The Perils of Offshoring and the Lessons for AI Adoption in Federal Agencies

In the 1980s and 1990s, many large manufacturing firms embraced offshoring, often not because it aligned with their business goals, but simply to keep pace with competitors. Over time, this strategy led to a significant shift of production overseas, resulting in a rigid supply chain. The issue wasn’t the concept of offshoring itself; rather, it was the failure to connect offshoring to overarching business strategies. Surprisingly, federal agencies appear to be making a parallel mistake as they ramp up artificial intelligence (AI) initiatives today.

The Impact of Pressure on Strategy

The offshoring phenomenon serves as a cautionary tale for federal decision-makers. Initially, many manufacturers shifted operations abroad due to relentless pressure from Wall Street to cut costs. Executives would quickly announce offshoring plans, engage consultants, and relocate operations without fully grasping the long-term implications. Hidden costs—like coordination challenges, quality control issues, and compromised supply chain flexibility—eventually surfaced, revealing that many organizations had unwittingly created vulnerabilities.

Successful offshoring was generally rooted in strategic objectives. Firms that viewed offshoring as a tool for achieving cost reduction or supply chain diversification saw it as a competitive advantage. In contrast, those who rushed without comprehensive analysis often found themselves distracted rather than assisted by the strategy.

Today, a similar race for AI adoption within federal agencies is underway. With the introduction of the Trump administration’s AI Action Plan in July 2025, urgency has replaced strategic clarity. Leaders scramble to demonstrate progress, focusing on launching AI pilots or forming working groups rather than considering how AI can support their strategic objectives.

The Hidden Costs of a Strategy-Free AI Approach

When AI initiatives lack a connection to organizational objectives, several predictable challenges arise:

1. Incremental Improvements: Use cases often focus on minor enhancements to existing processes instead of transformative solutions. Although small optimizations can yield benefits, they typically don’t harness AI’s full potential to radically redefine workflows.

2. Fragmentation: With different departments pursuing disparate AI tools independently, there’s often no cohesive strategy. This fragmentation inhibits the development of a unified organizational capability, reducing AI initiatives to isolated experiments devoid of tangible, strategic outcomes.

3. Employee Disengagement: If employees are instructed to implement AI without understanding its significance to their mission, they may feel disconnected. Particularly in an environment where AI’s potential for job displacement is highlighted, the push for adoption can lead to resistance rather than enthusiasm. The goal of AI should be to enhance productivity and reduce administrative burdens, but when strategic intent is missing, it can have the opposite effect.

Strategy-First AI Adoption: A Better Model

Imagine two hypothetical federal agencies, both implementing the same AI technologies but following different approaches.

Agency A begins its AI journey with the question, “What is our AI strategy?” As a response, they form an AI task force, evaluate vendors, and deploy training programs. They track metrics related to tool usage and number of identified use cases. However, when they attempt to link these results back to their strategic mission, their explanations lack depth.

In contrast, Agency B starts by asking, “What are our strategic imperatives?” and “Where can AI remove barriers or enhance opportunities?” This agency explores how AI can accelerate progress before implementing any solutions. By encouraging mixed-level teams to test AI tools in controlled environments, they promote a culture of learning and adaptability. Success is defined by advancements in strategic priorities instead of mere adoption rates.

Which agency ultimately derives greater value from its AI investments?

The Limitations of Top-Down Strategy

Achieving successful AI integration requires a dual approach: both top-down strategic clarity and bottom-up experimentation must occur in tandem. Leadership should clearly articulate strategic objectives and question which goals AI can amplify. They must also prioritize resource allocation effectively.

However, guidance from senior leaders isn’t enough. Employees on the front lines can provide invaluable insight about operational bottlenecks, untapped data, and decision-making processes that could benefit from AI enhancements. Their input is essential for translating strategy into practical applications.

Creating a culture that encourages experimentation is crucial. Leaders must assure employees that it’s safe to explore new ideas and that failed attempts are part of the journey toward finding effective AI applications.

Managing the Human Element in Technological Transition

The success of AI adoption hinges on human behavior more than any previous technological initiative. Two employees with identical resources and goals can achieve drastically different outcomes based on their engagement levels with the new technology. Creativity, willingness to experiment, and the ability to integrate AI into daily workflows become essential for real success.

AI adoption, therefore, poses a behavior change challenge. Employees need to understand how AI contributes to strategic objectives and clarifies their roles rather than positions them at risk.

Federal agencies may need to step out of conventional management practices. Traditional systems emphasize stability and repeatability, which can’t keep pace with the rapid evolution of AI. Dynamic teams that experiment, cross departmental lines to search for solutions, and encourage sharing through peer-to-peer communication will be better poised to unlock the potential of AI.

If federal agencies learn from past management missteps, the AI Action Plan could be transformational for mission delivery. Those that view AI as a challenge rooted in human behavior and strategy—rather than merely as a new technology deployment—will uncover the true value of their investments.