As the Army explores scaled employment of commercial-off-the-shelf artificial intelligence, the service’s chief information officer wants to make sure the technology’s use is being optimized in a way that it doesn’t break the bank.
“Our big lesson learned is, this is expensive stuff to do. I think that’s really driving our peeling back and tightening the guardrails on use cases,” Army CIO Leonel Garciga said Monday in an interview with DefenseScoop.
Over the last few years, the Army has initiated AI-focused prototype efforts — such as the CamoGPT large language model and Project Athena — to understand both the technology itself and how to effectively deploy and manage it. The service recently pivoted to a commercial model in May with the launch of the Army Enterprise Large Language Model Workspace, a generative AI platform powered by AskSage that’s used for tasks ranging from drafting press releases to reclassifying personnel descriptions.
But while the Army does see a future for AI in the department, Garciga said that the cost of running the software itself — whether the raw store and compute or tokens that LLMs use in processing — is influencing the specific use cases the service wants to apply the tech for in the near term. As a result, the organization is taking lessons learned from its prototype efforts to ensure the technology is available, but at an appropriate cost, Garciga said.
“There are many times that we find folks using this technology to answer something that we could just do in a spreadsheet with one math problem, and we’re paying a lot more money to do it,” he said. “Is the juice worth the squeeze? Or is there another way to get at the same problem that may be less cool from a tech perspective, but more viable from an execution perspective?”
For example, the service recently used artificial intelligence to review all of the position descriptions within the Army that shut down multiple GPUs for a cloud-service provider for eight hours. In an event where hundreds of thousands of people in the Army turned to AI tools for smaller jobs on a frequent basis, “that gets expensive real, real fast,” Garciga said.
“At times, we’re using some of these capabilities to do it, leveraging GPUs and getting all that done, when we could just do a regular algorithm on regular compute and it would get the same result in the same amount of time. It’s just more convenient to have it at the tips of your fingers,” he said.
The Army has been reviewing AI use cases from CamoGPT and other pilot efforts since November 2024 and is currently focused on understanding how the technology can be integrated across two key areas, he noted.
The first use case is to fill gaps within the service’s civilian and acquisition workforce, which has decreased in recent months due to budget cuts and the Trump administration’s push to downsize federal agencies. The second is finding opportunities to “rethink the way that we deliver certain services,” Garciga said.
In the future, the CIO said the Army is likely to focus on deploying more commercial AI platforms like the Army Enterprise LLM Workspace and fewer bespoke models, partly due to the cost of compute and storage, but also because enterprise-level systems are difficult to manage.
“The commercial space has kind of figured this out, and though there’s still some challenges on how we pay, whether it’s native and cloud or something that’s more COTS, we’ve still got some work to do in that space,” he said.
The Dr. Pritam Singh Foundation, in collaboration with IILM University, hosted a discussion on “Human at Core: AI, Ethics, and the Future” at Tech Mahindra, Cyberabad, on Saturday, in memory of the late Dr. Pritam Singh, a noted academic.
After launching the discussion, Assembly Speaker Gaddam Prasad Kumar highlighted the ethical challenges of Artificial Intelligence (AI), warning against algorithmic bias, threats to data privacy, and job displacement. He called for large-scale reskilling and emphasised that India must shape AI technologies to reflect its values of fairness, transparency, and inclusivity. He urged corporate leaders to establish strong governance frameworks, audit algorithms for bias, and ensure responsible adoption of AI.
Delivering the keynote address, Chairman of Administrative Staff College of India (ASCI) K. Padmanabhaiah stressed India’s opportunity to leverage AI for inclusive growth across healthcare, agriculture, education, and fintech — while ensuring technology remains human-centric and trustworthy.
One of the founders of the Dr. Pritam Singh Foundation P. Dwarakanath, Director at IILM University Chaturvedi, Director at the Institute for Development & Research in Banking Technology (IDRBT) Deepak Kumar, Managing Director of Signode Asia Pacific Gaurav Maheshwari, Pritam Singh’s son Vipul Singh, and author and economist Vikas Singh spoke.
With fears about the strength of consumer spending running high due to tariffs, inflation and other economic pressures, retailers are working hard to sustain revenue growth. While some retailers are leaning into worker-led personalized experiences for shoppers, other retailers are focusing more on leveraging artificial intelligence to optimize the shopping experience.
Walmart is one of those retailers, adding new “super agents” that aims to save time and effort for both workers and shoppers. At its recent Retail Rewired innovation event, Walmart highlighted the launch of four “super agents,” which include Marty for sellers and suppliers, Sparky for shoppers, the Associate Agent and the Developer Agent.
With agents performing capabilities in the realm of payroll, paid time off, merchandising and finding the right products for any event, Walmart is consolidating its powerful, time-saving tools for the sake of a streamlined experience for multiple points of interaction with the company.
“Having a plethora of different agents can very quickly become confusing,” Suresh Kumar, chief technology officer for Walmart Global, said at the event.
