BigBear.ai (BBAI) Partners with DEFCON AI to Deliver Next-Gen Military Decision Intelligence Solutions
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Unlike more generic AI plays, this deal anchors BigBear.ai deeper into the U.S. defense ecosystem, targeting logistics networks that underpin everything from troop movements to supply chain resilience. DEFCON AI, already known for its work on modeling sustainment for global operations, brings complementary capabilities to BigBear's decision support systems. Together, they aim to tackle readiness planning across land, air, sea, and cyber, improving how military forces prepare and respond under uncertainty.
BigBear.ai is a U.S.-based provider of AI-driven decision intelligence solutions for both government and commercial clients. Its platforms help users forecast, simulate, and optimize outcomes in real time, with core applications in logistics, autonomous systems, national security, and digital transformation.
While we acknowledge the potential of BBAI as an investment, we believe certain AI stocks offer greater upside potential and carry less downside risk. If you're looking for an extremely undervalued AI stock that also stands to benefit significantly from Trump-era tariffs and the onshoring trend, see our free report on the best short-term AI stock.
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AI is quietly reshaping the efficiency, power, and potential of U.S. healthcare, even as government health policy and spending drastically shift. Philips, the legacy electronics manufacturer turned medtech provider, is leading the AI healthcare revolution, streamlining and accelerating the workflow of patient care. Philips North America CEO Jeff DiLullo shares how technology can have the biggest impact on health outcomes today—from radiology scans to cancer diagnoses, and what it takes for leaders in any industry to rethink the way we work to best meet the moment. This is an abridged transcript of an interview from Rapid Response, hosted by the former editor-in-chief of Fast Company Bob Safian. From the team behind the Masters of Scale podcast, Rapid Response features candid conversations with today's top business leaders navigating real-time challenges. Subscribe to Rapid Response wherever you get your podcasts to ensure you never miss an episode. AI seems to be changing everything. There's a lot of talk about it, but in some businesses, I feel like the conversation about it is ahead of the actual implementation or the impact, and I'm curious how true that might be in medtech. How is AI impacting things now, today, versus what you think it can do in the future? If you remember, we released the Future of Health Index. One of the things that we realized is that AI, in some of these compartments I'm talking about, is quite mature. FDA cleared, very safe for clinical use. Other areas, it's more experimental. But the trust factor of the use of that AI is actually quite nascent. It's the biggest barrier right now to larger scale deployment. Yeah. That health index that you mentioned, the 2025 Future Health Index, I mean, there was this sort of trust gap in it, right? That something like 60, 65% of clinicians trust AI, but only about a third of patients or certainly older patients do. How do you bridge that gap? Is it Philips's job to bridge that gap? Whose job is it? So I have the benefit of having two Gen Zs and a millennial, they are digitally fluid. They don't worry at all about the AI models that are coming on the other side of this because they're used to it and they understand it. Older patients, not so much. The magic is always the healthcare practitioner that's directly interfacing with the customers or the patients. If they believe what they're doing, if they know it's credible, if they're using it to augment their analysis or their diagnostics, not replacing it, I think ultimately we'll see an uplift. It's our job to provide valid FDA-cleared, very good diagnostic capability leveraging AI. But if our doctors and nurses believe what we're doing and they see the value in increasing their time with patients and also a little de-stressing, we think it's going to really pick up in a parabolic way in the next few years, at least in health. I can understand and see how AI can quickly help some of the back office functionality in healthcare, but you're talking about for practitioners, right? How does that practically work today? So I'm going to give you, let's talk radiology. It's the biggest field right now, diagnostic, right? The earlier the diagnostic, the better the outcome most likely. And when I think of a radiologist, I have to wait a month and a half. I'm in a pretty nice part of Vanderbilt University area, like a lot of health tech around me in Nashville, but I've got to wait over a month to get a scan. So in radiology, we start with the box or the design, right? I have an MRI that is highly efficient. I can move it around, I can put it on a truck. But today, I can get a scan done in half or even a third of the time. The AI built into the system software makes it much faster. Just a few months ago, I had a scan that took only 20 minutes—whereas a couple of years ago, the same scan would have taken about 45 minutes. The smart speed that we have on the system actually compresses the scanning time. It doesn't fill in the blanks, it removes the noise. You actually get a better scan in a shorter time. If you're a radiologist having to do 12 or 15 studies a day, but you can do 20 studies a day, I get more patients through, I drive more reimbursement, it's better for the hospital, it's better for patient care. Then I take it into workflow, and today I can pinpoint things that are happening in that digital image and send it to a radiologist and say, 'You should look here,' in just very simple speak. It's very complicated stuff, but the AI is already mainstream today where we can actually pinpoint areas for radiologists to look at and make a determination. I can digitize the whole process today with digital pathology. And I can have a finding where somebody's waiting, do I have cancer or not? I can do this in hours now because it's all digital. And that kind of workflow and orchestration is a game changer. And the issue of AI hallucinations, which show up with some of the generative AI things, does that apply to healthcare? Are there different kinds of safeguards? Because I guess there's a human who's checking. There's so many things today, like smart speed I just talked about, being able to run that radiology workflow to compress the time of diagnostics, run the tumor boards in hours, on-demand meetings like you and I would on Zoom or teams, all of that is happening today, but not happening at the pace it could. My point is, go do that right now. Every health system, go do that. As you start to unpack these more generative AI models, I think there's real reason to be cautious and make sure we have the right controls and the governance on them, but not experimenting in them also is not an option. We kind of have to. But we see leading institutions, MGB, Stanford, Mount Sinai in New York, we see them really working with population health data to really try to train models on very specific and even broad use cases. There's so much to do right now. In other words, you don't have to go all the way out to the silver bullet of, we're going to live forever or we're going to solve every health problem. You can make the system we have right now more efficient and more effective today. Bob, when you first drove a car, was the first thing you did to go to the Autobahn? Probably not. There's so much to do in the neighborhood. There's so much to do in my town that I can really get good at what we're doing and drive productivity at scale. You need to have the innovation and the creativity to get us to the next place, but 80% of it we can do today. That is just game-changing in terms of how we deliver today, and that's what we think is really the next opportunity here for healthcare. And I think that'll happen with what's mature in AI and virtual capabilities in the next few years because the need is so great.
