SAN FRANCISCO - June 18, 2026 - AMESA, the AI agentic orchestration and proving ground platform, today introduced “Data to Autonomy,” a proprietary methodology designed to help industrial enterprises move from disconnected operational data and AI pilots to autonomous systems capable of executing reliably in real-world environments.  

Rather than requiring enterprises to rebuild existing infrastructure, AMESA’s Data to Autonomy methodology combines operational data analysis, simulation environments and agent orchestration to help organizations identify high-value operational decisions, capture expert knowledge and train systems before deployment. 

The announcement comes as 97% of manufacturing firms report concern about the loss of institutional and technical knowledge, while many enterprises continue to face fragmented data environments and growing operational complexity that makes it difficult for AI initiatives to scale. Although many organizations have invested heavily in digital infrastructure and data modernization, critical data often remains disjointed across systems and difficult to translate into consistent decision-making.  

“Most industrial companies already have the data and expertise they need,” said Kence Anderson, CEO and founder of AMESA. “The challenge is alignment across operations, engineering and data teams on how to turn that into something that actually runs in production. Too often, AI initiatives stall because they never make it past experimentation or cannot be safely validated in real-world conditions. Data to Autonomy provides a structured way to bridge that gap using the systems enterprises already operate on.” 

The methodology is built around three core capabilities: 

• Organizing operational data into activity clusters that represent recurring operational scenarios and decision patterns 

• Capturing and validating expert knowledge through a structured Machine Teaching process 

• Testing agent configurations and operational strategies in simulation against measurable business benchmarks before deployment 

AMESA is currently deploying its simulation and orchestration platform across more than a dozen Fortune 500 organizations spanning manufacturing, energy, aviation and industrial operations. Deployments have demonstrated measurable operational improvements, including more than 60% waste reduction in high-speed fill operations for a global consumer packaged goods manufacturer, up to $21 million in annual profitability improvement through refinery crude oil blending optimization and approximately $1.2 million in projected annual savings in a nitrogen gas manufacturing process. 

The methodology builds on Anderson’s earlier work developing The Machine Teaching Methodology and autonomous systems at Microsoft. 

"Industrial companies have spent years modernizing infrastructure and building vast data estates. The question now is how to turn those investments into action on the operations floor," said Dayan Rodriguez, Corporate Vice President, Manufacturing and Mobility, Microsoft. "As AI matures, the focus is shifting to systems that preserve hard-won expertise, adapt to workforce change and deliver consistency in complex environments. AMESA's Data to Autonomy methodology is a pragmatic path from experimentation to trusted industrial deployment."  

To illustrate the methodology in practice, AMESA is also releasing an e-book, “Data to Autonomy,” co-presented by Microsoft. The e-book outlines a practical framework for identifying, validating and scaling autonomous systems using existing enterprise data and expertise. 

To access the e-book, please visit https://www.amesa.com/data-to-autonomy-ebook.