Your marketing technology stack was likely built to answer questions about the past: What happened last quarter? Which campaigns performed best? Where did customers convert? Now, real-time AI expects answers in milliseconds — leaving MOPs and marketing leaders caught between high performance expectations and lagging legacy infrastructure.
At the MarTech Conference, free and online Sept. 2, Kevin Haag, SVP of data strategy, Qualified Digital; Koertni Adams, head of content and product marketing, MessageGears; Jacqueline Freedman, CEO and founder, Monarch Advisory Partners; and Mike Maynard, chairman, Napier Partnership Limited, explore how to bridge this gap in “Built for yesterday: Why your data architecture can’t keep up with AI.”
What does this customer need right now? Which message comes next? Should an AI agent act on this signal? These real-time demands expose a common hurdle: systems engineered for batch updates and scheduled reporting simply cannot support instant, automated execution.
When yesterday’s architecture meets today’s AI
A prospect engages with a campaign, or an account shows sudden intent. The value of that signal decays rapidly when data has to crawl through multiple legacy systems before your stack can react. The issue isn’t your strategy — it’s the latency built into traditional reporting workflows.
This session dives into these structural bottlenecks and offers a clear path forward for teams ready to unlock instant execution.
From monolithic stacks to composable infrastructure
Upgrading your architecture doesn’t require scrapping your current setup.
Instead, forward-thinking teams are turning to composable data architectures. By allowing specialized tools to connect flexibly, a composable approach ensures the right data reaches the right application exactly when needed.
AI-powered personalization, next-best-action triggers, and automated journeys require more than sophisticated algorithms—they rely on flexible pipelines. Modernizing your approach makes this agility achievable without disrupting current workflows.
Modernizing without starting over
A full infrastructure rebuild is neither practical nor necessary. This is completely doable using an incremental approach.
The immediate win lies in identifying where data slows down in your existing stack. Whether that means targeting system latency, unlocking trapped customer records, or streamlining flow between core platforms, small architectural shifts yield huge operational returns.
The goal isn’t just speed; it’s making critical data accessible the precise moment your AI tools need it. Attendees will gain actionable strategies to boost data velocity and modernize their foundations—enabling real-time decisioning without the cost of a complete teardown.
AI is moving fast, but your team doesn’t have to rebuild from scratch to keep up.
View the agenda for the free, online MarTech Conference on Sept. 2, 2026, and register today.
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