6 Things Every Government Leader Needs to Know to Effectively Implement AI
Faisal Hoque is recognized as one of the world’s leading management thinkers and technologists, and the founder of Shadoka, NextChapter, and other companies. His latest book is Transcend: Unlocking Humanity in the Age of AI (Post Hill Press, 2025). Reimagining Government: Achieving The Promise Of AI, co-written with Erik Nelson, Thomas H. Davenport, Dr. Paul Scade, et al, will be published by Post Hill Press in January 2026.
Adapted with permission from Reimagining Government by Faisal Hoque, Erik Nelson, Tom Davenport, Paul Scade, et al. (Post Hill Press, Hardcover, 2026). Copyright 2025, Shadoka, LLC & CACI International Inc. All rights reserved.
The orders from our nation’s leaders are now clear: federal agencies must adopt AI to improve efficiency and mission effectiveness, and they must do so rapidly. But for the government leaders navigating this transformation, the path forward can often feel uncertain. How do you balance innovation with accountability? How do you move quickly while maintaining public trust?
For our forthcoming book – Reimagining Government: Achieving the Promise of AI – my co-authors and I conducted extensive research across federal, state, and local AI implementations, including conversations with contractors and government agencies. Six critical insights emerged from this work. Together, they address the strategic, organizational, and leadership dimensions that determine whether AI transformation succeeds or stalls.
AI transformation isn’t just a technical challenge
The biggest barriers to successful AI adoption in government aren’t technical. They are human. Research consistently shows that organizational and cultural factors – not the limitations of technology – are the primary obstacles that prevent organizations from becoming AI-enabled. Yet most agencies approach the task of AI transformation primarily as a technical procurement exercise, investing heavily in infrastructure while underinvesting in workforce development and cultural change.
The agencies that will succeed are those willing to invest as heavily in their people as in their technology. This means comprehensive training that builds AI literacy across all staff levels. It means developing new competencies like data interpretation, ethical reasoning, and effective AI collaboration. It requires the creation of cultures in which experimentation is encouraged and productive failure is valued.
Consider the Department of Veterans Affairs’ mail processing automation. Success depended on helping employees understand how AI would change their roles, shifting from manual processing to exception handling. The VA achieved a 90% reduction in processing times through technology and workforce preparation.
Before you buy another AI system, ensure you’re investing equally in the organizational foundations that will determine whether that technology delivers real value.
Think about portfolios, not projects: Manage your AI transformation as an integrated investment strategy
Most agencies approach the adoption of AI capabilities by implementing isolated projects – a chatbot here, a predictive model there – without considering how these initiatives fit together. This leads to redundant efforts, missed synergies, and suboptimal resource allocation.
A portfolio management approach supports high-level decision making about a broad AI strategy. Instead of evaluating each AI initiative in isolation, treat your entire collection of AI ideas and active investments as an integrated portfolio that you balance across multiple dimensions. Maintain investments in near-term opportunities (0-12 months) that deliver quick wins, medium-term initiatives (1-3 years) that build capabilities, and long-term projects (3+ years) that position you for major future advances. Combine proven, low-risk implementations with experimental initiatives that could yield transformational breakthrough.
A portfolio approach also enables the efficient identification and sharing of infrastructure that supports multiple applications. Rather than each project building its own data governance framework, for instance, a strategic portfolio approach will allow the creation of components that can be replicated in a modular way across relevant projects.
For agencies managing annual appropriation cycles and strict accountability, a portfolio management provides the structure needed to maintain strategic coherence. Regular portfolio reviews support leaders in making evidence-based decisions about which initiatives to move forward, which to hold in place, and which to terminate.
You Need to be OPEN to innovation possibilities and to CARE about the risks
The cautionary tales from failed AI implementations are sobering. The Dutch government resigned in 2021 after an AI system wrongly accused thousands of families of welfare fraud. The UK government faced fierce criticism when AI-predicted exam scores exhibited clear bias. Arkansas’s automated disability care system caused “irreparable harm.”
These failures share a common cause: the pursuit of innovative solutions without the implementation of adequate safeguards. The solution isn’t to avoid AI – it is to balance innovation with risk management from the start.
