The agentic marketing stack starts with the data layer

2026-07-10 · Source: Databricks · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Cloud Computing & IT Infrastructure · Depth: Intermediate, medium

Summary

Acxiom, a connected data and technology foundation, modernized its core infrastructure by migrating from on-premises Hadoop to Databricks, achieving 80 to 90 percent faster run times for workloads that previously took 50-90+ hours, now completing in 2-3 hours. This foundational shift enabled the company to build agentic workflows that automate the full marketing value chain, from audience planning and media buying to campaign activation and performance analytics. Chief Cloud and Data Modernization Officer Ankur Jain emphasizes that a solid data foundation and cloud-native architecture are prerequisites for successful AI initiatives, particularly in agentic marketing. The company also integrates privacy as an architectural principle, routing AI-generated content through approval workflows and maintaining human oversight for regulatory and brand risk. Acxiom now embeds its proprietary data assets directly into client environments and martech platforms, moving beyond traditional SFTP transfers to offer transparent, AI-driven capabilities.

Key takeaway

For Directors of AI/ML evaluating agentic AI adoption, prioritize a robust data foundation over immediate AI tool implementation. Your organization will hit scalability and performance ceilings quickly if core workloads remain on legacy infrastructure. Invest in cloud-native data modernization, like migrating to platforms such as Databricks, to free engineering teams for product development and enable the transparent, AI-driven marketing workflows clients demand. This sequential approach ensures speed does not compromise trust or regulatory compliance.

Key insights

Agentic AI success hinges on a modernized, cloud-native data foundation, not just tool adoption.

Principles

• Data modernization precedes agentic AI.
• Privacy must be an architectural principle.
• Embed AI capabilities where clients operate.

Method

Migrate legacy on-premises data infrastructure to a cloud-native platform like Databricks, then build agentic workflows for marketing automation, ensuring human-in-the-loop governance.

In practice

• Migrate from Hadoop to Databricks for performance.
• Automate ETL with AI for faster pipelines.
• Integrate privacy controls into AI workflows.

Topics

• Agentic AI
• Data Modernization
• Cloud-Native Architecture
• Marketing Automation
• Customer Data Platforms
• Data Governance

Best for: Executive, AI Architect, AI Product Manager, Director of AI/ML, VP of Engineering/Data, CTO

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Editorial summary, takeaway, and curation by AIssential. Original article published by Databricks.