# Bridging the Federal Workforce Readiness Gap: A Human Performance Approach
Federal agencies are confronting an increasingly urgent challenge as mission demands grow more complex and workforce readiness gaps continue to widen. A recent survey conducted by SAIC among defense and civilian leaders reveals that workforce readiness ranks as the second biggest internal challenge to mission success overall, with civilian leaders identifying it as their single most pressing concern.
## The Upskilling Dilemma: Preparing for an Unknown Future
Addressing this readiness gap requires far more than simply recruiting new talent. It demands a fundamental commitment to building workforce capacity. The SAIC survey found that 81% of respondents believe strengthening workforce development is critical to overcoming challenges related to innovating for mission success.
The emphasis on upskilling is well-founded. Government hiring processes are notoriously slow and cumbersome, and candidates frequently need specialized qualifications and security clearances. Competition with the private sector for top talent further narrows the pool of experienced applicants. Meanwhile, agencies are expected to deliver mission outcomes with leaner teams and tighter budgets.
The path forward requires agencies to shift their strategy — moving from a focus on getting more people to a focus on getting more out of people by maximizing human performance.
## Human Performance and Technology-Powered Learning
Technology-powered learning and development represents a powerful solution for upskilling employees. Whether it is an air traffic controller sequencing aircraft for landing or a warfighter making command and control decisions in defense of our freedom, humans remain the ones making the decisions of consequence in mission-critical operations. Technology-led learning and development solutions help them perform at their best when it matters most.
Implementing these solutions successfully is multi-dimensional and extends well beyond the technology itself. SAIC’s work helping the U.S. Army train F-35 pilots has underscored the importance of monitoring data to track whether people’s performance is improving and making necessary refinements. Additionally, workforce readiness is as much about culture change and unlearning old ways of working as it is about learning new ones. This article explores the three interconnected dimensions of technology-powered learning for human performance: tools, data, and unlearning.
## The Tools: The Value of Immersive Technologies
The most effective learning occurs in real-world environments with real-world scenarios. Yet it is often difficult, dangerous, or even impossible to recreate actual training environments.
Immersive technologies address this challenge head-on. These digital tools enable virtual reality (VR), augmented reality (AR), and mixed reality (MR), creating interactive, three-dimensional environments that closely mimic the real world. Immersive technologies alter users’ perception to provide experiential, high-fidelity learning while improving skills retention and learning engagement.
Simulators represent one prominent type of immersive technology. In federal and defense agencies, they support training across aviation, wargaming, ground combat, naval operations, emergency response, cybersecurity, maintenance and repair, and more. The benefits are clear: using simulators is less expensive than live training, provides a safe way to train in dangerous and rare conditions, and allows training to occur at any time rather than only under ideal conditions.
Simulators and other immersive technologies maximize human performance in unique ways. They precisely calibrate the challenge level of learning to match a person’s ability at any given time — a critical factor. Learning science has identified a “sweet spot” of learning difficulty. If simulators are too complex, learners must focus on how to use the tool rather than on actual learning. If simulators are too simple, the brain doesn’t engage with the content. By tailoring simulators for scenario type, fidelity level, and difficulty level, agencies can help learners progress faster.
Before adopting immersive technologies, agencies should carefully understand the cost-benefit dynamics. The highest fidelity simulators are not always required. Selecting the right technology means weighing objectives and desired improvements against resource availability, including budgets and human trainers. Agencies should also prioritize modularity, which makes it easy to replace individual components such as headsets, image generators, or software without investing in rebuilding the entire system.
### Operationally Relevant, Combat-Ready Training at Scale
SAIC’s F-35 FENIX is a high-fidelity simulator designed for F-35 pilots. The U.S. Air Force and its allies are using this solution’s cockpit-level simulations for shared training and mission rehearsals in an advanced, virtual battlespace. Eight pilots can work on networked simulators practicing complex combat missions against realistic adversary defenses. As one participant explained, “During three days of FENIX execution, we accomplished as many advanced F-35 8-ship repetitions as our wing normally accomplishes in six months due to live fly limitations.”
### Round-the-Clock Immersive Learning for Pilots
The U.S. Air Force’s Pilot Training Next (PTN) program is transforming the flight training paradigm and getting undergraduate pilots up in the air faster. PTN uses significantly less expensive yet effective simulators built with commercially available components to give students in undergraduate pilot training extra “flying” time.
