The Role of AI in Reshaping Legacy Industries and the Investment Implications: Institutional Change and Leadership in AI Adoption

The integration of artificial intelligence (AI) into legacy industries is no longer a speculative exercise but a strategic imperative. From defense systems to manufacturing, organizations are redefining operational paradigms through AI-driven institutional transformation. However, the success of these initiatives hinges not just on technology but on leadership strategies that navigate complex regulatory, cultural, and infrastructural challenges. For investors, understanding these dynamics is critical to identifying opportunities in a rapidly evolving landscape.

Defense and Intelligence: A Blueprint for Institutional Transformation

The defense sector has emerged as a bellwether for AI adoption in legacy industries. Palantir Technologies, for instance, has secured multi-billion-dollar contracts with the U.S. Army, UK Ministry of Defence, and Poland's Ministry of Defense, leveraging its AI Platform (AIP) to optimize battlefield analytics and logistics. This success is underpinned by a leadership strategy focused on deep integration with existing government infrastructure, ensuring mission-critical solutions align with institutional workflows. Palantir's U.S. commercial revenue surged 71% year-over-year in 2025, underscoring the scalability of its approach, according to a TS2 profile.

Conversely, BigBear.ai has carved a niche in defense and homeland security applications. Its partnership with Tsecond to deliver AI-enabled edge computing for battlefield use and the deployment of its veriScan biometric system at Chicago O'Hare Airport highlight a mission-focused strategy. Despite a smaller market capitalization, BigBear's stock rose 37% year-to-date in 2025, reflecting investor confidence in its ability to execute high-impact contracts, as detailed in a TS2 analysis. These cases illustrate how institutional transformation in defense requires not only technological innovation but also strategic alignment with sector-specific demands.

Healthcare and Energy: Navigating Systemic Risks

In healthcare and energy, AI adoption is progressing at a slower pace, with applications largely confined to lower capability levels of the AI Capabilities Framework. For example, healthcare institutions are deploying AI to enhance patient outcomes while grappling with workforce dynamics and health equity challenges. Leadership here must balance clinical effectiveness with broader institutional goals, such as reducing disparities and ensuring ethical AI governance, according to a ResearchGate case study.

Similarly, energy companies are using AI to improve operational efficiency and reduce emissions, but systemic risks like model accuracy and resource competition remain. The adoption of tools like the Comprehensive Mapping Protocol for Anticipating and Adapting to Systemic Shocks (COMPASS) is becoming essential for leaders to proactively address these risks, as that case study notes. While these sectors lag behind defense in AI maturity, their institutional transformation is gaining momentum, driven by regulatory pressures and sustainability mandates.

Manufacturing: Scaling AI with Human-Centric Leadership

The manufacturing sector has seen a 77% adoption rate of AI as of 2025, up from 70% in 2023, with applications spanning production, inventory management, and customer service. Unlike the defense sector, manufacturers are prioritizing collaborative bots-AI agents that augment human workflows over replacing them. This approach reflects a leadership strategy focused on minimizing disruption while maximizing ROI. For instance, 49% of manufacturers are investing in AI for supply chain management, but 56% remain uncertain about their existing ERP systems' readiness for full AI integration, according to a Coherent Solutions report.

Leadership in this sector emphasizes foundational preparation, such as prototyping high-ROI use cases (e.g., predictive maintenance) and upskilling workers in data science and systems engineering. Jabil Inc., for example, has pursued strategic acquisitions like Hanley Energy Group to bolster its AI data center infrastructure offerings, while also investing $500 million in a North Carolina facility aligned with cloud and AI infrastructure, per a StockTitan article. These moves highlight how institutional transformation in manufacturing requires both technological and organizational redesign.

Investment Implications: Leadership as a Competitive Edge

For investors, the key takeaway is that AI adoption in legacy industries is not a one-size-fits-all endeavor. Success depends on leadership strategies that address sector-specific challenges:
1. Defense/Intelligence: Prioritize companies with deep institutional integration and mission-critical solutions.
2. Healthcare/Energy: Focus on firms demonstrating systemic risk management and ethical governance frameworks.
3. Manufacturing: Target organizations with hybrid AI-human models and clear ROI-driven use cases.

However, risks persist. Palantir's recent stock decline, despite a "beat and raise" quarter, signals shifting investor sentiment, as noted in a WRAL MarketMinute article. Similarly, manufacturers must navigate data quality issues and cybersecurity threats, which could delay ROI.

Conclusion: The Future of Institutional Change

As AI reshapes legacy industries, leadership will remain the linchpin of institutional transformation. Investors who recognize the interplay between strategic execution, cultural adaptation, and technological integration will be best positioned to capitalize on this shift. The coming years will reward those who look beyond the hype and focus on companies with proven leadership frameworks and measurable institutional outcomes.