Trust gap slowing AI integration in energy investment strategies

Artificial intelligence (AI) is widely viewed as a powerful tool for improving renewable energy investment decisions, yet organizations remain hesitant to adopt it in practice, according to new research published in Sustainability.

The study, titled “Organizational Attitudes Toward the Use of Artificial Intelligence in Renewable Energy Investment Decisions,” reveals a clear disconnect between the perceived potential of AI and its actual use in organizational decision-making processes.

The research shows that while organizations acknowledge AI’s ability to enhance risk analysis and decision objectivity, most still rely on traditional approaches. The findings point to structural barriers rooted in trust, organizational readiness, and decision culture rather than technological limitations.

Low adoption despite strong perceived value

Only a small fraction of organizations reported using AI or advanced analytical tools in renewable energy investment decisions, even as a much larger share recognized their potential benefits.

In both survey stages, conducted on samples of 325 and 610 respondents, the share of organizations actively using AI remained low, hovering around the mid-teens percentage range. At the same time, nearly 40 percent of respondents agreed that AI could effectively support decision-making in renewable energy investments.

This gap highlights a persistent mismatch between belief and behavior. Organizations appear to understand what AI can do, but they have yet to integrate it into real-world investment workflows.

The study identifies several factors behind this hesitation. Decision-makers tend to view AI primarily as a support tool rather than a system capable of making autonomous decisions. This cautious positioning reflects the high stakes involved in renewable energy investments, which are long-term, capital-intensive, and exposed to regulatory and market uncertainties.

Another key factor is limited organizational experience. Many respondents reported uncertainty about how AI systems function in practice, which leads to hesitation when applying them in critical investment decisions. This lack of familiarity reduces confidence and slows adoption.

The data also show that even organizations that use analytical tools do not always recognize them as AI-driven systems. This suggests a broader issue of awareness and internal communication, where the presence of AI technologies is not fully understood across decision-making teams.

Trust and organizational readiness shape AI uptake

Trust is one of the most decisive factors influencing AI adoption. The study finds that fewer than one-third of respondents believe trust in AI systems is sufficient to support their implementation in energy-related decisions.

This trust deficit is closely linked to concerns about transparency, accountability, and explainability. Decision-makers remain cautious about relying on algorithmic outputs, especially when responsibility for outcomes must be clearly assigned.

The research shows that organizations using AI report higher levels of trust, indicating that experience plays a critical role in building confidence. However, the overall level of adoption remains too low to create widespread familiarity.

Organizational readiness also plays a key role. The study finds that barriers to AI adoption are not primarily technical. Instead, they stem from institutional and cultural factors, including limited analytical competencies, lack of integration into existing decision processes, and uncertainty about how to interpret AI-generated insights.

Large organizations are more likely to use AI tools than smaller ones, reflecting differences in resources and infrastructure. However, the relationship is weak, suggesting that size alone does not determine adoption. Sectoral differences are also present, with industrial organizations showing greater confidence in AI for risk assessment, while administrative entities display higher levels of uncertainty.

One of the most revealing findings is the high proportion of neutral or undecided responses. Many participants selected “hard to say” when asked about AI’s role and effectiveness. Rather than indicating indifference, this pattern reflects a transitional stage in technology adoption.

Organizations appear to be moving from awareness to implementation, but have not yet reached a level of maturity where AI is fully embedded in decision-making processes. This intermediate state is marked by cognitive acceptance without operational integration.

Structural barriers slow AI integration in energy transition

The study demonstrates that attitudes toward AI are stable and deeply rooted. By comparing two independent survey stages, researchers found minimal variation in responses, indicating that current perceptions are not temporary but structurally embedded.

This stability suggests that the barriers to AI adoption are systemic. Organizations are not simply waiting for better technology. Instead, they face challenges related to governance, culture, and decision frameworks.

Renewable energy investments add another layer of complexity. These decisions require balancing economic, environmental, technological, and regulatory factors. AI has the potential to process this complexity more effectively than traditional methods, but organizations are reluctant to shift responsibility away from human judgment.

Consequently, AI is often confined to analytical support roles, such as risk assessment and scenario analysis. Its potential to transform decision-making remains largely untapped.

The research also highlights the broader implications for the energy transition. Efficient capital allocation is critical for scaling renewable energy projects, and AI could improve decision quality by reducing uncertainty and information asymmetry.

By enabling more accurate risk assessments and better alignment with market conditions, AI could lower investment costs and increase the availability of financing. However, these benefits depend on overcoming organizational resistance and building trust in algorithmic systems.

The findings suggest that policy and institutional support will be essential. Measures such as promoting pilot projects, establishing transparency standards, and integrating AI into public funding evaluation processes could help accelerate adoption.

Within organizations, gradual implementation is recommended. AI should be introduced as a decision-support tool, with a focus on enhancing human judgment rather than replacing it. Training programs and competency development will also be critical for enabling decision-makers to interpret and use AI outputs effectively.