Busting process ghosts: how to keep enterprise AI from automating processes that don't exist

(©Andrii Kudrin - canva.com) Companies are investing heavily in the race to become agentic organizations. But as they strive to implement end-to-end, AI-driven automation, most are running into the same obstacle - the chasm between theoretical business processes and the unique processes that make up their own operational reality. Every company knows how its processes work on paper, based on the information that was available the last time they were modeled. And every Large Language Model (LLM) trained on the public internet knows how business processes work in general. But what AI agents need - if they're to drive automation successfully - is to understand how end-to-end processes work right now, and how they should work in the future, for a specific business. For example, one of the world's largest pharmaceutical companies is speeding up clinical operations using AI agents grounded in the granular knowledge of its unique and highly regulated clinical trials process. For many companies, gaining this clarity of operational reality is still a work in progress. In fact, 79% of IT leaders say their current observability and analytics tools don't provide the real-time process visibility they need. Now, this longstanding barrier to effective business process optimization is becoming a silent killer of RoAI (return on AI investment). The thing is, when agents' actions aren't grounded in operational reality, they end up automating ghosts - processes that don't actually exist. The very real danger of automating ghost processes Historically, people completed process tasks - logging a sale, approving a request, shipping an order. And they recorded their actions in systems of record - ERPs, CRMs, and Supply Chain Management (SCM) systems. Now, AI is taking on a growing share of those tasks - reading information, making decisions, using applications, and committing changes. People can do a lot between each process step, such as calling a colleague to validate that a customer meant to buy five times their normal order. AI agents can also take action between process steps - accessing data, calling Application Programming Interfaces (APIs), even stringing actions together across systems. Ask independent agents to accomplish the same task, and if they don't have the right context, like people, they can take different paths and generate conflicting outcomes, each of which seems valid on its face. What happens when a procurement agent approves a supplier that a compliance agent just flagged for review? These "in between" or "shadow" transactions can become part of an overall agentic process - operating like "ghosts" in the background, using up tokens, making decisions, and taking actions without being seen. To better understand the problem, imagine an end-to-end business process as a series of highways and junctions. When an AI agent has only generalized and historical process data to navigate by, it's like a driver trying to complete a road trip based on a loose understanding of how traffic systems work, and an inaccurate, outdated map. Just like this metaphorical driver, the agent doesn't have a high chance of reaching the right destination. Worse, while the driver would swiftly pull over and ask someone for help, the AI agent wouldn't realize that its understanding is patchy and its map is all wrong. It would keep confidently pushing forward - because for AI agents, ghost business processes are indistinguishable from living, breathing ones. If (as is very likely the case) the company deploying the agent doesn't have a clear view of its operations either, the agent may even look as if it's succeeding in its tasks. But since the process the agent is automating isn't real, the resulting business value will be zero. What does it take to bust these ghost processes? Busting process ghosts with a Context Model To meet these fundamental needs, enterprises need a Context Model. LLMs are great for generating probabilistic answers based on human language. Context Models are needed for AI to make the deterministic decisions that drive business processes. Context Models use process data from a company's systems, applications, and devices, encompassing both the business's current operational state and everything that led to the present moment. They also include the human story of the company's operations, told in user clicks, actions, and interactions. This data is enriched with the organization's unique business knowledge (from business rules to Key Performance Indicators (KPIs)) and layers decision intelligence on top - providing the ability to determine why things happen, as well as what's likely to happen, and what should happen. The result is a dynamic, real-time digital twin of business operations in a machine-readable format. In other words, that long-sought, clear, and current view of operational reality that both people (through applications) and AI (through a protocol like Model Context Protocol (MCP)) can understand. For example, ask an LLM if you should order more materials to address a recent demand spike, and it will provide a generic answer, like - "Demand spikes can result in stock-outs, you may want to order more materials." That answer isn't specific to your industry, your company, your supply chain. However, AI grounded in operational intelligence from your company can give you a "specialist" answer, like - "Given your low 10% stock-out risk, avoid placing new orders and instead reallocate surplus inventory from your warehouse." Immediately, companies can see where their existing AI agents are automating ghost processes and intervene to drive real business outcomes. What's more, they can make sure their agents never get lost in the ghost world again. Goodbye, ghost processes. Hello, RoAI The best Context Models don't just provide business leaders with operational clarity - they provide AI agents, on whatever platform they're built, with the right operational context. As outlined above, this operational context is the key to successful AI-driven automation. To return to our analogy, it's like giving your AI agents rock-solid GPS, an accurate map, and even real-time traffic notifications to help them navigate your business processes. With operational context, they have everything they need to reach the right, ROI-delivering destination. Firmly grounding AI agents in your business reality Once they've equipped their AI agents with the right operational context, companies should also set up some guardrails - ensuring agents' actions stay both safe and compliant, and in service of broader business objectives. For example, there will be some actions an AI agent should always submit for human approval. And there will be some decisions that should always be guided by deterministic business logic rather than left to a probabilistic LLM. Choosing when and where extra care is needed requires knowing where potential AI errors carry unacceptable financial, regulatory, or operational risks. And this knowledge requires operational clarity. Putting the right guardrails in place helps to ensure that as well as following actual, non-ghostly processes, agents follow them in the safest, most cost-effective way. And as AI agents are part of the process themselves, with the right solution, businesses can and should mine their agents' activities, both to understand and demonstrate their impact on business outcomes, and to drive continuous operational improvement. Why more people should talk about AI relevance Everyone talks about AI accuracy. But when it comes to driving ROI, AI's business relevance is just as fundamental. An AI agent can automate a process with unerring accuracy, but if the process is a phantom, sprung from outdated or generalized models, the business won't see the benefit. AI relevance only comes with operational clarity and context - with the ability to spot and prioritize your most impactful AI use cases, to show your agents how the business runs, to safeguard