Imagine it is 3rd and 8 with 45 seconds left before halftime. The quarterback approaches the line of scrimmage and sees several defenders crowding the line. From film study and experience, he knows that this alignment frequently precedes a blitz. Before the ball is snapped, he is already processing the situation, considering how the offense should respond, and preparing to execute. 

Now consider a very different setting. Inside an industrial facility, an experienced process operator is overseeing a highly automated chemical process used to produce polymers and resins. Most people have never been inside one of these facilities, but they are surrounded by the products they help create. Polymers and resins are used in building materials, packaging, coatings, furniture, automotive components, boats, and many other everyday products. 

The environments could hardly be more different. Yet both experts face essentially the same challenge: recognize what is happening, determine how to respond, and execute the response correctly. That decision-making hierarchy can be described as scenario, strategy, and skill, and it provides a useful way to understand how human expertise can be translated into AI autonomy. 

Scenario: What situation am I in? 

The quarterback is processing several pieces of information at once. He knows the down and distance, the time remaining, the defensive personnel, the formation, the field position, and tendencies learned from previous games. The defenders moving toward the line are one part of a larger picture. Together, those signals suggest that the defense may be preparing to bring pressure. 

An experienced operator overseeing a polymer or resin reactor works with a similarly rich combination of signals. The process already contains extensive automation, with sensors tracking variables such as temperature, concentration, pressure, and coolant conditions while control systems make continuous adjustments. The operator brings experience to that automated environment, interpreting how those variables are interacting and what their combined behavior may indicate. During more difficult stages of the reaction, especially the transition phase when conditions become less predictable and thermal-runaway risk increases, that judgment becomes especially important because the operator must understand how the process is evolving, not simply what any one sensor is reporting. 

A scenario is the combination of those conditions and the meaning an expert assigns to them. The quarterback sees a defensive alignment and understands what may be developing. The operator sees a pattern in the process and understands what kind of operating state may be emerging. In both cases, expert judgment begins with recognizing the situation. 

Strategy: Given this situation, how should I respond? 

Once the quarterback recognizes the likely blitz, he has to decide how the offense should respond. He may adjust the protection and move toward a blitz-beating concept that gives him a quick receiving option in the space the defense is likely to vacate.  

The operator overseeing the polymer or resin process faces a comparable decision. The reaction generates heat as raw materials are converted into the desired product, and maintaining the right temperature is critical to both yield and safety. Too hot, and the reaction can become dangerously volatile. Too cool, and the process produces less usable product.  

The strategy is the approach the operator chooses to balance those conditions and keep the reaction moving toward the desired combination of product yield, quality, and safe operation. 

The same logic applies on the football field. The playbook may contain several possible responses to pressure. The quarterback must recognize which approach best fits the situation in front of him. In both environments, strategy is the expert's chosen approach for handling a specific scenario. 

Skill: Can I execute the strategy? 

Choosing the right strategy still leaves the problem of execution. Once the football is snapped, the quarterback has to confirm where the pressure is coming from, read the coverage, identify the best receiving option, set his feet, and deliver an accurate pass before the rush arrives. Those capabilities have been developed through repeated practice and experience. 

The operator overseeing the polymer or resin reaction also relies on a collection of skills. They must interpret how the reaction is progressing across multiple signals. They need to account for factors such as changes in feedstock, cooling performance, and other operating conditions that can affect how the process behaves. Their expertise allows them to recognize whether the reaction is developing as expected or determine when an adjustment is needed. 

The quarterback and the operator both rely on skills that support a larger strategy. Their expertise comes from knowing how to apply those skills under changing conditions and how to adapt when the situation develops differently than expected. 

From Expert Judgment to Trusted Autonomy 

Scenario, strategy, and skill are parts of AMESA’s broader Data to Autonomy methodology, which is designed to capture expert decision-making and translate it into AI systems that can be trained, tested, and trusted in real operating environments. By breaking expertise into scenarios, strategies, and skills, organizations can make expert decision-making more teachable. Those building blocks can then be used to train teams of AI agents to make autonomous decisions with the judgment and reliability expected from their best human experts. You can explore the full methodology in the Data to Autonomy report presented by AMESA and Microsoft.