The Air Force developed the PTN concept and approach of bridging the classroom-to-enterprise simulator gap and turned to an experienced technology integrator for help with execution. PTN students are given desktop-size simulators for round-the-clock access to an immersive simulation environment in their living quarters. SAIC set up a virtual trainer for each pair of roommates to conduct training on their own time.
SAIC brings specific expertise in integrating training technologies, including VR and AI, and got things up and running quickly. Their work also includes establishing virtual training stations inside schoolhouses for instructor-led, formal classroom training.
## The Data: Tracking Evidence of Improvement
The value of simulators and other immersive technologies extends beyond the experiences they provide — it also lies in the data they collect.
Agencies can use this performance data to track people’s progress and continually adapt learning and development to meet their individual needs. This tracking is essential. Without evaluating performance data, organizations cannot confidently distinguish between actual proficiency and luck.
Performance data should include metrics collected before, during, and after training activities to assess the impact of the learning program. It is possible to design data models that include both cognitive and behavioral elements necessary to perform within the simulation to achieve a particular competency. This takes digital engineering, learning engineering, and generative AI tools working together to integrate cognitive and mental models into the digital representation of the system architecture. This approach provides immediate benefits to specific training programs and can also improve how agencies use immersive technologies for learning more broadly.
## Unlearning: What People Need to Stop Doing
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*This article is based on content originally published by SAIC on Federal News Network. The original article can be found at: https://federalnewsnetwork.com/workforce/2026/06/the-upskilling-dilemma-preparing-for-an-unknown-future/*# Embracing Unlearning: How AI-Powered Training Is Reshaping Military Education and Workforce Readiness
## The Rise of IP GPT and the Need for a New Mindset
The 19th Air Force’s Flying Training Center of Excellence is pioneering a bold step forward in aviation training with the development of “IP GPT,” an AI chatbot specifically trained on aviation manuals and official guidance. The goal is straightforward yet transformative: help student pilots and instructors find procedures quickly to improve decision-making, performance, and training consistency across the board.
SAIC is supporting this training modernization effort by pairing legacy software with simulator-based innovation that expands access, helps scale instructor capacity, and accelerates the learning process. But the technology itself is only part of the equation. To truly make the most of IP GPT, instructors must unlearn a deeply ingrained assumption—that being the expert means knowing the answer. Instead, they must embrace a new role: validating the answer that IP GPT provides.
This shift is not trivial. It represents a fundamental rethinking of what expertise looks like in an AI-augmented world.
## Why Unlearning Matters in a Rapidly Changing World
We live in a paradigm-shifting society where technology is being introduced with lifecycles measured in days rather than years. In this environment, unlearning must happen. Yet traditional training programs often overlook this critical dimension. They tell people what they need to start doing, but they rarely provide guidance on what they need to stop doing in order to realize the transformational benefits of new tools.
Unlearning does not automatically occur when organizations deploy new learning tools. It requires cultural rewiring—an active effort to reshape the norms surrounding the work so that people can accept and adopt new behaviors. Unlearning and cultural rewiring change the context around the new tool, including the processes, expectations, and mindsets that no longer apply.
Without this intentional focus, humans naturally default to old workflows. This is not deliberate resistance. It is simply a function of how people learn and how habits form over years of repetition.
## Building the Right Environment for Unlearning
Organizations can support unlearning by fostering the right environment. Employees need psychological safety—the permission—to stop behaviors they have been expected to perform for years. They need to be able to point out places where unlearning must happen without fear of retribution.
Leaders should champion the unlearning process, rewarding new behaviors while disincentivizing old ones. The focus should not be on perfection. It should be on making progress by practicing clear, actionable behaviors every day. As behavioral science expert Angela Duckworth notes, “To facilitate durable behavior change, it is helpful to coach people to make if-then plans that put cues in place to trigger desirable behaviors.”[4]
Including leaders in the unlearning process is key to teaching unlearning systemically, which is essential for rewiring the culture. Rather than expecting unlearning to happen by accident, organizations should build dedicated time for it into the rhythm of work and encourage employees to share their strategies with each other in a community of practice.
