Product Management
AI en Tech due diligence: Wat bedrijven en investeerders moeten weten
April 15, 2025
10 minuten leestijd
Agu is a Co-Founder and Partner at Intium Tech, a tech advisory firm specializing in helping large companies and private equity funds buy and sell tech businesses. Over more than 20 years of his professional journey, he has accumulated experience in such spheres as development, architecture, and executive leadership. All this helped him to get a good understanding of how the tech world works. Seven years ago, he transitioned into consulting, helping businesses with acquisitions, carve-outs, and value creation.
Every episode of the Innovantage podcast offers a new perspective on different business aspects and the role of technologies in them. This time, Max Golikov, the podcast host and the CBDO at Sigli, invited Agu Aarna to talk about tech due diligence and the impact of AI on the investment landscape. Agu is a Co-Founder and Partner at Intium Tech, a tech advisory firm specializing in helping large companies and private equity funds buy and sell tech businesses. Over more than 20 years of his professional journey, he has accumulated experience in such spheres as development, architecture, and executive leadership. All this helped him to get a good understanding of how the tech world works. Seven years ago, he transitioned into consulting, helping businesses with acquisitions, carve-outs, and value creation. In 2021, he co-founded Intium Tech. With Intium, Agu and his team wanted to create a standardized approach to assessing technology, similar to what exists in other sectors. They recognized the need to describe technology in a clear, structured way for investors and business leaders. As they developed their system, they realized it could be integrated into software. This led to the creation of their own platform, which enables more efficient analysis of acquisition targets. How technology affects business In his dialogue with Max, Agu emphasized the complexity of technology’s impact on business. A minor technical detail can have significant business implications. That’s why assessing its true effect is crucial. Blindly following best practices is not the best approach. The focus should be on understanding their relevance to a company’s goals. For example, if a company doesn’t run unit tests, it’s not just about missing a best practice. First of all, it should raise questions about the quality of its solutions, leadership, and overall strategy. It’s necessary to find out why it is so. According to Agu, the key lies in finding a balance and understanding both the business’s ambitions and how technology can support them. This dynamic relationship between business goals and technology is what he finds most important. Challenges in tech due diligence Tech due diligence (TDD), which is one of the core aspects that Agu’s firm is focused on, is a detailed examination of a company’s technology infrastructure, products, and processes, typically conducted before a merger, acquisition, or investment. As Agu highlighted, the approach to such analysis has evolved significantly over the years. In the 2000s, it was viewed as a “nice to have” process. It presupposed that a couple of tech experts would assess a company’s technology, often resulting in a laundry list of issues based on their own expertise. This approach lacked a comprehensive view of the business impact. By the 2010s, tech due diligence had become more professional. It already could offer a broader perspective on leadership, architecture, and infrastructure. However, the analysis still lacked a focus on the actual business impact of these issues. In the 2020s, the focus shifted to understanding the business impact of technology and analyzing companies from this perspective. However, inconsistencies in reporting remained a challenge. Different experts can emphasize different aspects, which leads to varying results. Such an issue highlighted the need for a standardized approach. How to make TDD more efficient today Agu believes that to solve this, the industry needs more consistent, high-quality analyses. This could be achieved by leveraging software instead of relying on people-driven processes. This shift toward software-powered solutions, like the one developed by Intium, aims to provide a more scalable and smooth approach to tech due diligence. When discussing tech due diligence, Agu also highlighted two key aspects to focus on. First, it’s crucial to educate clients that tech due diligence is more than just a code or architecture review. Technology is the engine that powers a company, but just like a car, it needs to be steered in the right direction. Evaluating technology requires understanding its context within the business, not just identifying flaws in infrastructure or architecture. Equally important are the people managing the technology and the processes that connect them. Inefficiencies here can quickly undermine technical strength. The second key aspect is taking a comprehensive 360-degree view of the company. Concentrating on only one part of the technology or business won’t provide the full picture. Without this broader perspective, risks and crucial elements to make the deal successful might be overlooked. Moreover, Agu identifies several key risks in tech due diligence that can lead to failed deals: One major risk is when technology is presented as a core asset but doesn’t live up to expectations. Another risk is technical debt and architecture. If the debt requires too much effort to manage or fix, it can cause a deal to fall apart. A third risk is insufficient preparation for the sales process by the target company. When private equity firms are considering mature companies, a lack of proper preparation can reveal too many unknowns, making the deal seem too risky. A well-conducted TDD not only helps determine whether to buy this or that company but also provides information to negotiate the price, impose conditions in the purchase agreement, and even structure earnout plans. Key factors investors should pay attention to It is believed that when you are investing in tech businesses, technology always remains the key factor to evaluate. However, this is not always true. Agu explained that in early-stage investments like seed, pre-seed, and Series A or B, technology is often secondary (as at such stages there is hardly any tech at all). What investors are looking at are the ideas and leadership. Investors should focus on exploring whether the leadership team understands the technology they are working with. Here, the key task is assessing the leadership’s technical acumen to ensure they can build and execute on their vision. As companies move into the growth phase, product-market fit is already established. It means that technology becomes crucial. Scaling the technology to support growth is a different challenge from proving a market problem. This makes tech due diligence more important at this stage. In private equity, where mature companies are involved, technology is already a significant factor. Agu stressed the importance of being transparent and truthful when communicating with investors. If a company misrepresents its technology or misleads investors, it can result in the collapse of the entire deal. AI wrapper companies: Good or bad? While talking about tech innovations, Max mentioned the growing number of so-called AI wrapper companies. They build user-friendly interfaces or apps on top of existing AI technologies, often providing a simpler or more tailored