This question tends to surface when teams are caught between ambition and realism. Leadership wants earlier warnings, fewer surprises, and better coordination across the supply chain. IT hears “control tower” and immediately thinks of a multi-year data lake program. Somewhere in between, operations quietly wonder whether there’s a way to get most of the value without signing up for another large transformation that will take longer than the problem can tolerate.
That tension is understandable. Data lakes promise completeness. Control towers promise clarity. The mistake is assuming one automatically requires the other.
What an AI control tower is really trying to do
Strip away the terminology and an AI control tower has a modest goal. It tries to answer a few questions better and faster than existing tools do. Which orders are at risk right now? Which delays are likely to cascade? Where can we still intervene without paying a premium?
Notice what’s missing from that list. Nobody is asking for every historical record in one place before they act. They’re asking for timely signals, some prioritization, and enough context to decide.
In that sense, an AI control tower is less about storage and more about timing. It brings together operational signals close to when they occur, interprets them, and nudges people while there is still room to respond.
Why the data lake becomes the default answer
Data lakes often enter the conversation because they feel safe. If everything is centralized, nothing is missed. Future use cases are covered. Analysts can explore freely. All of that is true, to a point.
The problem is sequencing. Building a large lake usually takes longer than expected. Data quality debates drag on. Ownership questions surface. Meanwhile, the control issues that triggered the project in the first place remain unresolved. Teams keep expediting, reallocating, and firefighting, just as before.
A lake may be the right long-term asset. It is rarely the fastest route to operational improvement.
A simpler framing that changes the design
Instead of asking “where should all our data live,” a more useful question may be “which signals do we need, and how quickly?”
Most control decisions rely on a surprisingly small set of events: shipment delays, order changes, inventory movements, production disruptions, supplier confirmations. These events already exist inside ERPs, WMS, TMS, and MES systems. The issue is not their absence. It’s that they arrive late, in isolation, or without context.
If you can capture those events as they happen, align them in time, and enrich them lightly, you can support many control tower use cases without centralizing everything.
What this looks like in practice
Rather than building a massive, slow-moving data lake, modern supply chain visibility relies on a modular "working set" of data. This architecture focuses on speed and relevance over sheer volume.
The Lightweight Visibility Architecture
Event-Driven Ingestion: Captures "signals" (e.g., a delay or a quality hold) as lightweight, fast messages instead of copying entire databases.
Data Virtualization: Queries information where it currently lives, providing a real-time view of current states without the need for massive data migration.
Curated Feature Store: Maintains a small, focused set of high-value metrics—like lead times and supplier reliability—that are updated incrementally.
A short definition, for clarity
An AI control tower provides near-real-time visibility and decision support by combining current operational signals, prioritizing risk, and suggesting actions. It does not require a massive data lake if the focus is on decision-critical events rather than exhaustive history.
That distinction is subtle, but it shapes everything that follows.
Choosing the first use case carefully
One reason control tower initiatives stall is that they start too broad. “End-to-end visibility” sounds appealing but gives nobody a clear win to point to.
Teams that move faster usually pick a narrow, painful problem. Reducing emergency freight for a specific product group. Improving on-time performance for a key customer segment. Coordinating inter-plant transfers for shared components.
The scope stays tight. Signals are chosen deliberately. Success is measured in avoided costs or faster response, not in dashboard completeness.
A concrete example
A mid-sized electronics manufacturer struggled with last-minute air shipments when inbound components slipped. The ERP showed delays, but often a day late. The original plan was to build a centralized lake to support a broader analytics program.
Instead, the team started smaller. Supplier confirmations, shipment status updates, and plant consumption rates were captured as events. When a delay crossed a threshold, the control layer flagged which finished goods were exposed and whether alternate stock existed at another site.
Within two months, planners were intervening earlier. Not every alert led to action, but enough did to reduce air freight materially for the pilot SKUs. The lake project didn’t disappear, but it stopped being a prerequisite.
Where scepticism is healthy
While this event-driven approach is powerful for immediate operations, it comes with specific trade-offs that require a strategic rollout:
Long-Horizon Gaps: Without a massive historical data store, it is more difficult to perform multi-year pattern discovery or long-term trend analysis.
Data Quality Sensitivity: These systems rely heavily on precise timestamps and identifiers; if your underlying source data is messy, the system will highlight those errors immediately.
Strategic Sequencing: These limits don't break the model; they simply dictate that you should prioritize immediate decision-making value first and layer in complex historical context later.
Governance and trust still matter
Avoiding a data lake does not mean avoiding governance. Decision rights must be explicit. Who can approve an expedited shipment? Who can trigger an inter-plant transfer? What happens when recommendations are overridden?
Logging outcomes is essential. Over time, patterns emerge. Some signals consistently predict trouble. Others generate noise. The control tower improves not because the models are clever, but because the organization learns what to pay attention to.
Measuring early success
Early metrics should be simple and operational. Fewer emergency shipments. Faster response times. Fewer escalations. Hours saved by planners.
These are not abstract KPIs. They resonate because people feel them in their daily work.
About Heizen in supply chain
In supply chain operations, Heizen helps teams move from risk visibility to decisive action. Its customized software plugs directly into procurement, logistics, and planning workflows to automate follow-ups, escalate issues, and support faster decision-making when disruptions emerge. Instead of adding another dashboard, Heizen reduces response time by embedding intelligence where supply chain teams already work.
The bottom line
You can build an AI control tower without first building a massive data lake. The path is narrower, more pragmatic, and often faster. Focus on decision-critical signals. Capture them as events. Add just enough context to act. Let learning guide what comes next.
A data lake may still earn its place over time. It just doesn’t need to be the gatekeeper for better control. In many organizations, clarity arrives not from having more data, but from seeing the right data soon enough to matter.
Sources & other readings
Gartner. (2023). Control towers and event-driven architectures in modern supply chains*. Gartner Research.*
McKinsey & Company. (2022). From data lakes to decision velocity: Rethinking supply chain analytics*. McKinsey Global Institute.*
MIT Center for Transportation & Logistics. (2021). Event-based visibility and real-time decision support in supply chains*. Massachusetts Institute of Technology.*
Harvard Business Review. (2020). Why more data doesn’t lead to better decisions*. Harvard Business Publishing.*
World Economic Forum. (2021). Supply chain control towers: From visibility to orchestration*. World Economic Forum.*
Deloitte. (2022). Designing modular, event-driven supply chain architectures*. Deloitte Insights.*
