Previously on “Rethinking data architecture for AI”
In our earlier posts, we explored why most data architectures, even modern, cloud-native ones are still optimized for BI-era workloads, and how the first two pivots (lightweight workflows and pop-up integration) help systems meet AI’s need for high-speed, high-volume, multi-step reasoning.
This next stage of the architecture shift focuses on how AI needs to interact with your customers’ data in real time and the fact that it must access data across many systems at once.
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Pivot 3: Expand data lake with live data access
Many organizations have invested in data lakes and warehouses to power BI dashboards and reporting. These systems are excellent for their intended purpose. But as with the previous computing advancements, AI often requires something different.
AI-driven applications need:
Live operational detail
Raw, uncompressed signals
Data that reflects what’s happening right now
The ability to explore unexpected relationships
When data is delayed, pre-modeled, or aggregated for analytics, AI ends up reasoning over stale context, limiting both accuracy and usefulness.
This isn't about replacing the data lake and/or warehouse. It’s about augmenting them with live operational access for AI use cases.
What live data access looks like
Forward-leaning organizations are enabling AI to work with up-to-date information by introducing:
Direct access to operational systems where customer events, transactions, or changes originate
Metadata control, so AI understands what exists and how to interpret it
Fine-grained access controls, ensuring AI retrieves only what it is allowed to see
This shift extends the architecture: analytics continues to run on curated data while AI runs on live context.
Why live data access matters
Real-time access allows copilots and agents to:
Adapt to in-the-moment customer behavior
Reason across entire workflows
Detect anomalies instantly
Deliver recommendations based on “right-now” data
This is essential for scenarios like fraud detection, customer support, personalization, logistics, and operational decision making where the “yesterday’s truth” problem becomes immediately visible.
Pivot 4: Seamlessly connect to multiple sources at scale
Once users experience the value of AI-driven features, a new challenge emerges: scalability. Demand doesn’t slow down—it expands.
A single AI feature powered by one data source quickly becomes the beginning, not the end. Users immediately start asking:
“Can it also pull in our CRM data?”
“Can it combine that with marketing activity?”
“What about product usage?”
AI naturally exposes connections across workflows that humans never set out to link, and users rapidly expect the AI to follow those threads.
What starts as one integration quickly turns into five.
Then fifteen.
Then fifty.
This isn’t scope creep.
It’s AI revealing where the value actually is.
And it exposes another requirement of an AI-ready architecture; the ability to support cross-domain reasoning, multi-step workflows, and insights built from multiple live systems at a scale that grows with demand.
What multi-point connectivity looks like
Software providers successfully operationalizing AI are shifting from one-off integrations to an architecture that allows AI to seamlessly tap into many systems at once. This includes:
A rich library of prebuilt connectors
Unified authentication and permissions
Consistent data access patterns
Standardized schemas that AI can interpret
Support for both operational and analytical sources
Governance and observability across all connections
With these capabilities, AI can move fluidly across domains from CRM to billing to product usage to support, without custom engineering every time a new question arises.
Why multi-point connectivity matters
Multi-point connectivity enables AI to:
Answer cross-functional questions
Detect patterns across formerly siloed systems
Automate multi-step workflows
Reason over customer, product, and operational signals together
Provide insights that no single source can offer
It also prevents software providers from drowning in integration requests, because the architecture anticipates, rather than reacts to, growing demand and scales with it.
The bottom line
Together, the first four pivots—lightweight workflows, pop-up integration, live data access, and multi-point connectivity— form the essential foundation of an AI-ready architecture.
Without these capabilities, AI features:
Miss what’s happening now because they depend on workflows built for batch processing instead of lightweight, real-time access (Pivot 1)
Require constant maintenance because they rely on long-running, persistent pipelines instead of flexible, on-demand data retrieval (Pivot 2)
Operate with incomplete context because they can only access curated BI data, not raw, live operational signals (Pivot 3)
Can’t grow with user demand because they’re limited to single-point integrations instead of scalable, multi-system connectivity (Pivot 4)
With these pivots in place, AI can evolve from a promising add-on into a scalable, high-trust product capability that grows in value as users rely on it more deeply.
Up next: Codegen & AI-first consumption
In the next blog, we’ll explore the final two pivots: AI-ready code generation and AI-first consumption, the shifts that redefine how software is built and how users interact with it.
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