## Unlearning and AI-Based Learning: A Consequential Shift
Unlearning is especially important when it comes to AI-based learning. This form of learning flattens organizations, upending both “where” expertise comes from and “who” the experts are. This is a consequential shift for government organizations, given their traditionally hierarchical nature. Together, new learning models and AI are breaking down longstanding hierarchies.
Now, experts do not only provide knowledge—they also validate the knowledge that AI tools generate. This is more than a workflow shift. It requires unlearning from both experts and employees alike, challenging long-held assumptions about authority, knowledge, and the role of the instructor.
## How to Build a Strong Foundation for Outcomes
Technology-powered learning works within the context of strategy, people, and processes. Building the right foundation is critical. The stronger it is, the better the outcomes agencies can expect.
Key fundamentals include:
**Anchor strategy in mission-critical skills requirements.** Meaningful human performance strategies demand clear skills requirements as their foundation. While formal learning is essential, learning also happens organically. The long-established 70-20-10 model remains operationally relevant: 70% of learning happens on the job, 20% comes from others, and 10% comes from traditional interventions. As the lines between these categories blur in the new world of work, what matters most is building an integrated learning ecosystem where AI provides a seamless flow of knowledge, coaching, and practice directly within the workflow itself.
**Establish true change management.** No matter the technology enablement, learning and development programs succeed or fail on employee buy-in and adoption. This makes strong change management non-negotiable. Change programs should reflect goals, targeted skills, why they are mission critical, and how they align with organizational requirements and individual career progression. Employees should understand what skills and competencies are required for success and how to develop them. Change without change management undermines readiness.
**Design for flexible learning experiences.** Modern learning approaches have evolved from isolated learning events to programs designed for learning in the flow of work. This breaks learning into discrete components or micro moments that happen during daily operations. Technology-enabled tools are well suited for this flexible learning and have largely driven this shift. However, too much flexibility can cause unnecessary complexity. To avoid this, provide multiple pathways for learning but ground them in core requirements and standards for everyone.
**Adhere to humans-in-the-loop principles.** Technologies that enable human development and learning are not the end product—human performance is. Avoid adopting emerging training technologies simply because they are hyped. Instead, ground decisions in an understanding of how your workforce actually develops skills and capabilities. Additionally, account for limitations in the physical environment, such as remote locations or deployed environments where connectivity problems and unpredictable conditions limit the viability of tech-enabled solutions.
**Develop metrics and build in adaptability.** When developing learning programs, establish baselines and success criteria for what good looks like from the start. Monitor performance data continually to identify impacts early and adjust quickly. To ensure that programs evolve with fast-moving operational realities, include both proven, standardized solutions and agile, needs-based solutions that can more immediately address new skills requirements.
## Staying Ahead of the Readiness Curve
Workforce readiness is essential. But with the pace of change accelerating, the question becomes: What exactly do we need our people to be ready for? While the answers are not always clear, technology-powered learning and development can improve agencies’ readiness. The goal is progress over perfection through rapid prototyping, iteration, and fail-fast strategies. Learning systems developed this way are human-centered and built to evolve—exactly what is needed in today’s mission environments.
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*This article was developed based on content from SAIC’s insights on AI-powered training modernization, unlearning, and workforce readiness in federal and defense organizations.*
**Original Source:** Federal News Network / SAIC — “Introducing IP GPT” and related workforce readiness content. Copyright © 2026 Federal News Network. All rights reserved.
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**References:**
[1] SAIC 2025 U.S. Federal Leaders Mission Integration Survey — SAIC surveyed 153 respondents from the U.S. Armed Forces and federal government agencies. The online survey was conducted by Market Strategy Group between May 9, 2025 and May 22, 2025.
[2] Wilson, R.C., Shenhav, A., Straccia, M. & Cohen, J.D. (2019). “The Eighty Five Percent Rule for optimal learning.” *Nature Communications*, 10, 4646.
[3] Sweller, J. (1988). “Cognitive load during problem solving: Effects on learning.” *Cognitive Science.*
[4] Duckworth, A.L., Milkman, K.L. & Laibson, D. (2018). “Beyond Willpower: Strategies for Reducing Failures of Self-Control.” *Psychological Science in the Public Interest*, 19(3), 102–129.
