By testing where GenAI does—and doesn’t— add value, agencies can determine how these systems can transform operations. But achieving this also requires...
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Agencies across the government are using generative AI (GenAI) tools to accelerate operational tasks with promising results. From automating content creation to finding insights hidden in an ocean of data, GenAI frees agency employees at every level to focus on higher-level work and decision-making.
As missions evolve, the use cases for these tools also need to adapt. AI models, such as OpenAI’s ChatGPT, are becoming more capable. GenAI is poised to become infused in an expanding array of mission-centric processes.
Experimentation is the key to uncovering game-changing AI-powered capabilities. By testing where GenAI does—and doesn’t— add value, agencies can determine how these systems can transform operations. But achieving this also requires understanding the persistent myths about GenAI’s risks and accuracy.
Many mission use cases currently focus on automating tedious tasks, such as summarizing vast amounts of data or document creation. Condensing the high points of a 400-page document—or a warehouse full of 400-page documents—can quickly give human decision-makers the insight they need. Similarly, creating an RFP or technical document in minutes instead of days can enable users to stay ahead of changing conditions.
The near future will bring extensive multi-modal searches that can include images, video, sensor and satellite input, and much more to provide a more complete result. This capability may soon lead to functions such as mission planning, a perfect opportunity for experimentation.
For example, if the imagery in a mapping application is damaged or not high-resolution enough, a user could ask the system, “Create a mission plan to generate imagery from specific locations to provide these details.” Users can quickly finalize the output and operationalize the plan.
The essential question to ask is, “What if?” Different missions may need specific AI capabilities, such as geolocation and time frame, that are only now becoming more available. Trying new use cases in limited pilot programs or sandboxes can quickly prove if an idea is worth pursuing.
A secure cloud platform makes it straightforward to stand up a test environment, enabling teams to mimic real-world functions using actual data and systems without impacting current operations.
Misgivings about GenAI persist, whether valid or not, and these issues can impede new use cases. Overall, there are three main areas of concern:
Similar to reducing the cognitive load on a pilot, the essential purpose of GenAI is to provide the user with comprehensive, timely information to simplify decision-making. When integrated into mission applications and workflow, GenAI will provide decision support automation freeing mission operators to focus on achieving the objective.
The key to experimentation is finding a workflow, process, or task that could be accelerated or improved through automation — from training simulations to predictive analytics — and testing it using real-world data in a secure environment.
Working with a trusted partner to create that testbed in the cloud can save time while providing confidence in the outcomes. By embracing experimentation with GenAI, agencies can unlock its potential to drive mission innovation, evolution, and enhancement.
Read more: Classified Cloud for the US Federal Government (microsoft.com)
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