From Data Silos to AI-Enabled Customer Engagement: What Executives Need Now
Data and AI strategy is past theory. The leaders pulling ahead are moving from pilots to production, turning clean data and precise governance into measurable customer outcomes. If you oversee growth, risk, or operations, this is your moment to set the plan and get the org rowing in the same direction.
Columbus Leadership Summit - November 5, 2025
Senior executives will gather at the Hyatt Regency Columbus to align on enterprise AI strategy, precision AI agents, and the operating models that actually scale. Expect practical frameworks, peer-tested case studies, and clear next steps for 2026 planning. Secure internal approvals early-this is a working summit.
Precision AI Agents: Trust, Control, and Measurable Outcomes
Precision AI agents are narrow-scope, well-instrumented, and auditable. They focus on a defined process-claims triage, onboarding, collections-and ship with explicit data boundaries, policy checks, and human-in-the-loop controls. That's how you reduce risk while improving cycle time and customer experience.
Align your program with proven standards and controls. If you don't have a reference point yet, the NIST AI Risk Management Framework is a strong place to start for governance structure and risk treatment.
New Research: Data for AI and CX (Insights from 300+ Leaders)
A recent study of 300+ business and IT leaders surfaces what's working-especially for customer engagement. The headline: data quality, access, and clear ownership drive AI impact more than model selection.
- Treat customer data as product: documented contracts, SLAs, and version control.
- Standardize identity resolution and consent handling before scaling personalization.
- Instrument feedback loops so agents and models improve with each interaction.
- Link AI use cases to one KPI per journey stage (e.g., conversion, AHT, NPS)-no vanity metrics.
CDO Strategies to Unlock Value in the Agentic AI Era
CDOs and enterprise leaders need a blueprint to move from scattered experiments to enterprise-scale outcomes. Focus on a few levers you can actually manage and measure.
- Data foundation: productized data sets, lineage, and real-time access where it matters.
- Trust and controls: policy-as-code, model cards, bias monitoring, and exception workflows.
- Execution model: small cross-functional pods tied to business P&L, with weekly increments.
- Change and skills: build AI literacy for product, risk, and front-line teams-not just data science.
Podcast Spotlight: Empowering Next-gen Women Data Leaders
Michelle Boston (Bank of America) and Dr. Adita Karkera (Deloitte) join Amy Horowitz (Informatica) to dig into responsible AI, enterprise-wide data literacy, and the growing impact of women in data leadership. Their throughline: trust, collaboration, and human oversight aren't "nice to haves"-they're how large organizations move fast without losing control.
On the Calendar: Cincinnati Global Leadership Summit - Data (October 7, 2025)
An in-person working session focused on strategic data initiatives, executive alignment, and real-world delivery models. Useful for leaders who want peer-tested playbooks-not theory.
Join the Community
If you're driving data and AI at scale, you need a trusted circle. This community gives you access to leaders, benchmarks, and real stories from the trenches.
- Elevate your personal brand and POV.
- Influence the data leadership agenda.
- Build a durable network across industries.
- Exchange practical playbooks and lessons learned.
- Stay current on events, research, and AI regulation.
Quick Actions for Executives
- Block time for the Columbus Leadership Summit and bring one business partner and one risk partner.
- Assign your data product lead to summarize the "Data for AI & CX" research with three actions you'll fund this quarter.
- Greenlight one precision AI agent in a controlled process with clear guardrails and a 60-day success metric.
- Update AI governance to align with the NIST AI RMF and automate policy checks in your MLOps stack.
- Upskill leaders and ICs with curated training built for roles, not just tools: AI courses by job and popular AI certifications.
Bottom Line
AI impact is a management problem before it's a technical one. Get the data right, pick one customer moment to improve, deploy a precision agent with guardrails, and measure the outcome. Repeat until it shows up in the P&L.
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