The CSA Alliance has released their annual report on AI and security. Alan, Anton Chuvakin and Hillary Baron discuss the state of AI security and governance, how companies are actually adopting AI (both agentic and generative) and most importantly how organizations are integrating it into their business practices in a secure manner. AI adoption doesn’t..
IgniteTech CEO Eric Vaughan says AI drove 2023 layoffs that cut staff by 80%, arguing the shift enabled faster product development and higher profitability.
IgniteTech CEO Eric Vaughan says AI drove 2023 layoffs that cut staff by 80%, arguing the shift enabled faster product development and higher profitability.
AI is everywhere in the enterprise, but value isn’t guaranteed. Here are the seven trends CIOs are betting on in 2026 to scale deployments, close skill gaps, modernize data, and manage rising risk.
AI is everywhere in the enterprise, but value isn’t guaranteed. Here are the seven trends CIOs are betting on in 2026 to scale deployments, close skill gaps, modernize data, and manage rising risk.
The Army is creating a dedicated artificial intelligence and machine-learning career field for officers as it pushes to integrate AI more deeply into its operations.
The new 49B specialty establishes artificial intelligence and machine learning as an official “area of concentration” for Army officers, a move the service says will help accelerate its transformation into a more data-centric and AI-enabled force.
The Army will roll out the new career field in phases. Army officers interested in transferring will be able to apply through the service’s Voluntary Transfer Incentive Program beginning Jan. 5. Selected officers are expected to formally transfer into the new career field by October 2026.
“We’re building a dedicated cadre of in-house experts who will be at the forefront of integrating AI and machine learning across our warfighting functions,” Army Spokesperson Lt. Col. Orlandon Howard said in a statement.
The Volunteer Transfer Incentive Program allows active-duty officers in the competitive category to voluntarily transfer into a different branch or functional area based on Army manning needs. Human Resources Command typically opens application windows once or twice a year, depending on a branch’s strength and personnel requirements.
Officers selected for transfer will incur a three-year active-duty service obligation, which will begin after completion of all required training.
The specialty will be open to all officers eligible for the voluntary transfer program, but those with advanced academic degrees or technical experience in AI- and data-related fields are expected to be more competitive candidates.
Selected officers will undergo graduate-level training and “gain hands-on experience in building, deploying and maintaining” the service’s AI-enabled systems.
The Army is also considering expanding the specialty to include warrant officers in the future.
The service created a new robotics tech warrant officer career field earlier this year to provide tactical units with in-house experts who can deliver robotic and autonomous capabilities directly to soldiers. The role includes training on unmanned and counter-unmanned systems, as well as networking, software engineering, electronic warfare, artificial intelligence and machine learning.
The decision to establish a new AI and machine-learning career pathway for officers comes amid a broader transformation effort aimed at preparing the Army for future warfare and optimizing its force structure and workforce. Earlier this year, Defense Secretary Pete Hegseth directed the Army to enable AI-driven command and control at theater, corps and division headquarters by 2027, field unmanned systems across every division by the end of 2026, and accelerate the integration of counter-UAS capabilities at the platoon level by 2026.
The Army also brought in four senior executives from tech giants like Palantir and Meta to be part of Detachment 201, the service’s new executive innovation corps. The four men were sworn into the Army Reserve as direct-commissioned officers in June and work at companies heavily invested in artificial intelligence and machine learning.
Meanwhile, the Defense Department has been pushing the use of large language models across the force — earlier this month, the department launched GenAi.mil, a platform designed to put “frontier AI models” into the hands of warfighters. DoD selected Google Cloud’s Gemini for Government as the first AI deployed on the new platform.
“The future of American warfare is here, and it’s spelled AI,” Hegseth said.
U.S. Army soldiers assigned to the 6th Squadron, 8th Cavalry Regiment, and the Artificial Intelligence Integration Center, conduct drone test flights and software troubleshooting during Allied Spirit 24 at the Hohenfels Training Area, Joint Multinational Readiness Center, Germany, March 6, 2024. Allied Spirit 24 is a U.S. Army exercise for its NATO Allies and partners at the Joint Multinational Readiness Center near Hohenfels, Germany. The exercise develops and enhances NATO and key partners interoperability and readiness across specified warfighting functions. (U.S. Army photo by Cpl. Micah Wilson)
In the 1980s and 1990s, many large manufacturing companies pursued an offshoring strategy — not always because a careful analysis showed a clear link between offshoring and achieving their business objectives, but because their competitors were doing it. Within a few years, companies had moved significant production overseas, often at the expense of supply chain flexibility. The problem wasn’t offshoring itself, but rather that leaders were starting with the wrong question and not creating clarity around how offshoring fit within their overall strategy. Federal agencies are making the same mistake with AI.
The Trump administration’s AI Action Plan unveiled in July 2025 has created urgency for agencies to demonstrate progress on artificial intelligence. But urgency without clear direction produces activity, not outcomes. Across government agencies, leaders are asking, “what’s our AI strategy?” when the question should be, “how can AI enable our strategy?” Here’s why.