experience for end-users. Instead of developing their own AI models or deep technologies, these companies focus on wrapping AI capabilities into practical solutions. They interact directly with users and often become "sticky" due to people’s habits. Agu believes there is nothing wrong with establishing a wrapper company. In fact, being a wrapper company can be even more important than being a deep tech innovator like OpenAI. He pointed out that AI wrapper companies need to work in some specialized areas like prompt engineering, which may not require deep tech knowledge but still involve particular skills. These companies must know how to effectively augment prompts and optimize user interaction. He also noted that developing and hosting AI can be expensive, adding another layer of complexity for companies in this space. According to Agu, building your own AI is not impossible. However, convincing investors that the team has the expertise to do it is challenging as AI can be very technical. When evaluating an AI company, it is crucial to determine if AI is truly the right tool for the indicated problem. For example, traditional mathematical or statistical models may work as well as AI in some cases, and using AI unnecessarily could signal a lack of understanding of the problem. However, in competitive markets, simply being a wrapper around AI isn't enough. Teams behind such projects must specialize in and understand how AI works. This is also necessary to choose whether they will use off-the-shelf solutions or develop their own models. Privacy is another major concern, particularly in regions like Europe, where data protection is strict. In some cases, companies opt to develop their own AI in order to avoid privacy issues with third-party systems. Impact of AI regulation and privacy laws AI regulation and privacy laws, such as GDPR, have sparked significant debate. Nevertheless, over time, they have proven to be pretty manageable and even beneficial. For instance, GDPR served as a template for other laws like the CCPA in California and the UK’s data protection frameworks. These regulations were initially seen as hurdles but now they are generally accepted as necessary for privacy protection. There is a concern that regulation can stifle innovation. This can happen not necessarily due to any created barriers, but due to the lack of input from business and tech representatives during the drafting process. A more collaborative approach that includes industry experts can make the regulations much more balanced and practical. Regulations are important for protecting personal data. It is crucial to remember that not all market players have good intentions. Without regulation, the misuse of personal data, especially in AI training, could lead to manipulation on a massive scale. Proper regulation ensures that the technology benefits society without being exploited. Policies serve as a tool to raise awareness and guide behavior. They are like a friendly reminder to look both ways before crossing the street, providing useful information that helps keep us safe. When viewed in this light, regulations aren’t obstacles but safeguards that help us navigate potential risks. As AI and technology continue to connect us more deeply, establishing ground rules becomes essential. These rules will help define what data can be used and under what circumstances, ensuring that people are not overwhelmed by the complexities of these technologies. With proper guidelines, people can better understand and trust the systems in place. This clarity is vital for preventing confusion and misuse as the tech landscape evolves. Future of AI for investors These days, there are a lot of talks about the role of AI in different industries and domains. That’s why Max couldn’t help but ask Agu to share his vision of the role of AI in tech due diligence. AI is already being used by investors, particularly in early-stage analysis. Today investment firms leverage AI to gather data on potential companies, analyze it, and automate certain tasks. For example, AI can notify investors when a company becomes more lucrative to drive further investigation. Investors can also use advanced tools like ChatGPT to ask AI for advice about companies. AI plays a significant role in the early stages of investing, and its use extends to later stages and new purposes. However, relying entirely on artificial intelligence without expertise can be risky. If you input a company’s documents into AI, like OpenAI’s ChatGPT, and ask for a summary of the top issues, the technology may provide a polished response that seems accurate but could be misleading. This is because AI sometimes hallucinates and fills in gaps with logical but incorrect information, leading to wrong conclusions. This can be especially problematic for non-experts who might be misled by the polished language. AI is particularly useful in summarizing large amounts of data. But it should always serve as a tool to support expert analysis, not replace it. The key is using AI’s output as an input to the expert’s thinking while controlling that AI doesn’t miss important details. This approach allows for more accurate and reliable results. AI has made significant progress in assisting with due diligence. However, it is still not at the point where it can fully conduct the process on its own. Connecting AI findings to the investment thesis and business impact remains a significant challenge. While AI can provide valuable insights, human expertise is required to make sense of AI-generated data in a meaningful way. In the future, AI may gradually take over more tasks, with humans focusing on areas where AI struggles. However, a key challenge will be ensuring that AI systems continue to evolve. They need constant feedback to stay updated with new information, trends, and market shifts. Without this ongoing learning, AI may become outdated and far less helpful. Investment opportunities and trends in the tech market While talking about the current investment opportunities, Agu noted that in recent years, many specialized startups have emerged. What makes them successful is their focus on niche products that effectively solve specific market problems. According to Agu, today a lot of private equity accounts are sitting on a significant amount of dry powder, which means that there is capital ready for immediate investment. This situation suggests that a period of consolidation may be on the horizon, where smaller companies are acquired and merged into larger corporations. This trend is likely to create opportunities for venture capital and growth equity investors who have supported these niche companies. In particular, AI wrapper companies, if they solve a real problem and maintain strong customer relationships, are well-positioned in this environment. In conclusion, Agu agreed with the common opinion that AI is here to stay. It is expected that this domain will become increasingly efficient over time. We will likely see the emergence of more advanced AI use cases and implementations. However, all of these AI systems will still require resources to operate. Therefore, anything that powers AI is likely to remain essential moving forward, which is a quite expected trend. And if you want to learn more about the current and future trends in the business world, the Innovantage podcast is exactly what you need. The next episodes will be available soon (moreover, don’t forget to verify whether you haven’t missed the previous ones)!
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