The Associate Agent, for example, is “a single point of entry where any associate can find access to all of the agents we’ve built on the back end,” explained David Glick, senior vice president for Enterprise Business Solutions at Walmart. “As you speak to it more, as you work with it more, it’ll know more about you.”
The evolution comes alongside a broader shift for retail, an industry actively seeking to counteract cost concerns from consumers and the government, and Walmart isn’t alone in its push toward all things AI. Amazon’s Prime Day event over four days in July saw generative AI use jump 3,300% year over year, according to TechCrunch. Meanwhile, Google Cloud AI partnered with body care retailer Lush to visually identify projects without packaging, ultimately reducing the expense of training new hires.
Making digital twins of Walmart stores
Walmart is also all-in on physical and spatial AI, specifically digital twins (a virtual copy of any physical object or space — in Walmart’s case, their stores and clubs). Using digital twin technology powered by spatial AI, Walmart can “detect, diagnose and remediate issues up to two weeks in advance,” Brandon Ballard, group director for real estate at Walmart US, said at Retail Rewired. Using this technology comes with big savings, according to Ballard. “Last year, we cut all of our emergency alerts by 30% and we reduced our maintenance spend in refrigeration by 19% across Walmart US,” he added.
“At its core, retail is a physical business,” said Alex de Vigan, CEO and founder of Nfinite, which generates large-scale visual data for training spatial and physical AI models. “We’ve seen retailers use digital twins to reduce setup time for new promotions, reallocate labor more efficiently, and improve robotic picking accuracy, small gains that add up quickly when margins are under stress,” he said.
While the impact of digital twins may not be outwardly visible to consumers in the same way, say, Walmart’s Sparky agent is, its effects will be real. “Better stock accuracy, faster site updates and fewer order issues mean a smoother retail experience, even in a tighter economy,” said de Vigan.
Another innovation on the back end is Walmart’s use of machine learning to better understand how long it will take to get a delivery order on a customer’s doorsteps, effectively managing expectations while increasing efficiency.
As for what consumers can see, Sparky is already helping shoppers generate baskets built on an intuitive understanding of their needs. Walmart is currently working on enabling the agent to take action on reordering products, ultimately reducing the mental load that shoppers deal with.
For retailers, AI is one way to combat any slowdown in consumer spending, but we’ve yet to see how a fully integrated AI shopping experience — both in person and online — will shape our relationship with retail moving forward.
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Amid the hype over AI, a practical question: When will the technology boost the economy the way its developers and promoters are promising? Is artificial intelligence going to unleash a surge in worker productivity, as epochal new tech has done in the past? Or is investor enthusiasm for it overdone?
In one sense, AI is already adding to GDP. Spending on AI hardware is astronomical, both for the costly, specialized chips that power AI and the related infrastructure to deliver the electricity that those chips devour. This spending raised GDP by 0.3% in the second quarter of this year. Even that doesn’t fully capture the size of this investment surge, since some capital outlays that tech firms are making don’t show up in the official GDP accounting method. Just look at the top five firms by AI investment: Amazon, Alphabet, Meta, Microsoft and Oracle. The increase in their AI-related capital expenditures over the past two years equals about 10% of GDP gains in the U.S. over that time period. Add the power plants, transmission lines and other infrastructure they need to run their data centers, and the outlay is even bigger.
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There are also signs that businesses are gearing up for AI to make an impact on their operations. Company mentions of AI use for research tripled since Nov. 2022, when ChatGPT launched and turbocharged generative AI. 25% of job listings posted for IT professionals since the start of last year have asked for AI-related skills. The number of mobile AI app downloads hit 60 million this March. Internet searches related to AI have grown tenfold since OpenAI unveiled ChatGPT to the public. When it comes to whether AI will make workers more productive, the picture gets murkier. There are some early signs that it’s happening. Inflation-adjusted revenue per worker among S&P 500 companies has been rising since late 2022, following a 15-year period when it stayed flat.
It’s not clear why, but the overlap with advanced AI applications going mainstream is hard to ignore. But with so much money pouring into AI, there are reasons for skepticism. Much of the investment being made today could end up wasted. Many companies that are in vogue now figure to fail. It’s possible that AI computing power being rushed online could ultimately prove to be unneeded, akin to how fiber-optic cable networks got overbuilt in the 1990s. That capacity eventually got used as data consumption rose, but not before builders who spent too much on it went bankrupt. If the current AI data center boom fizzles, the pullback in spending could spark a mild recession, as the tech bust in 2001 did. Most major technological leaps take time to filter through the economy. AI does seem genuinely transformative. But the transformation may take many years.
This forecast first appeared in The Kiplinger Letter, which has been running since 1923 and is a collection of concise weekly forecasts on business and economic trends, as well as what to expect from Washington, to help you understand what’s coming up to make the most of your investments and your money. Subscribe to The Kiplinger Letter.
