When new technologies emerge, we often see the same pattern: breakthrough capabilities don’t deliver breakthrough results right away. Consider the early days of electricity. Manufacturers were euphoric about the transformation electricity would bring. But the expected productivity boom didn’t happen—not at first.
Why?
Because companies simply bolted electric motors onto systems designed for steam power. The workflows, factory layouts, and operating assumptions stayed the same. The new technology wasn’t the problem: the old patterns were.
The exact same dynamic is playing out with AI today.
When groundbreaking technologies are attached to old systems, the results fall short. Breakthroughs require new ways of thinking, new workflows, and new architectural design principles. This is precisely what the e-book Six Moves to Rewire Software for the AI Age by Mark Palmer explores.
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Modern, but not AI-optimized
Over the past decade, organizations have invested heavily in modernization:
SaaS companies built cloud-native platforms with APIs, event streams, ETL pipelines, and warehouses designed for analytics and scale.
Enterprises modernized legacy systems, adopted cloud data lakes, strengthened governance, and implemented hybrid data strategies.
These are modern architectures, just not modern for what AI needs next.
One reason is the sheer scale of data AI consumes per interaction. As the e-book notes:
This is not just faster. It represents a fundamentally different physics of computing:
More reasoning steps
More context
More variety
More volume
More real-time signal
We are asking data systems designed to support 15 pages per query to suddenly support 6,000. Most architectures, modern or not, were never designed for this level of demand.
The rewiring has already begun
Forward-leaning organizations recongize this shift and are quietly adapting their architectures. Instead of treating AI as a bolt-on feature, they are beginning to design workflows where AI acts as an active participant.
With these adaptations, their AI can:
retrieve operational context the moment it’s needed
collaborate on planning and decision-making
validate outputs and detect inconsistencies
trigger autonomous actions across systems
These aren’t experimental prototypes anymore. They’re early signals of how software behaves when the underlying architecture supports AI-speed reasoning.
And this is the key difference: organizations that treat AI as a consumer of data, rather than a reporting layer, are the ones seeing meaningful results. Those that keep AI sitting on top of BI-era assumptions struggle to move beyond demos.
AI doesn’t require a replacement: It requires a rethink
The message is not: “Your architecture is outdated—start from scratch.” The message is: “Modernize the parts of your architecture that AI stresses the most.” AI doesn’t require wholesale replacement. AI requires a rethink.
This is exactly what the e-book outlines.
Some organizations will only need to refine pipelines. Others will need to introduce live-data layers and semantic discovery. All will benefit from aligning architecture to AI’s emerging design patterns.
AI doesn’t invalidate what has been built. It simply changes what the architecture must do next.
Download the e-book: Six Moves to Rewire Software for the AI Age
Get the complete blueprint for building AI-native data architecture.
