Data Strategy Is Now Business Strategy for Agentic AI

Enabling Agentic AI: Data Strategy Is Business Strategy

Agentic AI isn't a chatbot. It's a set of systems that can plan, decide, and take action across your stack. Think: an AI that reads a ticket, queries systems, executes steps, and closes the loop-without a human in the middle.

The point is simple: no data strategy, no agentic AI. Your outcomes will match the quality of your identities, metadata, and governance. That's why data strategy has become business strategy.

What "Agentic" Means for Executives

• Multi-step execution: Orchestrates workflows across apps and APIs.
• Tool-use: Calls internal services with the right context and permissions.
• Feedback loops: Learns from outcomes to improve the next decision.
• Guardrails: Operates under policy, audit, and human oversight.

Use cases: case triage in support, pricing adjustments in commerce, supplier onboarding in procurement, KYC in financial services.

Why Data Readiness Decides AI Readiness

• Identity resolution: One view of customer, product, supplier, and asset-or your agent acts on the wrong entity.
• Context depth: Rich attributes, history, and relationships enable better decisions than "just the prompt."
• Lineage and quality: If you can't trace it, you can't trust it-or certify it for automation.
• Policy-as-code: Permissions, PII rules, retention, and masking enforced at runtime.
• Real-time access: Agents need fresh data; nightly batches won't cut it.

Common Architecture Pitfalls

• Point-to-point spaghetti: Agents stall when one integration breaks. Use APIs, events, and contracts.
• Warehouse-only thinking: Analytics is historical; agents need operational MDM and event streams.
• Unlabeled chaos: No metadata, no reuse. Treat data as products with owners and SLAs.
• "RAG solves it": Retrieval helps, but garbage in still equals garbage out. Start with data quality.

Risk, Controls, and Compliance

• Data leakage: Classify data, restrict scopes, and mask sensitive fields at the source.
• Prompt injection and tool abuse: Safelist tools, apply content filters, validate outputs before execution.
• Audit and explainability: Log inputs, actions, decisions, and who approved them.
• Model risk: Track versions, evaluate drift, and define clear fallback paths.

Useful frameworks: NIST AI Risk Management Framework and OWASP Top 10 for LLM Applications.

Near-Term Investment Priorities

• Master data and identities: Customer/product/supplier 360 as the system of truth.
• Data contracts and observability: Define schemas and monitor freshness, accuracy, and drift.
• Policy engine: Centralize access controls, masking, and consent.
• Event-driven plumbing: Publish changes; let agents subscribe to what matters.
• Evaluation harness: Offline tests + online metrics for every agent before scale.

A Pragmatic 90-Day Roadmap

Weeks 0-2: Align and Assess

• Pick two high-value, low-regret processes (e.g., support triage, vendor data onboarding).
• Map systems, data sources, owners, and failure modes.
• Define success metrics: time-to-resolution, cost per task, error rate, customer effort score.

Weeks 3-6: Fix the Data and Guardrails

• Stand up golden records for the entities your pilot touches.
• Add data contracts, lineage, and quality checks to the path of execution.
• Implement policy-as-code: scopes, masking, role-based permissions.

Weeks 7-10: Build and Contain

• Prototype agents with tool-use restricted to a safelist.
• Add validation steps and human-in-the-loop for critical actions.
• Instrument logs and feedback loops from day one.

Weeks 11-12: Evaluate and Decide

• Run A/B or shadow mode. Compare cost, speed, quality, and risk.
• Document gaps: data, controls, and process changes needed for scale.
• Greenlight, iterate, or kill-based on evidence, not hype.

Operating Model That Works

• Data product owners: Accountable for entity quality and contracts.
• AI product manager: Owns use case design, KPIs, and guardrails.
• DataOps + MLOps: One pipeline for data quality, features, models, and evaluations.
• Security and compliance: Embedded from the start, not a late-stage gate.

KPIs to Track From Day One

• Cost per resolved task and cycle time reduction
• First-contact resolution and rework rate
• Policy violations prevented and audit coverage
• Model/agent quality: precision, recall, and override rate
• Data quality SLOs: freshness, completeness, and match rate

Leadership Questions to Ask This Week

• Which two processes could an agent safely execute end-to-end today?
• Do we have certified golden records for the entities those processes touch?
• What policies are enforced at runtime versus "on paper"?
• Where are the logs that prove what the agent did, why, and with whose approval?
• What's our kill switch if something goes wrong?

This perspective reflects insights discussed with Abhi Visuvasam, Field CTO of Enterprise Architecture and Solutions at Reltio: strong data foundations, clear guardrails, and measurable business value-delivered in weeks, not quarters.

If your leadership team needs a structured path to upskill on AI strategy, governance, and agent design, explore focused programs here: AI courses by job role.