CDO Adastra Advisory Board: AI-Powered Data Management Delivery

Martin Benes, CDO, Dr. Max Group; Marius Maslo, Regional CEO, Adastra

IT delivery has long been priced and planned around capacity, counted in man-days, FTEs, and sprints. AI-assisted delivery is breaking that model, delivering work faster, with fewer people, at a fraction of the cost.

Dr. Max opens the session, sharing how the organization rolled out AI-assisted delivery across its teams and what changed as a result.

We then look at what it takes to get there, from the investments behind an AI-assisted SDLC (tooling, integration, governance) to the organizational shifts needed to be ready (new skills, new processes, new ways of measuring output).

Finally, the harder question every CDO now faces alongside CFOs and procurement. If delivery no longer scales with headcount, how should you buy it? We discuss the shift from capacity-based procurement toward value-based and outcome-based models, and the considerations that come with it.

Luboslav Gabal, Head of GTM, Adastra

Following Dr. Max’s example, the Adastra team unpacks our approach. We embed AI across the full data platform lifecycle, from requirements to pipelines, testing, and governance, with a human quality gate at every step. We share an observed maturity curve of the organizations we work with, showing how far along they are with AI-assisted delivery, and the governed toolstack behind it, drawn from projects delivered for clients.

Now over to you: How far along is your organization? What are your priorities, and what do you expect from vendors and suppliers? Let’s trade wins, challenges, and lessons learned.

Context is king. In the era of AI agents, ontologies and semantics are the central theme at the leading conferences, from Gartner to the hyperscalers. Semantic layers and knowledge graphs are the bedrock of agentic AI. A governed, machine-readable model of what your data means (entities, relationships, rules) is what decides whether your agents return trustworthy answers. Supported by examples from recent projects and hackathons with pharma, energy, and utility clients.

Now over to you: Where are you with ontologies, and what is your approach and plan?

T-Mobile is aiming to retire 70% of its active BI reports through data agents. Data agents are moving from pilot to production fast. Gartner expects up to 40% of enterprise applications to embed task-specific agents by the end of 2026, though it warns that over 40% of agentic projects stall on cost, unclear value, and weak controls.

T-Mobile had encouraged teams to launch AI use cases freely. Budget reality is now setting in, and every use case is being weighed on cost and value. Learn how the T-Mobile team uses precise data governance to ensure data agents do not hallucinate in chat with your data scenarios.

Now over to you: Where are you currently, and which data management capabilities would you agent-enable first?

The question is no longer whether to adopt, but how fast you can scale effectively.

We will showcase how our prominent banking client stood up an enterprise-scale agentic platform on Adastra’s Agate LLM gateway, with governance, cost control, and compliance built in. First agents are already live across marketing, compliance (AML and KYC), and customer service, delivering scalable deployment, less manual work, and faster resolution.

Now over to you: How do you build an AI operations layer, and which tooling do you lean on?