​Context Is The Oil Of The AI Economy—And The Basis For New, Intelligent Systems Of Action

Mahesh Rajasekharan is the President and CEO of Cleo.

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For years, business leaders have understood data is the “new oil.” In the age of AI, that premise deserves an update.

Data is abundant. AI models are becoming commoditized. Compute power continues to scale. What remains scarce—and increasingly valuable—is context. Context is the oil of the AI economy because it is the ingredient that enables intelligent systems to interpret signals, prioritize decisions and drive meaningful outcomes.

Given the rise of AI, organizations are investing in tech to improve forecasting and automate decision making. Yet, most supply chains remain highly reactive operationally, responding to disruption only after it appears versus sensing issues and changing course before customer outcomes are impacted.

Disruption can take many forms: a supplier delay becomes a missed delivery, a bottleneck becomes a chargeback, or a missed customer commitment becomes lost revenue.

The issue isn’t a lack of data. The real problem is in determining which signals matter, what outcomes are at risk and what actions should come next. AI without context is just computation. Value is created when supply chain signals are connected into a trusted contextual understanding of what is happening and what action should be taken next.

It’s high time today’s business leaders tap into these massive data-rich reserves. But how?

EDI: The Hidden Goldmine

For years, enterprises have exchanged operational information through electronic data interchange (EDI) and business-to-business (B2B) integration networks. Purchase orders, ASNs, invoices, shipment updates and forecasts flow continuously across suppliers, retailers, manufacturers and logistics providers.

Many organizations view EDI as “integration plumbing,” but never overly valuable or strategic. Now we know better, because embedded within those transactions is something powerful: a real-time representation or “digital twin” of how the supply chain is actually operating.

Today’s solutions not only capture relationships between customers, orders, suppliers, shipments, inventory and commitments, but they can leverage correlation to make sense of everything—reflect dependencies, exceptions and execution outcomes as they happen.

Supply chains today are loaded with years’ worth of structured operational interaction data. We just never considered it valuable before now, and we certainly never considered it as the foundation for intelligent orchestration.

But, thanks to what I’ll call the “rise of the context layer,” all that’s beginning to shift. Let me explain. ​

The Rise Of The Context Layer​

The next evolution of supply chain orchestration is the emergence of the context layer—a living, breathing, continuously updated real-time view into the operational state of your supply chain. This is where intelligent execution begins.

The gap between supply chain planning and execution has frustrated supply chain professionals for years. But now, thanks to modern integration platforms and AI-driven solutions, there’s a trustworthy context layer that connects and normalizes signals across enterprise ecosystems, providing dynamic views into what’s happening across their supply chain at any given moment.

More than just a massive digital twin, the context layer is aware, continuously evolving and always providing operational context focused on decisions and outcomes.

As such, the context layer helps answer critical questions like these, in real time:

• Which signals require attention?
• Which disruptions will impact customers?
• Which orders or shipments matter most?
• Where is revenue or service at risk?
• What actions should occur next?

For most companies, the challenge is prioritizing action given there are thousands of events happening across a supply chain network every second, minute and hour of every day. Admittedly, most events are noise, yet some require immediate intervention. So, how can any supply chain-dependent organization discern between the two? It’s hard because most supply chain architectures were built around systems of record and systems of planning, rather than outcomes.

ERP systems store transactions. Planning systems optimize future scenarios. Visibility platforms surface events. Workflow tools automate predefined processes. That’s all great, but not enough for today’s needs.

Companies today require systems that can interpret operational meaning and coordinate execution in real time. Disruptions don’t wait, so you can’t have planning cycles that run weekly or monthly in a world where execution happens minute by minute. A supplier issue, port delay or demand spike can cascade into customer impact long before planning systems adjust. Organizations need to sense change, evaluate impact and continuously make localized decisions that people can leverage.

Systems Of Action

But what makes those decisions possible? The context layer gives AI awareness, but it’s the intelligence layer that turns that awareness into coordinated decisions. This evolution is leading to the rise of “systems of action.”

Far more capable than traditional systems of record, a system of action continuously senses operational signals, understands context, prioritizes decisions, coordinates responses and executes corrective actions.

Again, the context layer is what makes this possible. Without it, AI lacks prioritization. Automation is skittish, at best. Workflows operate in silos, where teams are incapable of real-time decision making because they’re overwhelmingly confronted by noise.

But with the context layer, organizations can coordinate execution across systems, partners and functions in real time. Here, orchestration becomes real—and alive.

This shift is about elevating human decision making, not replacing it. AI agents will increasingly handle localized execution such as rerouting shipments, resolving exceptions, reprioritizing orders or triggering workflows. Human teams can then focus on higher-order decisions like catering to customer relationships and managing financial risk. The result is a more proactive, responsive and resilient supply chain.

The Next Competitive Advantage

As AI becomes embedded into enterprise operations, context will determine which organizations prove most successful.

Supply chains are uniquely positioned for this shift. They already possess decades of structured interaction data flowing through their networks. What was once seen as back-office integration may prove to be one of the most valuable data assets in the enterprise.

The opportunity now is to transform that fragmented data into a unified context layer that enables intelligent orchestration at scale and shapes systems of action that you can trust.

When that happens, supply chains move beyond automation. They become adaptive systems capable of sensing, learning and course-correcting before disruptions become customer problems.

And in the AI economy, that may ultimately be the defining competitive advantage.

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