SKN | Barclays Survey Finds AI Is Becoming Essential for Institutional Investing as Infrastructure Challenges Loom

Key Points

• Barclays’ global survey found artificial intelligence is now embedded in the daily workflows of institutional investors, particularly in research and risk analysis.
• Hedge funds are leading AI adoption, while data security remains the primary obstacle to broader implementation across the investment industry.
• The survey’s findings align with growing concerns that future AI expansion will depend on sufficient energy, power infrastructure, and cooling capacity.

Barclays has released new research indicating that artificial intelligence has become an essential tool for institutional investors, with adoption rapidly expanding across investment research, portfolio analysis, and risk management. The findings suggest AI has moved beyond experimental use cases and is now becoming part of the daily operating model for many financial institutions.

At the same time, the survey reinforces a broader industry discussion about whether global infrastructure—including electricity generation, power grids, and data center cooling—can keep pace with AI’s accelerating computational demands.

Institutional Investors Move AI Into Daily Operations

Barclays surveyed 410 fixed-income investors across North America, Europe, the Middle East, and Asia to better understand how artificial intelligence is being integrated into institutional investment processes.

Research emerged as the most common application, with 52% of long-only asset managers and asset owners using AI primarily to support investment research. Among hedge funds, 44% reported using AI extensively to process and analyze market data.

Hedge funds remain the most active adopters overall, with 72% reporting daily AI usage compared with 49% of long-only managers and 38% of asset owners.

Despite this growing adoption, investment professionals continue to retain decision-making authority, with AI serving primarily as a tool to improve efficiency, accelerate analysis, and enhance information processing rather than replacing portfolio managers.

Security and Trading Adoption Remain Key Challenges

While AI adoption has accelerated in research and analytics, Barclays found relatively limited implementation within trade execution and portfolio trading.

Respondents generally viewed AI’s impact on execution as modest, reflecting continued caution around deploying automated decision-making in highly sensitive trading environments.

The survey also identified data security as the single largest obstacle to wider AI adoption, highlighting ongoing concerns surrounding confidential financial information, regulatory compliance, cybersecurity, and model governance.

Interestingly, fears of widespread workforce displacement remain relatively limited. Only 7% of respondents expect AI to result in significant staffing reductions, while most believe the technology will primarily improve productivity without materially reducing headcount.

AI Growth Depends on Physical Infrastructure

The survey also complements recent observations from technology investor Marc Andreessen, who has argued that artificial intelligence’s long-term expansion will increasingly depend on the availability of reliable electricity, computing infrastructure, and advanced cooling systems.

Modern AI models require enormous computational resources, placing growing demands on power generation and data center infrastructure.

According to estimates from the International Energy Agency (IEA), global data center electricity consumption could more than double by 2030 to approximately 945 terawatt-hours, approaching the current annual electricity consumption of Japan.

This highlights an increasingly important reality: future AI growth will depend not only on software innovation but also on continued investment in energy infrastructure capable of supporting next-generation computing.

What Investors Should Watch

Investors should monitor institutional AI adoption rates, enterprise software spending, data center investment, semiconductor demand, energy infrastructure development, and regulatory standards governing AI deployment.

The convergence of artificial intelligence, cloud computing, digital infrastructure, and energy investment is expected to create new opportunities across multiple sectors while reshaping long-term capital allocation priorities.

Closing Insights

Artificial intelligence is rapidly evolving from a productivity tool into foundational infrastructure for modern financial services. As adoption expands across institutional investing, the next competitive advantage may depend as much on access to computing power, energy resilience, and secure digital ecosystems as on AI models themselves.

Financial institutions, technology firms, infrastructure investors, and enterprise leaders exploring AI adoption, digital transformation, data governance, or next-generation technology infrastructure are invited to engage SKN’s senior advisory team for a confidential discussion tailored to the evolving intersection of finance, artificial intelligence, and critical infrastructure.