Two complementary frameworks make this practical. The OPEN framework (Outline, Partner, Experiment, Navigate) provides a systematic methodology for identifying mission-aligned opportunities, building collaborations, testing solutions, and scaling successful implementations. In parallel, the CARE framework (Catastrophize, Assess, Regulate, Exit) establishes safeguards by identifying potential failure modes, evaluating their likelihood and impact, implementing controls, and developing contingency plans.
Innovation and risk management are not opposing forces. When managed correctly, they pull in the same direction, creating the guardrails that allow ambitious goals to be achieved while maintaining public trust.
Don’t separate your innovation team from your risk management function. When they collaborate from day one, you get innovations that are both transformative and trustworthy.
Partnerships are essential – no agency can go it alone
AI is advancing so rapidly that no single agency can keep up alone. Success requires developing partnerships across three critical dimensions.
Internal government partnerships break down traditional silos. For instance, resource pooling enables agencies to share computing infrastructure and platforms rather than building duplicate systems.
External partnerships provide access to cutting-edge capabilities you cannot develop internally, but they need to be managed carefully. Commercial vendors often develop the most powerful and cost-efficient AI products. However, their outputs are aimed primarily at broader markets rather than being adapted to government-specific needs. Government-focused contractors can serve as a critical “translation layer” that helps adapt commercial innovations to government contexts – technically, operationally, and culturally.
The biggest barriers to successful AI adoption in government aren’t technical. They are human.
FAISAL HOQUE
Human–AI partnerships are the most fundamental form of collaboration for the age of AI, so it is essential that agencies design effective relationships between employees and AI systems. The question of how a new AI system will impact your human staff needs to be tackled at the design stage. If it is treated as an afterthought, you will spend enormous amounts of time and effort trying to properly integrate AI into workflows after deployment.
AI demands a new leadership paradigm: The CITO model
Traditional technology leadership tends to focus primarily on system deployment. But this approach falls short when tackling the complex challenges of AI transformation. Instead, you need leadership that bridges technology implementation with organizational change and public service values.
Enter the Chief Innovation and Transformation Officer (CITO). The CITO operates at the intersection of a range of critical functions. Strategic alignment ensures AI initiatives advance mission objectives. Cultural leadership guides the mindset shifts that AI demands: building psychological safety, fostering collaboration, and maintaining mission focus. Technical oversight ensures systems meet standards for reliability and security. Operational excellence establishes measurement frameworks that track both implementation progress and mission impact. Perhaps most critically, the CITO builds coalitions spanning technology enthusiasts and skeptical practitioners, executive leadership and frontline employees. Sustaining transformation through political transitions and budget cycles requires this broad support.
This role requires real authority. The CITO should report directly to the agency head, participate in executive leadership meetings, and maintain budget authority for transformation initiatives. Without this positioning, even the most capable leaders will struggle to drive change.
Maturity models provide essential structure, but you should look for ways to leapfrog ahead
Technology maturity models exist for a good reason. Systematic progression from basic systems to increasingly complex models allows an organization to progressively acquire new capabilities in an organized way. But respecting maturity models does not mean shackling your agency to rigid, uniform progression across every department and project. A maturity model is a tool for helping you understand what resources you have and what you need to move forward. Once you collate that data, you can then make deliberate decisions about where to accelerate.
Domain-specific acceleration concentrates resources on high-priority areas and on areas where rapid advancement is technically possible. You might aggressively advance AI in one mission-critical domain where some of the key foundations are already in place while maintaining measured progression elsewhere. This isn’t about bypassing maturity stages but taking a granular approach that allows you to move through them strategically.
Moving Forward
These six insights provide the foundation for an integrated approach to AI transformation that addresses many of the leadership challenges facing government agencies today. Success requires recognizing that AI implementation is fundamentally human work. It requires taking a strategic approach to portfolio management that balances quick wins with transformational projects, and using frameworks that balance innovation with rigorous risk management. It depends on building partnership ecosystems that extend your capacity far beyond what any single agency could develop alone, establishing leadership models that bridge technology and organizational change, and using maturity models strategically rather than rigidly.
AI transformation is inevitable. The question is whether it will be managed well or badly. Successfully implementing this powerful technology begins with understanding these six foundational principles and then using them to deliver on the promise of AI while maintaining the public trust.
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