What happens when pressure replaces strategy
The offshoring rush offers a cautionary tale that federal leaders should revisit. For many manufacturing companies, offshoring was an entirely reactive decision driven by intense pressure from Wall Street to demonstrate efforts to reduce costs.
Executives would announce an offshoring strategy, consultants would be hired, operations would be moved, and often real cost implications would only emerge over time: hidden costs in coordination, quality control, and lost flexibility to withstand disruptions. In many cases, the operational changes created strategic vulnerabilities across supply chains.
The companies that were most successful with offshoring started with their strategic objectives and considered offshoring as one lever to help reduce costs or diversify supply chains. Treating it as a tool to improve cost performance rather than an imperative in itself is what differentiated between it being a competitive advantage or an expensive distraction.
Today’s AI adoption race shows the same warning signs. Agencies are under pressure to demonstrate AI progress, and the easiest path is typically to launch pilots, create AI working groups, and report on the number of use cases identified. However, this focus on activities may or may not produce outcomes that matter to the agency’s mission.
The hidden cost of strategy-free AI adoption
When AI initiatives aren’t rooted in organizational strategy, predictable problems emerge. First, use cases cluster around process optimization rather than transformation. Teams identify ways to make existing workflows slightly faster or cheaper. While these improvements are real, they are only incremental. The transformative potential of AI to entirely reimagine current workflows and significantly change how work gets done remains untapped because of a lack of clarity on what transformation should look like in service of strategic goals.
Second, adoption becomes fragmented. Different business units pursue different tools to solve different problems with no coherent thread connecting them. This fragmentation makes it nearly impossible to build organizational capability in AI. Each initiative becomes a one-off experiment rather than a building block toward strategic objectives.
Third, and most damaging, employees disengage. When people are told to use AI without understanding how it advances the mission they care about, the mandate feels arbitrary. Especially with the heightened media coverage of AI-driven job displacement, this can lead to resistance. The goal of AI adoption is to reduce administrative burden and increase productivity. But without strategic framing, it can produce the opposite: reduced productivity as people spend time on tools they don’t understand in service of objectives that aren’t clear.
What strategy-first AI adoption looks like in practice
Consider two hypothetical federal agencies, both adopting the same AI tools.
Agency A starts by asking, “what’s our AI strategy?” They might form an AI task force, evaluate vendors, select platforms, and roll out training. They then track metrics on tool adoption and use cases identified. After a year, they report on how much of the workforce has used AI tools and the number of cases documented. But when asked about how those results tie back to the agency’s strategic mission, the answer is likely vague.
Agency B starts by asking, “what are our strategic imperatives,” “where are we seeing barriers to progress, or opportunities to accelerate?” Only then do they explore where AI could help remove the barriers or accelerate the opportunities. They might create mixed-level teams to test AI tools in sandbox environments, fail fast, and share learnings. Success is measured by progress against strategic priorities, not by adoption rates. After a year, they report that a smaller percentage of employees have used AI tools regularly, but those employees have eliminated major bottlenecks. These case studies and the results achieved inspire many more people to adopt AI tools.
Which agency got more value from their AI investment? Which agency is likely to continue to build momentum on AI?
Why top-down alone fails
Successfully adopting AI across an organization requires both top-down strategic clarity and bottom-up experimentation happening simultaneously. Senior leaders must provide a strategic framework and ask themselves questions like: Which of our objectives could AI accelerate? Where should we focus resources? What does success look like?
However, leaders can’t identify every valuable AI application alone. Employees closer to the tactical work understand where manual processes create delays, where data exists but isn’t being leveraged, and where decisions could be faster with better information. Their insights are critical to making AI adoption practical rather than theoretical.
Successful AI integration requires leaders to provide strategic direction, resource allocation and employees to experiment, learn and identify opportunities. This only happens if leaders create safe spaces for experimentation and reward employees who do so, even when experiments aren’t successful.
To further activate meaningful participation, federal leaders should engage employees in solving strategic challenges, not simply adopting technology mandates. By inviting people to join committees or creating evaluation teams that include diverse perspectives, the connection between AI experimentation and mission advancement becomes clear.
Managing the human side of technological change
More so than any previous technology implementation, AI success depends on human behavior. Two employees with identical objectives and access can produce vastly different outcomes based on how they engage with the technology. Success depends on creativity, experimentation, and integration into daily workflows.
AI adoption is, therefore, fundamentally a behavior change challenge. Employees must understand how AI serves the strategic objectives they care about and isn’t only an attempt at replacing their roles.
AI is evolving much faster than traditional management systems were designed to handle. They were built to produce reliable, repeatable performance, not rapid change. Federal leaders may need to operate outside standard practices by using dynamic experimental teams, engaging more people in finding solutions, and utilizing peer-to-peer communication where employees share discoveries with each other.
If agencies avoid the mistakes of previous management fads, the AI Action Plan represents an opportunity to accelerate mission delivery. The agencies that recognize AI transformation as a people challenge rooted in strategic clarity — not just a technology implementation — will be the ones to truly realize value from their investments.
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