Artificial intelligence is reshaping work by providing speed, efficiency, and instant insights across industries. But the risk of AI abuse is particularly high for infrastructure investment teams, where a phantom contract term or miscalculation of financial ratios can put millions of dollars at risk. This risk has led many such companies to ban AI altogether, while others are conducting ad hoc experiments that can create significant professional liability.
Neither of these methods is viable as a long-term solution. For project funders, thoughtful and practical AI applications must include strict integrity, privacy, and accuracy guardrails, with purpose-built systems validated to industry demands.
Leveraging Technology to Improve Efficiency and Profitability of Infrastructure Portfolios In our experience building project finance software at Banyan Infrastructure, we have seen firsthand the risks that unchecked AI adoption poses to bias, rigor, and accountability. To combat these risks, we must start building the foundation for responsible use of AI in project finance now.
Understand artificial intelligence
Project investors often confuse automation and AI, but these technologies play different roles. Automation is deterministic and repeatable. For example, use Ctrl + F to search for documents. Get reliable results even when AI features are turned off. In contrast, AI systems introduce probabilistic behavior that requires stronger monitoring and safeguards, especially in high-stakes workflows like project finance.
Some tasks benefit from AI’s probabilistic reasoning, while others are better suited for predictable automation without AI. To implement AI responsibly, project finance professionals must first understand the types of intelligence available and the real outcomes each can deliver.
- Search extension generation: Search extension generation connects large language models to one or more internal or external knowledge sources, such as an organization’s document repositories or systems of record. In project finance, these systems allow teams to capture and extract critical information from term sheets, financial models, and due diligence reports, and store that information in a standardized format.
- Agent AI: AWS definition AI agents as software that can “perform autonomous tasks that achieve predetermined goals.” In project finance, AI agents can detect overdue contracts, create reminder emails, and generate monthly reports for approval.
- model context protocol: Model Context Protocol is an open, emerging standard that allows AI applications and agents to connect to external tools and data sources. Although such integration is still in its infancy, it points to a future where a single AI interface reaches many systems and alleviates much of today’s context switching between platforms.
Where AI breaks
AI risks in project finance rarely manifest as clear failures. Often it appears as a small shortcut that quietly weakens rigor and responsibility. For example, a model trained on historical transaction data can be a useful tool. However, without human oversight, existing patterns can be reinforced and inadvertently bias due diligence, such as overvaluing certain asset classes, sponsors, or structures.
Similarly, incorporating AI-generated output directly into credit memos and reports can lead to decision-making that lacks a clear audit trail. If a ratio is calculated incorrectly or the terms and conditions are misread, it becomes unclear how decisions were made without traceability to the source data or human approval.
Project sponsors can avoid these risks by introducing checks and balances into AI workflows. Regardless of the task, AI output should always be reviewed by project finance experts. Careful reviews also support higher quality work and encourage teams to engage deeply with the underlying documentation.
Finally, good governance is a critical step to responsibly deploying AI. As technology evolves rapidly, AI adoption may outpace control, increasing risk.
Principles of responsible use
Responsible AI adoption starts with responsible system design. AI tools need to be built in a way that allows users to understand how they work, know when to rely on AI, and clearly monitor their output.
This “human-involved” approach keeps project finance professionals involved at defined points in the AI workflow, ensuring accuracy, safety, accountability, and ethical decision-making. Simply put, responsible AI is built in, not bolted on. Effective use depends on concrete guardrails, privacy controls, and integrity checks that are built into every stage of development, rather than being an afterthought.
A trusted software provider should have an internal AI policy governing review, approval, and escalation procedures. Software should always include features that make the use of AI explicit and manageable, such as creating clear opt-in settings so users always know when AI is being used. Separating automation from predictive AI ensures ease of use and value even when companies have strict AI usage policies.
Finally, data privacy is a major concern, especially in highly regulated industries such as project finance. All AI tools must include strict privacy and control measures for data governance and model training permissions.
These considerations are part of an ongoing conversation at Banyan Infrastructure. We believe responsible AI design requires not only thoughtful internal policies, but also ongoing customer and industry dialogue. We established the AI Advisory Board for this very purpose. It’s about working with project finance leaders on their strategic goals and safety requirements, ensuring our platform minimizes risk while providing the ease of use that project finance teams need.
Dedicated model
There is an ongoing debate about the value of AI tools built by project finance experts versus general-purpose platforms like ChatGPT and Gemini. Common systems can summarize language or extract numbers, but they lack nuance in loan structures, covenants, and compliance triggers that require domain-specific context. That’s why Deloitte recommends fine-tuned vertical models for finance rather than large, one-size-fits-all language models.
Simply put, dedicated models are suitable for project finance because they “speak our language”. They are trained on clean, well-organized transaction data, allowing them to more accurately understand difficult terminology and contract logic, and reduce mistakes in their answers.
Roadmap for implementation
Managing organizational change and the introduction of new technology is no easy task. Here are the strategies successful operations professionals use to get their teams on board and rapidly adopt new and innovative technologies.
- Define your goals. Identify business processes where AI can deliver tangible benefits, such as faster diligence reviews and automated portfolio monitoring.
- Please select a partner: Choose a technology vendor with deep experience in project finance, transparent data practices, and teams leading advances in AI so you don’t have to go it alone.
- Test before scaling. Run pilot programs to get rapid, iterative feedback and reduce risk before expanding into daily operations.
- Scale with governance: Use scales only if they have a fully built-in monitoring process and human review.
AI does not replace expert judgment. Power it by reducing manual effort and improving access to insights. Through disciplined design and rigorous validation, AI can help the project finance sector move faster, smarter, and with more confidence.
Amanda Lee I am the COO of banyan infrastructure.
Guest posts on ImpactAlpha represent the opinions of the poster and do not necessarily reflect the views of ImpactAlpha.
