10 AI Mistakes Alternative Asset Managers Must Avoid

We've all heard it countless times since the rise of AI, "AI is the new electricity." This famous quote by Andrew Ng perfectly sums up AI's transformative power across industries. And alternative asset management is no exception. 

According to a PwC report, AI could contribute up to $15.7 trillion to the global economy by 2030. With numbers like that, can anyone still call it just a trend? Not at all.

Yet, AI adoption in alternative asset management requires careful planning. A misstep can mean flawed investment decisions, compliance issues, and reputational damage.

Why AI Adoption Must Be Strategic:

• Regulatory Compliance Risks: AI-powered decisions must align with financial regulations.
• Data Integrity Issues: Poor-quality data leads to inaccurate investment strategies.
• Over-Reliance on Automation: AI enhances human expertise & it doesn’t replace it.

Recently, an article by Goldman Sachs warned that firms misusing AI could face massive financial and reputational risks. So, is AI a magic bullet or a double-edged sword? The answer depends on how wisely it's implemented.

This blog explores the top 10 AI mistakes alternative asset managers must avoid & and how to prevent them. Read on to ensure AI works for you, not against you!

10 AI Mistakes Alternative Asset Managers Must Avoid

AI promises smarter decisions and higher returns, but one wrong move can turn it into a liability. Many asset managers unknowingly fall into traps that slow them down or put their investments at risk. The difference between success and failure often comes down to avoiding these critical missteps. 

Are you making any of them?

Here are the 10 AI mistakes alternative asset managers must avoid:

1.  Over-Reliance on AI Without Human Oversight

Trusting AI blindly? That’s a disaster waiting to happen. AI can crunch numbers, but it can’t replace human intuition. Over-reliance on AI leads to blind trust in algorithms without questioning their outputs. 

Without human oversight, errors can go unnoticed, leading to costly mistakes. As such, a balanced approach combining AI insights with expert judgment is the key to making smarter investment decisions.

How to Avoid This Mistake:

• Establish AI-human collaboration in decision-making.
• Implement AI model monitoring for anomalies.
• Ensure manual verification of AI-generated insights.
• Conduct periodic AI audits to test reliability.
• Train employees in AI risk management.

2. Poor Data Quality and Inconsistent Data Sources

Messy data leads to messy outcomes - no exceptions. AI models depend on data quality. If the data is inaccurate or inconsistent, AI-driven insights will be flawed. The benefits of data extraction include filtering out errors, standardizing formats, and ensuring reliable inputs for AI analysis. 

Clean, structured data helps alternative asset managers make smarter, data-backed decisions. In AI-driven investing, high-quality data is the difference between success and costly mistakes.

How to Avoid This Mistake:

• Use reliable data providers for investment decisions.
• Implement data validation processes.
• Ensure alternative data integration follows standard protocols.
• Regularly clean and update datasets.
• Invest in data governance frameworks.

3. Ignoring AI Explainability and Transparency

Would you trust an investment strategy you can’t explain? Probably not and neither will your investors. Regulators and investors demand transparency. AI that operates as a "black box" creates trust issues. Without clear insights into how AI makes decisions, risk assessment becomes a guessing game. 

The key is to prioritize AI explainability, ensuring models provide interpretable and auditable insights. Thus, transparent AI builds confidence, helps meet compliance requirements, and ultimately leads to better investment decisions.

How to Avoid This Mistake:

• Choose explainable AI models.
• Maintain AI decision logs for audit trails.
• Align AI adoption with SEC and ESG guidelines.
• Use interpretable machine learning techniques.
• Train stakeholders on AI model accountability.

4. Using Generic AI Models Instead of Tailored Solutions

One-size-fits-all rarely fits well in finance. AI models built for retail trading don’t work for hedge funds. Off-the-shelf AI solutions don’t address unique investment strategies. Thus, generic models lack the customization needed to handle complex alternative assets. 

Tailored AI solutions adapt to specific strategies, risk tolerances, and market conditions. As such, the right AI should enhance decision-making, not force firms into a rigid framework.

How to Avoid This Mistake:

• Develop AI models customized for alternative assets.
• Ensure AI aligns with fund-specific strategies.
• Work with domain experts in AI development.
• Validate AI performance with backtesting.
• Avoid AI solutions that promise one-size-fits-all results.

5. Overlooking AI Model Bias and Ethical Risks

When AI inherits bias, so do your investment decisions. AI learns from past data, meaning it can inherit biases. This creates risks in investment decision-making. In investment management, biased AI models can lead to skewed predictions, unfair risk assessments, and missed opportunities. 

This makes it essential to use diverse, high-quality data and conduct regular bias audits. By doing so, firms can ensure ethical AI that not only meets compliance standards but also provides a competitive edge.

