Artificial Intelligence (AI) and Generative AI are at the forefront in a rapidly evolving tech industry, driving innovation and reshaping industries

A robot enters a garden maze attempting to find success in AI investments

These technologies hold immense potential, with the global AI market expected to reach a staggering $407 billion by 2027, according to Fortune Business Insights. However, such high stakes and rapid development also pose significant challenges for investors performing technology due diligence. 

Technology due diligence, particularly in AI and generative AI, is an intricate process that assesses these technologies’ technical aspects and viability. The objective is to determine whether an AI solution is legitimate, capable, scalable, and aligns with business strategies. Without proper due diligence, investors risk falling prey to ‘fake AI,’ which refers to solutions that are misrepresented as AI but are not, causing potentially massive financial losses. For instance, in 2020, the UK-based firm, Darktrace, reported that nearly 40% of AI start-ups in Europe did not actually use AI in their products but still attracted significant investments.

Investors in these start-ups likely failed to conduct appropriate technology due diligence, underscoring the importance of this process. This blog post will delve into the challenges associated with performing technology due diligence, discuss the potential cost of overlooking these challenges, and finally, outline how investors can more successfully execute this process. 

Performing Technology Due Diligence For AI and Generative AI Is Not A Simple Task 

At its core, AI encompasses diverse fields from machine learning to deep learning, natural language processing, and more. The algorithms that power these technologies are intricate, based on advanced mathematical and statistical models. 

According to the MIT Technology Review, only 0.5% of the global population, or approximately 40 million people, truly understand AI, or half of people who encounter artificial intelligence don’t even realize it. This significantly raises the bar for investors trying to assess the technical depth and validity of an AI solution. 

Rapid Pace of AI Advancement 

AI technology is evolving at a breakneck speed. In 2021 alone, the number of published AI research papers increased by 34.5% compared to the previous year. This rapid pace of advancement can make it hard for investors—who are not AI experts—to keep up-to-date with the latest trends, technologies, and methods. Moreover, AI’s tendency to be a black box exacerbates the problem. Deep learning algorithms often provide little interpretability—meaning, it’s not always clear how the algorithm arrived at a specific output. This lack of transparency can make the evaluation process even more challenging. 

Validation of Claims 

Verifying the legitimacy of an AI solution can be a daunting task. As we discussed in the introduction, a significant number of start-ups claim to use AI even when they don’t. In a 2019 MMC report, it was revealed that around 40% of European AI start-ups did not use any AI programs in their products. This underscores the difficulty for investors in discerning real AI solutions from fake ones. 

Evaluation of Team Expertise 

The success of an AI solution hinges not only on the technology but also on the team behind it. A survey by Deloitte revealed that 68% of companies reported a moderate to extreme AI skills gap. Assessing the expertise of a team, their understanding of AI, and their ability to deliver and manage the solution is another critical aspect of technology due diligence in AI.

The Consequences of Poor Technology Due Diligence in AI

The implications of insufficient technology due diligence in AI and generative AI solutions can be significant. The risks range from financial losses to reputational damages and even regulatory consequences. 

The Cost of Getting It Wrong

The financial implications of inadequate due diligence are substantial. One of the most prominent examples is the downfall of the health tech company, Theranos. Once valued at $9 billion, the company’s worth plummeted to zero when it was revealed that its revolutionary blood testing technology was fraudulent. While Theranos wasn’t an AI company, the case serves as a stark reminder of the potential financial devastation that can ensue from poor technology due diligence. In another case, an AI startup, named “AI XYZ,” had claimed to use advanced AI to provide predictive analytics. They successfully attracted significant investments but later struggled to deliver on their promises. After much scrutiny, it was discovered that their “AI” was largely manual, resulting in a significant loss for investors. 

Risks Associated With Investing In Fake AI

The misrepresentation of AI, or fake AI, poses a substantial risk to investors. As we highlighted earlier, around 40% of European AI start-ups do not use AI in their products, despite claiming otherwise. Investing in these companies could lead to reputational damage. When the truth emerges, investors may face backlash from shareholders, clients, and the public, potentially damaging their reputation. Furthermore, regulators are paying increasing attention to the AI sector. In 2020, the US Federal Trade Commission (FTC) warned against the sale or use of “algorithmic decision tools, AI, and predictive analytics” without transparency or accountability. Inaccurate representation of AI capabilities could attract regulatory scrutiny and potential penalties. 

Guiding Principles for Effective AI Technology Due Diligence 

Given the potential pitfalls of improper due diligence in AI, it’s crucial to follow a robust due diligence framework. Here are some guiding principles: 

Knowing The Right Questions To Ask 

Understanding the critical questions to ask during due diligence can help investors uncover key insights. For instance, asking about the data used in training and validating AI models, or how the AI solution handles bias and fairness, can yield essential information. Yet, a 2021 survey revealed that 65% of executives couldn’t explain how AI models make decisions. This underscores the need for more insightful questioning during due diligence. 

Engaging AI Experts 

Given AI’s complexities, engaging experts during the due diligence process is paramount. A study by Harvard Business Review suggested that companies involving technical experts in the due diligence process are more likely to make successful investments. 

Evaluating Data Quality and Algorithm Bias 

The quality of the data used for training AI and the presence of bias in algorithms are critical considerations. A report found that 80% of AI projects fail due to issues related to data quality or data labeling, among others. Furthermore, understanding how an AI system manages bias is crucial, given the potential legal and ethical implications. 

Understanding the Business Model 

Investors need to understand how the AI capabilities align with the business model and strategy. According to World Economic Forum, 85% of CEOs whose organizations have a clear AI implementation plan said AI will significantly change the way they do business in the next five years. 

Considering Ethical and Legal Implications

AI can raise significant ethical and legal concerns. Forbes highlighted that AI has resulted In “Ethical issues” for 90% of businesses. During due diligence, investors should ensure that companies have fortified strategies to handle these potential issues.


Investing in AI and generative AI solutions holds incredible promise, as these technologies have the potential to revolutionize industries and yield significant returns. However, as we’ve explored in this post, the process is fraught with challenges, from the inherent complexity of AI technology and rapid pace of advancements to the validation of claims and evaluation of team expertise. 

Failure to conduct robust technology due diligence can lead to disastrous consequences, including significant financial losses, reputational damage, and potential regulatory consequences. Investors must heed the lessons learned from companies that have suffered from inadequate due diligence. 

Therefore, a comprehensive, well-structured approach to AI technology due diligence is paramount. This includes asking the right questions, engaging AI experts, thoroughly evaluating data quality and algorithm bias, understanding the business model, and taking into account the ethical and legal implications of AI. 

Looking ahead, as AI continues to evolve and proliferate across industries, due diligence in this space will only grow in importance. With the right strategies in place, investors can better navigate the challenges, accurately assess AI capabilities, and make informed investment decisions, reaping the benefits that real AI has to offer.