Data Governance Is Cool Again: Agentic AI at Workday Cuts QBR Prep from 2 Weeks to 2 Hours

From Data Silos to AI-Enabled Customer Engagement: Why Data Governance Is Cool Again

Executives don't need another AI hype deck. They need a clear plan to turn scattered data into decisions, and decisions into revenue. The fastest path right now: treat data governance as the core product that makes agentic AI useful, safe, and accountable.

Recent leadership conversations and summits point to the same shift. AI agents are moving from demos to day-to-day colleagues. The companies seeing traction are the ones that make governance practical, not performative.

What leaders are learning about agentic AI

• AI agents can coach sales teams in real time. Think call summaries, objection handling, and next-best actions delivered inside the workflow.
• Quarterly business review prep is dropping from two weeks to two hours with automation that pulls metrics, explains variance, and drafts narratives.
• Executives improve decisions when they see more raw data, not less. Data literacy comes from exposure, not another dashboard.
• Governance isn't a brake. Done right, it's the system that keeps humans in the loop while letting agents act with confidence.

The governance model that accelerates AI

Replace slow gates with clear guardrails. Define what an agent can do, on which data, with what controls, and how outcomes are reviewed. Treat agents like coworkers: they need access, responsibilities, feedback, and performance metrics.

• Access: Role-based permissions and data contracts at the source.
• Quality: SLAs for freshness, completeness, and lineage tied to business outcomes.
• Safety: Human review for high-impact actions, with audit logs for every agent decision.
• Recovery: Fallback rules when confidence is low or data is missing.

From silos to customer engagement

Customer teams win when agents sit on top of a governed data foundation: CRM, product usage, support tickets, and billing. With that in place, agents can prioritize accounts, suggest offers, tailor outreach, and flag churn risks-without guessing.

The kicker: customers feel the difference. Faster answers, fewer handoffs, more context. Internally, leaders get traceable decisions they can trust.

Operating model for executives

• Make the CDO and COO co-owners of AI agents used in revenue and operations. Strategy without ownership stalls.
• Fund data products, not projects. Each product has a contract, SLA, steward, and P&L impact.
• Create an Agent Review: a weekly 30-minute meeting to evaluate agent outputs, errors, and ROI. Iterate like you would with a new hire.
• Instrument everything. Log prompts, data sources, confidence, interventions, and outcomes.

ROI and accountability metrics

• Time-to-prep: QBR prep time reduced from weeks to hours.
• Conversion: Meeting-to-opportunity rate and win rate lift in agent-supported deals.
• Cycle time: Lead response and case resolution time.
• Accuracy: Forecast deltas and variance explanations matched to reality.
• Adoption: % of workflows with agent assistance and human override rates.

30-60-90 day plan

• Days 1-30: Pick one use case (QBR prep or sales coaching). Lock scope. Map data sources, access, and minimum quality thresholds. Establish human review steps.
• Days 31-60: Ship a guided agent to a pilot team. Log outcomes, overrides, and errors. Tune prompts, data joins, and UI friction.
• Days 61-90: Add two adjacent workflows. Stand up the Agent Review. Publish an ROI brief and a data contract catalog for leadership.

Risk, compliance, and trust

Adopt a standard so you don't invent controls from scratch. The NIST AI Risk Management Framework provides a practical baseline for mapping risk to controls and monitoring over time.

• NIST AI Risk Management Framework

Common blockers-and how to remove them

• Dirty data: Set SLAs and throttle agent actions when quality drops. Visibility beats wishful thinking.
• Model drift: Schedule evaluations tied to business metrics, not just perplexity or benchmarks.
• Security reviews that stall: Pre-approve patterns (retrieval, summarization, outbound actions) with clear data boundaries.
• Shadow AI: Offer a sanctioned path that's faster than going rogue. Provide templates, guardrails, and support.

Leadership takeaway

Agentic AI will not fix bad data or unclear ownership. But with governance that's simple, enforced, and tied to outcomes, it becomes a reliable teammate. Start with one revenue-facing workflow, measure hard results, and expand with discipline.

Level up your team's skills

If you're building executive fluency and practical capabilities for your org, these resources can help:

• AI courses by job function for leaders, operators, and data teams.
• Popular AI certifications to standardize skills and expectations.

Final word

Make governance the enabler, not the excuse. Give agents clean data, clear limits, and a scoreboard-and they'll give your teams time back and better decisions.