How to Avoid This Mistake:

• Monitor AI for unintended biases.
• Use diverse datasets to train AI models.
• Implement ethical AI guidelines.
• Regularly audit investment decision fairness.
• Follow global best practices in AI ethics.

6. Underestimating Cybersecurity Risks in AI Adoption

AI can be a goldmine for hackers if security is an afterthought. This is because of the fact that AI-driven platforms are prime targets for cyberattacks. Data breaches can expose investment strategies. Moreover, weak security measures put sensitive financial data at risk, leading to regulatory penalties and reputational damage. 

Therefore, robust cybersecurity strategies including encryption, access controls, and continuous monitoring are essential to protect AI-driven investment operations.

How to Avoid This Mistake:

• Implement robust cybersecurity protocols.
• Use end-to-end encryption for AI transactions.
• Regularly update AI software security patches.
• Conduct penetration testing for AI-driven platforms.
• Ensure AI models comply with data protection laws.

7. Neglecting Regulatory Compliance in AI-Driven Decisions

Regulatory blind spots can lead to costly fines. The SEC and other regulators are scrutinizing AI in financial markets. AI must comply with investment regulations. 

Failing to do so can result in legal consequences, loss of investor trust, and operational setbacks. To stay ahead, firms must ensure their AI systems align with evolving compliance requirements and maintain transparency in decision-making.

How to Avoid This Mistake:

• Ensure AI decisions follow SEC and global guidelines.
• Maintain comprehensive AI compliance documentation.
• Conduct regular AI regulatory audits.
• Implement AI explainability for compliance teams.
• Stay updated with changing financial AI regulations

8. Expecting Immediate Returns Without AI Training

Rushing AI won’t get you results, it’ll only get you regrets. Thus, firms that expect overnight success set themselves up for failure. 

Like any investment, AI requires proper training, fine-tuning, and continuous learning to deliver real value. Thus,  patience and strategic implementation are key to discovering AI’s full potential in alternative asset management. 

How to Avoid This Mistake:

• Set realistic AI implementation timelines.
• Train employees on AI best practices.
• Gradually scale AI adoption.
• Monitor AI impact before full deployment.
• Invest in continuous AI learning and development.

9. Failing to Integrate AI Seamlessly into Investment Workflows

What good is AI if it slows you down instead of speeding you up? AI should be a power-up, not a roadblock. AI should enhance workflows, not disrupt them. Thus, poor AI integration leads to inefficiencies. 

Without a seamless fit, teams may struggle with adoption, leading to wasted resources and missed opportunities. The key is aligning AI with existing processes to maximize efficiency and drive smarter investment decisions.

How to Avoid This Mistake:

• Ensure AI aligns with existing workflows.
• Use AI to augment, not replace, investment teams.
• Develop clear AI adoption strategies.
• Involve investment professionals in AI integration.
• Monitor AI’s real-time impact on workflows.

10. Not Continuously Updating and Optimizing AI Models

An outdated AI model is as risky as an outdated investment strategy. Markets evolve, and so should AI models. Thus, failing to update AI reduces its accuracy. 

Without regular optimization, models can produce irrelevant insights, leading to poor decision-making. To stay ahead, firms must continuously refine their AI to adapt to changing market conditions and new data trends.

How to Avoid This Mistake:

• Regularly retrain AI models.
• Monitor AI for performance drift.
• Implement AI-driven market trend analysis.
• Adopt feedback loops for continuous learning.
• Ensure AI models reflect current market conditions.

Splore: Strategic Insights for Modern Asset Managers

Splore is an AI-powered investment analysis platform that simplifies data extraction, portfolio analysis, and alternative data integration. It provides alternative asset managers with real-time insights, reducing manual workload and improving accuracy.

How Splore Helps:

• AI-Driven Data Analysis: Splore identifies trends and investment opportunities using advanced AI algorithms.
• Streamlined Investment Analysis: It speeds up decision-making by providing quick and data-driven insights.
• Enhanced Accuracy and Efficiency: Splore minimizes human errors in data processing, ensuring more reliable results.

Why Asset Managers Trust Splore:

• Reduces Manual Workload: Splore automates tedious data tasks, freeing up time for strategic analysis.
• Provides Real-Time Insights: It delivers up-to-the-minute data, helping fund managers stay ahead of market trends.
• Supports Investment Strategies: Splore ensures investment decisions are backed by accurate, data-driven insights.

Conclusion

AI is reshaping alternative asset management, but missteps can be costly. Thus, avoiding these 10 AI mistakes ensures successful AI adoption. 

This is where Splore comes in offering intelligent, transparent, and efficient AI solutions tailored for investment professionals. It helps firms stay ahead of the curve, making AI-driven decisions more reliable and impactful.

Ready to integrate AI-powered insights into your investment strategy? Book a demo with Splore today!