From data chaos to agent-ready in a day

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A recap of the May 21st Nextdata webinar with Zhamak Dehghani and Sina Jahan

The pressure on data leaders has never been greater. In our recent webinar, Zhamak and Sina laid out a clear-eyed diagnosis on why enterprises are struggling to connect their data to AI, and showed a working path forward.

Here's what we covered.

The CDO's expanding mandate, and growing pressure

Zhamak opened with a candid observation on the environment data leaders are operating in. More than half of CDOs have recently had their charter expanded to include AI. They're not just responsible for data anymore, but for data for AI and for AI itself.

The ambitions driving this churn aren't trivial. Drawing on published research from Gartner, BCG, and McKinsey, Zhamak highlighted the scale of transformation on the horizon:

  • 70% of employee time will be automatable
  • 40% of workforce tasks deemed "low value" will be replaced
  • Business will move at least 50% faster
  • 40% of enterprise applications will become agentic (a figure Zhamak called out as probably conservative)

These aren't soft predictions. They represent a fundamental shift in how work gets done — and data infrastructure has to keep pace.

The missing link between data and AI

For those targets to become reality, Zhamak argued, there's a critical gap that needs to be bridged. At the top, you have LLMs capable of planning and executing tasks from a few prompts. At the bottom, you have sprawling data — structured and unstructured, sitting in warehouses, lake houses, and SaaS applications.

The missing middle layer needs to:

  • Help agents discover relevant data
  • Provide data that is safe to use and semantically understandable
  • Do all of this fast

The three questions organizations keep asking:

  1. How do we turn fragmented enterprise data into AI-ready data products?
  2. How do we do it two, twenty, or fifty times faster?
  3. How do we do it at scale, removing organizational bottlenecks?

What is an AI-ready data product?

Zhamak offered a precise definition rather than a vague aspiration. An AI-ready data product isn't just data with a description attached — it's a running application that encapsulates and governs context at runtime. It has five critical characteristics:

1. Semantic first The product has an explicit semantic definition that establishes ground truth — not semantics extrapolated from data dumps after the fact.

2. Domain-bounded context The product is organized around the full business context of a domain. Not just the data itself, but the documentation, definitions, and language used in that business.

3. Stable business APIs Access happens through stable, business-bounded APIs — not schemas. Today, dashboards drive schema-as-interface. That has to change. APIs that expose meaning, not just tables.

4. Speed Getting to these data products cannot take months. Agents won't wait. Business won't wait.

5. Computational governance The product must be verifiable at runtime — continuously and autonomously.

Demo: A data product up close

In the next demonstration Sina showed a Git-backed source code repository where a specification file packages the different aspects of a data product: metadata, transformation logic, data inputs, and how the data is exposed to consumers.

What semantic means for agents

When an agent asks a question about sales data, it first does tool discovery — fetching context from the mesh to understand what's available around sales, channels, and regions. Then it routes specifically to the right data product.

Glossary terms are themselves exposed as data products, and the whole semantic graph becomes navigable by both humans and agents.

Zooming out: The mesh view

When different parts of an organization each define their data as products using the same interface, the result is a mesh of interconnected, semantically linked data products. The underlying technology stays where it is. The semantic links become traversable.

Zhamak: "The semantics are where the AI really needs to figure out what these concepts are so that it can answer questions more intelligently."

How to get there fast: The three-move strategy

Throwing AI at old processes won't create the exponential gains enterprises need. The problem is structural — data management was built for an era of dashboards, not agents. The pipeline of ingest → model → store → add metadata → govern → provision access → measure quality is months of manual handoffs between fragmented tools.

Zhamak's three-move strategy:

Move 1: Standardize and abstract complexity Create an interface standard that gives agents agility and predictability.

Move 2: Productize the right assets Focus effort on sources and consumer-aligned data products.

Move 3: Use AI to bootstrap Apply AI-assisted tooling to generate those data products from selected assets — fast.

Governance at scale: Computational, not documentation

Zhamak's concept of computational governance is the answer. Governance isn't documentation. It's code that runs on products.

Policies are defined at various levels and applied automatically to products matching certain criteria.

A concrete example shown in the demo: a PII policy that says if a data product contains PII data, it must not be exposed to any LLMs.

Zhamak on why this abstraction matters: "The agent is dealing with one simple interface — the data product specification. The platform is abstracting all the complexity underneath: pipelines, terminology management, contracts, catalogs."

Your options: Three paths forward

When evaluating how to move forward, Zhamak outlined three macro strategies:

Option 1: All-in with one vendor Go fully into Snowflake, Databricks, Fabric, etc.

Option 2: Build it yourself Accept multi-stack reality and build your own integration layer.

Option 3: A purpose-built abstraction layer What Nextdata OS is: an opinionated, production-tested layer that handles standardization, semantic modeling, and computational governance across any technology stack.

How to get started

For organizations ready to begin, the recommended approach is phased:

  1. Build a sandbox.
  2. Incubate.
  3. Operationalize.
  4. Self-serve enablement by customizing and tuning the agentic UX for managing and using AI-ready data products.

At Nextdata, we partner with our customers to execute the full transformation journey in months, not years.