BBC

HIWFRS

Wartime “legacy”

City staff in the planning department at Bellevue, Wash., have a new assistant that already has a commanding understanding of development codes, zoning maps and other detailed information.

The department has been using an AI-powered “smart assistant” to aid in the city’s permitting processes. Earlier this month, Bellevue moved the project — a partnership with Govstream.ai — from its early testing phase to “real-world scenarios,” Sabra Schneider, Bellevue CIO, said.

The smart assistant tool has been trained in the city’s development code, GIS data, zoning codes, parcel data, permit history and more so that it can assist in speeding up the permitting process, Safouen Rabah, founder and CEO for Govstream.ai, said.

“The system drafts code-cited emails, checks plans for completeness, flags zoning conflicts, and answers routine questions,” Rabah said via email. “But staff always remain in control — reviewing, editing and making final decisions.”

The assistant is three tools in one, Schneider said via email: chat, email and voice, “all tied into the same knowledge base and built for government use, with traceability and staff oversight built in from the start.”

For now, planning staff is using the AI assistant to look up parcel-specific zoning rules and then generate draft responses to resident inquiries, with citations to the development code.

“We’re also starting to use the tool to analyze conversations between the AI assistants and staff for thematic insights about how our community interacts with different aspects of permitting,” Schneider said.

Ultimately, technology like this smart assistant will free staff from repetitive tasks — which Rabah described as “document toil” — allowing them to focus on “judgment-driven work like safety, equity and community priorities.”

Bellevue also plans to extend the technology to the public so that residents, designers or contractors can also benefit from fast, “citation-based answers” to questions regarding projects or processes, Schneider said.

Initiatives like the Govstream.ai pilot stem from Bellevue’s Innovation Forum, which brought together members of the community, startups, students and others to brainstorm how to enhance city services and the overall quality of life — part of a broader strategy, Schneider said, to make innovation more open and actionable. The city will host a Civic Innovation Day Oct. 16 to highlight civic innovation in the greater Puget Sound area.

AI tools that help to cut down on repetitive tasks, search quickly through data or smooth out workflow gaps work in the kinds of areas where experts say artificial intelligence can bring value to organizations. The technology, they stress, should serve people.

“When I talk about artificial intelligence, I not only talk about computation. I talk about people, first and foremost,” Nathan McNeese, founding director of the Clemson University Center for Human-AI Interaction, Collaboration and Teaming, said during a recent webinar to discuss the new report, Strategies for Integrating AI into State and Local Government Decision Making: Rapid Expert Consultation, by the National Academies of Sciences, Engineering and Medicine.

“You need to understand who your people are before you can even have the idea of putting AI into an organization. Because AI’s going to fail in your organization if people are not your first consideration,” he said.

And starting small — a pilot project in one city department, limited to internal staff — is also advisable, Suresh Venkatasubramanian, a computer science professor at Brown University, where he directs the Center for Technological Responsibility, Reimagination and Redesign, said as he urged tech leaders to embrace an ethos of experimentation and “sandboxing” to understand how the AI systems work, within a specific context.

“AI is not like buying a piece of software, you buy it once, and it’s done, because it serves a specific purpose,” Venkatasubramanian said during the webinar. “It’s one of those things where it’s an ongoing conversation amongst the folks using the system, the folks impacted by the system.”

This is part three of a three-part series examining how the Air Force is experimenting with artificial intelligence for its DAF Battle Network capability. It is based on several exclusive interviews with the 805th Combat Training Squadron, the Advanced Battle Management System Cross Functional Team, airmen and members of industry. Part one can be found here, and part two can be found here.

LAS VEGAS — A recent experiment held by the Air Force demonstrated that industry-developed artificial intelligence tools can not only shorten the time it takes battle managers to make decisions, but also give operators a more holistic picture of the battlespace.

The 805th Combat Training Squadron hosted its second Decision Advantage Sprint for Human-Machine Teaming (DASH) wargame in July, bringing Air Force personnel and industry software teams together at the squadron’s unclassified office in downtown Las Vegas, Nevada. DefenseScoop observed part of the two-week event, where vendors custom-coded AI-enabled microservices and tested how the technology could be applied to command-and-control operations.

“DASH 2 continued the Air Force’s campaign of learning to accelerate decision advantage for the joint force,” Col. John Ohlund, director of the Advanced Battle Management System (ABMS) Cross Functional Team, said in a statement. “While detailed results are still under review, the experiment advanced our understanding of human-machine teaming, match effectors, and collaborative battle management. The effort provided valuable insights that will guide future development.”

The DASH sprints are a new wargame series led by the 805th, also known as the Shadow Operations Center – Nellis (ShOC-N), and are designed to test emerging AI capabilities and concepts for battle management. Each experiment focuses on one subfunction of the Air Force’s Transformational Model for Decision Advantage, a methodology that breaks down the entire C2 process into 52 specific choices a warfighter makes during operations.

Teams from six companies — who asked to remain anonymous — as well as a group of Air Force software engineers participated in the DASH 2 wargame. Throughout the event, coders developed an AI microservice for the subfunction known as “match effectors,” which decides what weapons system is the best available to destroy an identified target.

“They’re going to run a generic red versus blue [scenario],” Lt. Col. Wesley Schultz, ShOC-N’s director of operations, said in an interview. “In general, offensive counter-air is going to go to the red side, drop a bomb [or] shoot down things and then get out of town.”

While the scenario seems simple in practice, there are dozens of variables an air battle manager considers when deciding what weapon to use. Those include tactical decisions — such as the specifics of the target and availability of blue forces — as well as environmental factors like weather and risk to civilians.

