Building a Culture of AI: 7 Guardrails for Success

The adoption of artificial intelligence is accelerating across the financial sector as firms utilize AI for nearly everything including fraud detection, investor relations, and investment recommendations. According to Forbes, AI is expected to reach an “expert level” in twelve months.

While AI holds tremendous promise for our industry, effectively integrating it into a firm’s operations requires strong guardrails to protect ethical, safety, and corporate concerns. Today, we’re covering seven guidelines hedge fund leaders can follow to use AI optimally and responsibly.

If you’re currently using AI technology or are considering the impacts it could have on your fund, read on to learn the essential guardrails you should put in place to ensure success.

1. Lead with Ethics

AI ethics must start at the top. Leaders should issue clear guidelines on ethical AI development, from eliminating biases in data and models to ensuring transparency in AI decision making. Issue a formal ethics policy for AI development that aligns with your company’s values and addresses risks such as unfair bias, transparency, and integrity in data/models. Make ethical AI a regular discussion topic at leadership and board meetings to assess each AI project for potential biases and safety risks before launch.

When embarking on new projects, make ethics central to all AI conversations. Organizations can integrate ethics reviews for all AI projects and flag potential issues. Leadership should always empower and encourage employees to call out unethical uses of data or AI as well as protect whistleblowers to establish a culture of trust and responsibility.

Because models operate independently, AI requires ongoing governance to ensure accountability and minimize risks. Assign employees the task or role of monitoring AI issues such as data changes and accuracy decay while establishing response protocols, documenting model versions and changes, and developing reporting mechanisms and unfair outcomes. For example, an asset manager might appoint an employee to a Head of AI Governance role so the employee can continuously audit algorithms and data post-deployment.

With proper governance, financial institutions can rapidly iterate their AI responsibly while ensuring quality control.

2. Establish Governance

Because models operate independently, AI requires ongoing governance to ensure accountability and minimize risks. Assign employees the task or role of monitoring AI issues such as data changes and accuracy decay while establishing response protocols, documenting model versions and changes, and developing reporting mechanisms and unfair outcomes. For example, an asset manager might appoint an employee to a Head of AI Governance role so the employee can continuously audit algorithms and data post-deployment. 

With proper governance, financial institutions can rapidly iterate their AI responsibly while ensuring quality control. 

3. Prioritize Hybrid Teams

The most robust AI requires both technical and domain expertise, so team members should develop T-shaped skills spanning both technology and finance. Build multidisciplinary teams of both data scientists and finance veterans who understand industry nuances and challenges in using new technology. A trading firm, for example, might consider pairing data scientists with veteran traders to build AI for trade execution. Traders would ensure the technology follows proven effective strategies. Meanwhile, both traders and scientists would collaborate through activities such as co-location, pairing, and cross-training to ensure the AI tackled relevant challenges and provided the business value.

4. Invest in AI Literacy

Organizations adopting AI across all operations must provide employees with basic AI literacy. Developers need to know how AI enhances creativity, while advisors need to know how it augments client service. Sponsor training on AI basics, use cases, and limitations to equip all team members to work with AI solutions. Foster a learning culture as AI evolves. For example, a bank might send its finance managers to an AI bootcamp covering topics such as machine learning interpretability, NLP, and computer vision.

Providing accessible, role-based training and continuous learning opportunities helps all employees embrace AI.

5. Customize Employee Engagement

Employees are more likely to embrace AI when they understand the benefits. Help them understand the benefits as soon as possible by involving them in solution design early on and finding solutions to meet their precise needs. Messaging about AI initiatives should also highlight benefits specific to each department’s work to show the tangible value of the new technology. Within that messaging, announcing new solutions in terms of specific end-user impacts rather than technical details will resonate more with your employees. A firm might demonstrate to asset managers, for example, how an AI forecasting tool can provide more robust inputs for portfolio optimization strategies. They might also show developers how AI augments creativity, analysts how it drives insights, and advisors how it serves clients better.

6. Align to Corporate Goals

For the greatest business impact, AI projects should clearly map back to business objectives — whether those objectives include cutting costs, improving client service, catching fraud, acquiring insights, or other defined goals. Leaders should prioritize solutions driving multi-year strategic roadmaps over narrow use cases. For example, an AI project focused on client retention should tie directly to a wealth management firm’s three-year plan to boost retention rates by 15%. Evaluate initiatives for ROI potential and strategic alignment, not just technical novelty. Firms should always focus on how AI delivers tangible business value.

7. Iterate Responsibly

AI models require continuous feedback cycles and improvements to fix flaws, incorporate new data, and remain relevant within an organization. But organizations must subject models to rigorous validation, testing, and staging first to avoid any risks or negative impacts. Teams should monitor the technology for degradation and drift over time as conditions change. For example, a bank may retrain credit risk models on a strict 90-day cycle but freeze deployment if testing uncovers accuracy drops or discrimination. Responsible iteration balanced with proper controls unlocks AI’s potential.

The Bottom Line

Using AI with strong guardrails allows financial firms to harness its capabilities while proactively managing risks. Leaders play a pivotal role in shaping organizational culture around AI. By sharing the promise of AI with their team, prioritizing ethics and expertise, and implementing robust governance, firms can fully and responsibly optimize their use of AI. The future success of AI in finance depends on establishing these cultural foundations today.

Ready to supercharge your operations strategy? Our expert consultants, including data scientists, specialize in tailoring solutions to the needs of hedge funds and family offices. Book a discovery call to learn how our services can help you unlock your organization’s full potential.