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AI Due Diligence: Exploring Essential Categories for Successful Implementation

AI Due Diligence

Artificial Intelligence (AI) has always been hot property. And since ChatGPT was launched, AI has demonstrated the potential for increased efficiency, productivity, and cost savings.

However, implementing AI solutions has inherent risks, including potential biases, data quality issues, and security vulnerabilities. So if you’re investing in it, you must ensure you’re not liable for future issues.

This is why conducting comprehensive AI due diligence is essential before investment or buyout.

AI due diligence is the process of evaluating the technical, legal, and ethical risks associated with AI implementation. This process involves assessing the technical capabilities and limitations of the AI solution. Analyzing the quality of data used by the AI model, and identifying any potential ethical or legal issues.

In this blog, we will explore the different categories that investors and acquirersshould explore when investing in AI firms.

Data quality and bias

Data quality and bias are critical considerations in any AI implementation. AI models rely on high-quality data free from bias to deliver accurate and reliable results. Therefore, evaluating the company’s approach to data quality and bias is essential during AI due diligence.

This includes assessing their data collection processes, data labelling processes, and data augmentation techniques. You should also evaluate their approach to detecting and mitigating bias in their data and evaluating Explainability and interpretability.

AI models can be complex and difficult to understand, making explainability and interpretability critical considerations during AI due diligence. Explaining how an AI model works and why it produces specific results is essential for building trust in the model.

You should evaluate the company’s approach to explainability and interpretability. Including its model architecture, feature selection approach, and methods for explaining model decisions. This includes assessing their approach to model validation and testing and evaluating their methods for tracking and reporting model performance.

Robustness and security

AI models can be vulnerable to attack, making robustness and security critical considerations during AI due diligence.

Ensuring the AI model’s robustness and security is essential to protect against vulnerabilities and ensure the model performs as intended. You should evaluate the company’s approach to model robustness and security. Including its model testing and validation processes and its approach to model monitoring and maintenance. Don’t forget ongoing security and vulnerability assessments.

This includes assessing their security policies and procedures. Evaluating their approach to vulnerability management, and assessing the risks associated with model vulnerabilities.

Legal and regulatory compliance

Legal and regulatory compliance is an essential consideration for AI due diligence. AI models can be subject to a range of laws and regulations, including data protection, privacy, and discrimination laws. You should evaluate the company’s legal and regulatory compliance approach, including its adherence to relevant GDPR, HIPAA, or PCI-DSS regulations. This includes assessing compliance processes and procedures, any compliance violations or risks, and their commitment to ongoing compliance management.

Ethical considerations

AI models can significantly impact society, making ethical considerations essential during AI due diligence. You should evaluate the company’s approach to ethical considerations. Including their approach to data privacy, algorithmic bias, and ethical considerations around emerging technologies such as facial recognition and automation. This includes assessing their approach to ethical risk management, evaluating their ethical metrics, and assessing the impact of their AI solutions on society.

AI Due Diligence – Intellectual property

Intellectual property is a valuable asset for AI companies. It’s essential to evaluate the company’s approach to intellectual property protection and management during AI due diligence. This includes examining their patent portfolio, trademarks, copyrights, and trade secrets and assessing their processes for managing intellectual property assets and protecting them from infringement.

AI Due Diligence – Team expertise

AI models require a skilled team of professionals to build, maintain, and improve the models’ performance. Therefore, evaluating the company’s team expertise during AI due diligence is essential. This includes assessing their team’s technical capabilities, evaluating their experience in developing AI solutions, and assessing their approach to team training and development.

AI Due Diligence – Scalability

AI models must be scalable to accommodate growth and changes within the organization. Therefore, it’s essential to evaluate the company’s approach to scalability during AI due diligence. This includes assessing their approach to model architecture, evaluating their use of cloud computing services, and assessing their ability to manage large-scale data sets.


In conclusion, conducting comprehensive AI due diligence is essential for mitigating the risks associated with AI implementation and ensuring the model delivers reliable and accurate results. By exploring these categories during AI due diligence, investors, acquirers, and other stakeholders can gain a more in-depth understanding of the AI solution’s capabilities, limitations, and potential impact on the business.

Hutton Henry
Hutton Henry
Hutton has worked with Private Equity Portfolio firms and Private Equity funds since 2015. Having previously worked in post-merger integration for large firms such as Ford and HP, Hutton understands the value of finding issues prior to M&A deals. He is currently the founder of Beyond M&A and provides technology due diligence for VC, PE and corporate investors, so they understand their technology risks before entering into a deal.

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