“As we get into the more complicated tasks, I am not saying, ‘Okay, I want a bomb on this airplane.’ It is, ‘I need this weapon on this airplane, plus another weapon on another airplane, plus an intelligence aircraft that is providing support, plus I need a data link.’ Finding all of those effects together takes time, and it is a very slow process,” Capt. Steven Mohan, chief of standards evaluations for the 729th Air Control Squadron and one of the participating battle managers, told DefenseScoop.

To lighten that cognitive load, software vendors at DASH 2 were tasked to develop an AI-enabled microservice that could ingest battlefield data and create a ranked list of available effectors for a battle manager to choose from. 

Each company would routinely stress-test their code during 45-minute simulations called “vulnerabilities,” or VULS, where they could receive real-time feedback from air battle managers.

Senior Airman Besner Carranza of the 729th Air Control Squadron said that when a tasking order would appear in their system, the vendor’s AI would replicate that tasking and create a ranked list of matched effectors within the area or responsibility they could choose from. Oftentimes, the software would also provide reasoning behind their rankings, giving battle managers more confidence when making decisions.

Carranza noted that during the baseline run, the entire process took approximately 10 minutes for them to complete. But when the AI tools were integrated, it was much faster.

“For me as a battle manager, it’s difficult because we get so many taskings at once. And having that [user interface] helps us integrate, using both of our knowledge and brains to match-effect the best course of action,” he said.

Industry teams often sat next to air battle managers during the VULS, receiving critical feedback that was used to improve their software. Some vendors even ran through the simulation while being instructed by airmen to provide additional context to the problem, according to Capt. Steven Mohan, chief of standards evaluations for the 729th Air Control Squadron.

“From the first demo of, ‘This is what our product looks like,’ to the first test run with data, to what we’ll see with the final sims today, we’ve been seeing improvements that helped basically teach the vendors to speak the language so that it fits naturally in our workflow,” Mohan told DefenseScoop.

The experiment also highlighted areas where both the Air Force and industry could improve. For example, Mohan said the AI tools would generate inaccurate results or fail to recognize a specific message — signalling that the service could be more organized with the data it’s using.

Members of industry also noted their struggles with integrating various sources into a common data layer. 

“Each team addressed this challenge in different ways,” one industry team noted. “We took an approach that allowed us to test the capabilities on which we were conducting research; however, releasing the data artifacts during [request for proposals] solicitation would help the government get better solutions.”

Another key factor was that the simulations included weapons systems from the other services, such as Army Terminal High Altitude Area Defense (THAAD) batteries, Navy Arleigh Burke-class guided-missile destroyers (DDGs), space-based systems and cyber capabilities.

Multiple air battle managers at DASH told DefenseScoop that the inclusion of multi-domain capabilities was challenging, but opened their eyes to options outside of Air Force assets.

“If I need to investigate something and put sensors on it, my mind automatically goes for ground-based tracks like the RC-135 [Rivet joint reconnaissance aircraft], but maybe a space asset will be much better because then I don’t have to put the Rivet Joint in harm’s way trying to get over there,” Staff Sgt. Jacob Mucheberger of the 128th Air Control Squadron said.

He added that participating in DASH demonstrated that if the Air Force had to use assets from other services in future conflicts, he personally doesn’t have the training to fully leverage those systems because he isn’t well-versed in the specific capabilities.

“Now I’m thinking about space assets or naval assets or cyber assets and things of that sort, and how to implement them,” Mucheberger said. “But it also makes me think this sort of program is really needed, because the whole idea of match effectors is generating effectors that you wouldn’t have otherwise thought of.”

The inclusion of multi-domain capabilities is integral to the Air Force’s plans for the DAF Battle Network, an integrated “system-of-systems” that will support the Pentagon’s Combined Joint All-Domain Command and Control (CJADC2) concept. The effort broadly seeks to connect sensors and weapons from across the military services and international partners under a single network, enabling rapid data transfer between warfighting systems.

“They have to work all-domain problems, right? And because these problems don’t respect the boundaries of our services, we have to have naval and air forces and cyber and space working together,” Col. Jonathan Zall, ABMS capability integration chief, said in an interview. “And so these battle managers have got to be able to come up with solutions that don’t just involve Air Force assets or airborne assets.”

Moving forward, the ShOC-N has one more DASH sprint planned for 2025 and expects to hold four wargames in 2026. Both the squadron and ABMS Cross Functional Team believe that the results from each event will help the Air Force’s program executive officer for command, control, communications and battle management (C3BM) develop future requirements for individual AI microservices specific to the subfunctions under the transformational model.

“We’re able to put that repository into requirements, and now the Air Force — really, I shouldn’t even say the Air Force, it’s agnostic of service — can go acquire exactly what they want, as opposed to a vendor coming along and saying, ‘Hey, I think I know what you want,’” ShOC-N Commander Lt. Col. Shawn Finney explained during an interview at Nellis Air Force Base in Las Vegas, where the 805th is headquartered. “We’re trying to flip that and say, ‘No, that’s exactly what I want. It fits exactly with the specific need that we need.’”

And while the 805th is very early on in their experiments, officials said they’re already seeing proof that their vision for the DAF Battle Network could one day become a reality. Ohlund noted that future events could experiment with multiple microservices together at a single DASH sprint — something in line with efforts by Maj. Gen. Luke Cropsey, PEO for C3BM, that pushes for “disposable software.”

“The way I interpret that is, we need to be able to experiment with one version of software, and if there’s another one or a better one that’s out there, we should be able to replace it. We should be able to plug and play within the government reference architecture,” Ohlund said.