​Getting AI from Experimentation to the Warfighter

​AI capabilities are advancing at extraordinary speed. The challenge for government is no longer simply gaining access to AI—it is determining where AI can create the greatest mission value, getting those capabilities into operational use, measuring whether they work, and scaling successful automation across the enterprise.

​That requires moving beyond pilots and demonstrations.

​What does winning with AI actually mean?

​Is it deploying more AI tools? Automating more tasks? Saving time or money? Improving decision speed? Increasing readiness? Giving warfighters capabilities they did not have before?

​This discussion will bring together government and industry leaders to examine the full path from AI opportunity to operational advantage: identifying high-value use cases quantitatively; building secure and governed solutions around real workflows; establishing meaningful measures of ROI and mission impact; and turning successful deployments into repeatable, scalable AI automation.

​Drawing on emerging lessons from government implementation and research such as MIT Project NANDA's GenAI Divide, the conversation will look beyond model performance to the factors increasingly separating successful AI programs from stalled pilots: workflow integration, measurable outcomes, organizational ownership, continuous learning, and the ability to scale.

​Participants will explore questions including:

• ​How do we identify the AI use cases that are actually worth pursuing?

• ​What metrics should determine whether an AI deployment is succeeding?

• ​How should government define ROI when the return may be cost, time, readiness, decision advantage, risk reduction, or mission effectiveness?

• ​What prevents successful pilots from becoming operational capabilities?

• ​How do we move from individual AI applications to scalable AI-enabled workflows and automation?

• ​Where should humans remain in, on, or above the loop as automation expands?

• ​How can government and industry work together to move from experimentation to measurable mission impact at flank speed?

​The objective is practical: not simply to put AI into government, but to create a disciplined path for finding what matters, proving that it works, and scaling it until it changes the mission.