(Spoiler: most context models on the market only check some of these boxes)

1. Process-centric: Without a holistic process view, your AI is essentially a loose cannon. Your context model needs to show how work gets done, by whom, in what order, and how it should be done (understanding your organizational goals and constraints) to achieve the best outcomes.

   Example: An AI agent might spot a raw material shortage and auto-order from the cheapest vendor to save money. In isolation, that solves the problem, sure.

   What it misses is that the vendor's lead time has slipped to 3 weeks instead of 3 days because a port is blocked. And that one factory line runs out of stock in 5 days. So you have AI triggering a massive bottleneck in manufacturing and huge fulfillment delays; costing far more in penalties than it saved on parts.

2. Dynamic: A context model should evolve at the same speed your business evolves. Think of it as a continuously updated feedback loop. It ingests real-time signals from humans, backend systems, and AI agents - learning from emerging process patterns, adapting, and feeding this right back into your agents (more on that in part 2). That's how you keep your AI grounded in operational reality.
3. System agnostic: Your context model needs to connect across your entire system landscape - from knowledge bases to CRMs, from data lakes to BI tools - to represent your business reality end-to-end. This is the only way for AI to understand downstream or upstream dependencies.

Example: In customer service, resolving a complex case may require coordination across CRM, billing, and logistics systems. A context model allows AI to understand the full situation and orchestrate actions across these systems rather than treating each in isolation.

1. Cross-time: A context model needs to track the real-time status of your operations. Crucially, it should include memory - the exact order and timeline of events and decisions that explains how and why a process reached its current state.

   Now, I might be biased (I said I work at Celonis). But in the best case, your context model also includes decision intelligence, prediction, and simulation capabilities. That means you don't just know when operations break, but when they're about to break - and exactly how to prevent that.

Example: take Inventory Management. Your ERP system's built-in AI features might tell you that you should reorder a specific material based on a demand spike in a region. But, when powered by a mature context model, AI can go much further:

Because it monitors the present (warehouse inventory), the past (historical shipping data), predicts what is likely to happen next (stockout), and simulates what-if scenarios (rail vs. air freight), your AI can recommend not just any action, but the best action to keep your stock levels balanced at all times.

1. Aware of constraints and external factors: meaning it needs to consider regulations, market conditions, external events (e.g. a central port being blocked) affecting the process.

   Example: in financial operations, for instance, AI querying a context model can adjust invoices or prioritize payments while respecting policies, approval thresholds, and compliance requirements.

2. Open: You can't lock your business logic inside a single platform's walled garden. A mature context model must be able to pull raw data from anywhere via APIs, feeding rich context to any AI agent or application via MCP. In short: ingest from everything, serve context to anything.
3. Built on institutional process knowledge: This is where the wheat separates from the chaff. A mature context model doesn't just make sense of your internal data, it brings pre-built institutional knowledge about how real-world processes actually function.

   Example: take Warehouse Management. A mature context model understands the core objectives of the process, how operational decisions get made, and what intent sits behind human actions (e.g. why a warehouse manager routinely overrides safety stock limits).

This knowledge can't just be derived from your organization alone.

It needs to be built over thousands of implementations across hundreds of companies in dozens of industries, allowing AI to recognize operational patterns and apply proven best-practices from day one.

That's where I see even advanced context products, like Palantir's Ontology, still lacking. Most just aren't designed to embrace the full, sometimes messy reality of business processes, with all their nuances. So consultants and engineers have to spend 6 to 12 months reverse-engineering a tool that is meant to collect data only - not process dimensions like business rules, connections between upstream and downstream work, and operational patterns. Those are the small but powerful details that tell the real story of how a business works. And if your context model can't capture them, then it can never serve its true purpose.