For two decades, enterprise software revolved around applications. Each department had its killer app — Salesforce for sales, Workday for HR, Tableau for analytics. That era is ending. Generative and conversational AI are dissolving those boundaries, turning conversation itself into the universal interface.

In this new model, data becomes the medium of conversation — not the output of a report. Every employee can ask a question in natural language and get an intelligent, context-aware answer. Suddenly, everyone becomes a data consumer.

For Chief Data Officers (CDOs), this changes everything. The mandate is no longer just governance or pipelines; it’s about enabling safe, contextual, on-demand access for both humans and the AI agents that increasingly act on their behalf.

A new mandate for the Chief Data Officer

Conversational AI reframes what it means to deliver data value. In the app-centric world, CDOs focused on pipelines, catalogs, and dashboards. In the conversational world, they must design data experiences — fluent, governed interactions that meet users where they already work: Slack, Teams, email, notebooks, or AI copilots.

The new question isn’t “How do I deliver more dashboards?” It’s “How do I ensure every conversation — human or machine — has access to the right data, at the right level of trust, with the right guardrails?”

The CDO’s role shifts from steward to orchestrator: from enforcing governance to engineering it into every interaction.

Everyone becomes a data consumer

When AI is embedded directly into enterprise tools, the data literacy gap narrows overnight. People no longer need SQL skills or BI training to interrogate data — they can simply ask.

That democratization is powerful for productivity, but risky for unprepared organizations. Query volumes surge. Contextual risk — where an innocent question surfaces sensitive data — rises sharply. The static access model built for pre-approved datasets can’t keep up.

The answer lies in dynamic, intent-aware provisioning: Systems that infer context from conversation, evaluate policy in real time, and deliver governed data instantly. It’s a shift from permissioning to provisioning — from one-time approvals to continuous orchestration.

The rise of the technical creative — and the agent

When technical barriers to data disappear, every knowledge worker can query, analyze, and act on it using simple natural language. And among them, a new archetype is emerging: The technical creative.

These are domain experts who think like system designers. In the past, they were the “power users” of Excel or Tableau. Now, with conversational AI, they’re building workflows through dialogue. They’ll soon deploy AI agents that monitor, analyze, and act on data continuously.

Most of those agents won’t come from engineering — they’ll be created by these new data consumers. Each agent represents a continuous data consumer, querying and generating outputs around the clock.

This explosion of both human and digital consumers means governance must scale exponentially. Policies built for human access must now account for machine behavior. Lineage must extend beyond datasets to conversational contexts. Observability must track how data flows through thousands of agents that think, reason, and sometimes hallucinate.

In short, the CDO is no longer managing a platform — they’re managing an ecosystem.

From pipelines to policies

Traditional provisioning frameworks were built for predictability: Known users, known datasets, known uses. Conversational AI obliterates that predictability. The user can be anyone; the question can be anything.

Data provisioning must now evolve along three axes:

1. Context-driven access: Evaluate every request at query time based on who’s asking, what they need, and why. Attribute-based access control and policy-as-code become foundational.
   
2. Governance by design: Governance can’t be a gate — it must be embedded in the provisioning layer, classifying, masking, and approving data at conversational speed.
   
3. Conversational interoperability: The enterprise data fabric must connect securely to AI ecosystems — OpenAI, Anthropic, Gemini, and conversational layers inside Snowflake, Databricks, and Microsoft Copilot — without bypassing governance.

The CDO’s job is to architect this layer so conversational AI inherits, rather than evades, enterprise controls.

Operationalizing governance at conversational speed

To measure success, CDOs need new metrics beyond datasets cataloged or reports produced. The following five define agility and trust in a conversational enterprise:

• Provisioning Latency: Time from conversational data request to governed fulfillment.
  
• Trust Score: Percentage of AI-generated responses validated as compliant and contextually appropriate.
  
• Governed Coverage: Share of enterprise data available through governed conversational endpoints.
  
• Adoption Velocity: Growth in unique conversational users and active agents accessing governed data.
  
• Policy Automation Ratio: Percentage of access decisions resolved automatically by policy rather than manual review.

Together, these metrics balance speed, trust, and reach — the true measures of modern data provisioning.

Governance and security in the age of conversation

Conversational interfaces introduce new risks. Data can leak not just through queries, but through context. A prompt might indirectly reveal sensitive information, or an agent might infer relationships never intended for exposure.

To manage this, CDOs and CISOs must build governance-as-infrastructure:

• LLM-aware data classification tagging datasets for sensitivity and AI suitability.
  
• Prompt firewalls that filter or rewrite unsafe queries before they reach protected systems.
  
• Synthetic risk scoring to assess outputs for hallucination or leakage.
  
• Joint observability dashboards to trace who—or what—accessed which data and under which policy.
  

The goal isn’t to slow adoption, but to govern at the speed of conversation—turning governance into an accelerator of innovation, not a constraint.

Building the Conversational Data Infrastructure

Every CDO will need a provisioning architecture that extends gracefully into conversational AI ecosystems. That includes:

• Metadata-driven APIs enriched with lineage and policy metadata for safe, transparent AI queries.
  
• Contextual identity frameworks that evaluate identity, purpose, and sensitivity in real time.
  
• Policy-driven connectors that broker secure exchanges between enterprise data clouds and conversational platforms.
  
• Observability fabric that tracks every interaction—who asked what, which data was used, and whether governance rules held.
  

Think of it as a Conversational Data Mesh: Each domain exposes governed data products through APIs that conversational platforms can consume safely.

The CDO as conversational architect

The age of conversational AI isn’t another analytics trend — it’s a redefinition of enterprise data itself. The database, dashboard, and workflow all converge in dialogue.

In this world, the CDO’s mission expands to:

• Make data universally accessible but contextually safe.
• Provision not just pipelines, but experiences.
• Govern not just datasets, but dialogues.

The CDOs who succeed will treat provisioning as product design — dynamic, composable, and grounded in trust.

The enterprises that embrace this shift will operate at a new tempo: decisions accelerated, insights democratized, and innovation fueled by conversation itself.

Because in the age of AI, the interface is no longer the app — it’s the dialogue. And the CDO sits at the center of it.

About the Author:

Matthew Carroll, Co-founder and CEO of Immuta, is driven by a mission to secure the future of data. Renowned for building and protecting scalable data systems, he has a background in service and innovation within the U.S. federal government, and a passion for data policy and risk management.

Before founding Immuta, Carroll served as an intelligence officer in the U.S. Army, completing tours in Iraq and Afghanistan. He later became CTO of CSC’s Defense Intelligence Group, leading data fusion and analytics programs, and advising the U.S. government on data management.

Working with the U.S. Intelligence Community, he tackled complex data challenges as a forward-deployed engineer, realizing the critical need to make data accessible only to the right people, for the right reasons. This insight led to Immuta’s creation, enabling real-time, secure data analysis for organizations worldwide.

He holds a B.S. in Chemistry, Biochemistry, and Biology from Brandeis University.