulatory environment.”
The distinction matters because enterprise AI is moving from answering questions to taking action. In that world, a “close enough” answer is not enough. Agents need to understand which definition is authoritative, which data asset is certified, and how concepts relate across finance, customer, product, legal, and operational systems.
That is especially important in regulated industries such as financial services, insurance, healthcare, energy, and telecommunications, where explainability, lineage, permissions, and governance are not optional.
The hidden challenge: stewardship, not technology
Artefact’s Databricks message also challenges the idea that the hardest part of enterprise AI is the technical build. Kale, many initiatives stall not because the technology fails, but because organizations underestimate the human and governance work required to sustain it.
Akhilesh Kale (AK), “The build phase is not where these stall. They stall in two places that teams treat as afterthoughts. The first is that auto-extraction produces a draft, not a decision: when it surfaces three definitions of margin, someone with authority has to choose, and that is a political act most organisations work hard to avoid. The second is decay. A definition that was correct in Q1 is wrong after a reorg or a system migration, so the graph needs instrumented pipelines and a named owner, not an annual refresh. Underwrite the stewardship or the ‘living graph’ quietly dies.”
This is one of the core lessons Artefact emphasizes: AI transformation is not just about deploying platforms or models. It is about redesigning how organizations manage meaning, ownership, accountability, and change.
A living ontology requires active stewardship. Business definitions change. Systems migrate. Teams reorganize. Regulatory expectations evolve. Without ownership and instrumentation, the semantic layer becomes stale — and once that happens, agents lose trust.
Governance moves into the runtime path
The third lesson is that modern AI governance must operate in real time. It is no longer enough to govern what data a system can access at the beginning of a workflow. Production AI agents need governance embedded into the runtime path, controlling what an agent is allowed to do while it is acting.
Akhilesh Kale (AK) further explains:
“Yes, with a hard prerequisite: the real advance is that governance moves into the runtime path: Unity AI Gateway polices what an agent is allowed to do mid-task, not merely what it can read, and there is no second permission system to keep in sync. For banking, insurance and asset management, that is the difference between a pilot and a deployment. But the layer inherits trust; it does not create it. It is a multiplier on the data foundation you already have, and a multiplier keeps the sign: the firms that did the governance work get an immediate lift, the rest get a faster, more confident version of a mess they had not looked at directly.”
That point connects directly to Artefact’s broader position: production-ready agents require trusted foundations. Databricks can provide the governed data layer, but enterprises still need to do the hard work of defining ownership, resolving ambiguity, and redesigning operating models around human-agent collaboration.
From pilots to production-ready agentic systems
Artefact showcases what this looks like in practice, with live agentic use cases built on Databricks, including:
- A conversational marketing measurement tool that lets executives simulate budget allocations through natural language.
- Synthetic personas for financial services and CPG, enabling marketers to interrogate simulated customer profiles for faster insight.
- An AI beauty shopping assistant supporting personalized product discovery across a 60,000+ SKU catalog.
- Corrie, a logistics resolution AI assistant that helps warehouse teams resolve missing or swapped items faster.
Across these examples, agents become valuable when they are connected to accurate, governed, enterprise-ready data, and when organizations redesign the human roles and governance structures around them.
For Artefact, the lesson from Databricks is clear. The future of enterprise AI is not a collection of pilots, copilots, or disconnected assistants. It is a production-grade hybrid organization where humans and AI agents operate through shared data foundations, governed workflows, and clearly defined accountability.
The model is not “AI instead of people.” It is people moving up the value chain, from doing to deciding, from producing to judging, and from executing processes to governing the systems that run them.
Artefact is the AI-native company with engineering-grade delivery and consulting grade-thinking for proven high-impact results. The team specializes in accelerating data & AI transformation and AI/data-driven marketing to drive tangible business outcomes across the entire enterprise value chain, with a focus on top and bottom-line business value. Artefact offers the most comprehensive set of data-driven solutions per industry, built on deep data science and cutting-edge AI technologies, delivering AI projects at scale in all industry sectors.
From strategy to design to implementation, Artefact offers an end-to-end approach and solutions: data & AI strategy, data quality and governance, data platforms, AI Factory, data-driven customer experience, and marketing ROI. Our 2000 employees operate in 27 countries (Americas, Europe, Asia, Middle East, India, Africa) and we partner with 1000+ clients.
Akhilesh Kale (AK) is a Partner at Artefact, a global data and AI consultancy, where he leads the Financial Services data and analytics practice in the US. A trusted advisor to CDAOs, CIOs, and business leaders, AK specializes in driving tangible business outcomes by bridging the intersection of finance and cutting-edge technology. Over his career, he has helped Fortune 100 financial institutions build next-gen data products and AI platforms that accelerate revenue growth, boost operational efficiency, and ensure regulatory compliance. A recognized thought leader, AK frequently shares his insights on the future of Data & AI at global conferences and industry forums.
