Most enterprise agentic AI pilots follow the same path

The first demo feels like magic. The agent answers questions, interprets intent, and appears surprisingly capable. Teams expand the scope. More tools get added. The planner becomes more “autonomous.”

Then production reality sets in.

It isn’t just that latency increases or costs drift upward. It’s that the system becomes brittle. The same request, run repeatedly, produces different execution paths. Engineers add guardrails. Operations adds human review.

What started as an “autonomous agent” quietly turns into a supervised workflow, just with more expensive compute underneath it.

Nothing is obviously broken. But nothing quite scales.

To understand why, it helps to look at the gap between how these systems are described and how they actually behave.

Asking for a map, getting a guess

There has been a growing recognition in the research community that today’s large, general-purpose language models do not truly plan in the way enterprise systems require. They lack an internal model of cause, effect, and constraint specific to a given environment.

When a generic model is asked to plan a complex business workflow, it does not reason over a known structure. It infers likely next steps based on statistical patterns learned from broad, public data.

In other words, it improvises.

This distinction is easy to miss in early pilots. As long as the problem is shallow, improvisation looks like flexibility. As complexity increases, it becomes variance.

When a planner has to infer your business logic from a prompt, the system is forced to rediscover the process at runtime. That may work in a demo. It does not hold up in production, where consistency, auditability, and predictability matter.

This is where many pilots stall.

The real mistake teams make

Most teams assume that “planning” is a capability they can rent from a foundation model provider.

In practice, planning is not a generic skill. It depends on deep familiarity with constraints, policies, sequencing, and exceptions that are specific to your organization.

The issue is not that today’s models are insufficiently powerful. It’s that they are insufficiently grounded in your world.

Asking a generic model to act as the planner for a complex enterprise workflow is equivalent to asking a new hire to make decisions without onboarding. The output may look plausible, but it won’t be reliable.

Where custom models change the equation

Some organizations address this by changing where planning happens.

Instead of relying on a general-purpose model to infer the process, they introduce a planner that has internalized the structure of the workflow itself. This is typically done by training or fine-tuning a model on the organization’s own historical decisions, policies, and edge cases.

The result is not a rigid rules engine. It’s a model that already knows the order of operations, the non-negotiable checks, and the sequences that matter.

• A generic planner guesses the next step based on probability.
• A custom planner recognizes the next step because it has learned how decisions are actually made in that environment.

For example, consider a commercial loan assessment.

In a generic setup, the planner is given a goal and a set of tools. It pulls a credit report, then realizes it needs to verify entity structure, then discovers a missing document and pauses to request it. The exact path varies depending on what the model tries first.

The outcome may be acceptable. The behavior is not.

Now compare that to a system where the planner has been trained on the institution’s historical loan decisions.

That model does not need to be told that entity verification comes first. It already knows. It executes the process with the same ordering, the same checks, and the same language used by the credit committee reviewing the result.

The decision is faster, more consistent, and explainable in terms the organization already understands.

The question that matters

When an agentic AI initiative struggles to scale, the issue is rarely that the model isn’t capable enough. A more useful question is this:

Are we asking a generic model to guess our business logic, or have we built an AI asset that actually knows it?

Generic models are powerful tools. They are well-suited for language, synthesis, and broad reasoning.

But when it comes to planning within a specific enterprise context, the systems that scale stop asking models to improvise and start giving them something concrete to execute.

That shift, more than any architectural diagram or vendor choice, determines whether an agentic pilot remains an experiment or becomes a durable system.

What can I do about it?

Most organizations are stuck in "pilot purgatory"—running shallow experiments that never reach production. To move forward, you must answer three questions:

• Are you asking a generic model to guess your business logic, or have you built an asset that actually knows it? 
• Where are your most expensive experts currently trapped in low-leverage, manual workflows? 
• If you continue on your current path, will you have a durable, revenue-generating AI asset in 90 days, or just another roadmap? 

Let’s Whiteboard the Solution 

Give us 30 minutes to look at your bottleneck. We will help you evaluate your current maturity stage and map out exactly how to move from "experiment" to "production" in weeks, not quarters.

‍