How to Invest in AI — Part 3 (Due Diligence)

17 min readDec 21, 2022

Image generated with DALL-E-2 using the prompt “an investor looking at a robot using a magnifying glass, generative art.”

This is Part 3 of my series of blog posts on how to invest in AI. In my job at building and leading one of the biggest AI Startup Incubators in Europe, working closely with various founders, and teaching a course on AI for innovation and entrepreneurship at the Technical University of Munich, I screen a lot of pitch decks on AI and am privy to a great deal of AI startups as they come and go. Frequently I am asked by investors and VCs to examine AI startups and help them to make their investment decisions. In this series of posts, I wish to share my investment hypotheses and explain what I look for in evaluating and investing into AI startups.

How to Invest in AI — Part 1
How to Invest in AI — Part 2

Step 5 — Do the Due Diligence

There is no way of avoiding due diligence when it comes to investing in AI startups. It is important to speak with the founding team in order to gain a more comprehensive understanding of the startup’s business model, technology, and growth prospects. Such a discussion can provide valuable insights into the startup’s vision, strategy, and execution capabilities, as well as its approach to risk management and compliance. This can also help investors to identify potential red flags or areas of concern that may impact the long-term viability of the startup. When I speak with teams, and when I have more time than just 30 minutes, I try structure my questions along the machine learning life cycle.

The Machine Learning Lifecycle, appliedAI

Scoping

Product-Market fit

To overcome the challenge of finding the right product market fit, an AI startup may need to take the following steps:

Define the target market: Identify the specific group of customers the startup’s products or services are intended to serve and understand their needs and preferences.
Research the competition: Analyse the competitive landscape and understand how the startup’s products or services compare to those of its competitors.
Test and validate the product or service: Use market research, customer feedback, and pilot tests to validate the value and viability of the startup’s product or service.
Adjust the product or service as needed: Based on the results of testing and validation, make any necessary adjustments to the product or service to improve its fit with the target market.

During the due diligence process, investors may want to ask the founding team the following questions to assess the startup’s ability to find the right product market fit:

Who is the target market for the startup’s products or services, and how is the startup identifying and targeting this market?
What is the competitive landscape, and how does the startup’s product or service differentiate itself from its competitors?
How has the startup validated the value and viability of its product or service through market research and customer feedback?
What steps has the startup taken to adjust its product or service to better meet the needs of its target market?

By answering these questions, the founding team can provide insight into their approach to finding the right product market fit and their ability to adapt and evolve their products or services as needed.

Scalability

To overcome the challenge of scaling, an AI startup may need to take the following steps:

Define the growth strategy: Identify the specific goals and milestones that the startup aims to achieve as it scales and develop a plan to achieve them.
Build a scalable business model: Identify the key drivers of growth and develop a business model that is scalable and can support that growth.
Secure the necessary resources: Identify the resources (e.g., funding, talent, infrastructure) that the startup will need to scale and develop a plan to secure them.
Invest in automation and optimization: Implement automation and optimization technologies and processes to improve efficiency and reduce the workload as the startup scales.

During the due diligence process, investors may want to ask the founding team the following questions to assess the startup’s ability to scale:

What is the startup’s growth strategy, and how does it plan to achieve its scaling goals?
What is the startup’s business model, and how is it designed to support growth?
What resources will the startup need to scale, and how does it plan to secure them?
How is the startup addressing the challenge of scaling with heterogeneous data, and how does it plan to handle and make use of this type of data as it grows?

By answering these questions, the founding team can provide insight into their approach to scaling and their ability to secure the necessary resources and implement automation and optimization technologies as needed.

Long lasting USP

To overcome the challenge of creating a long-lasting unique selling point (USP), an AI startup may need to take the following steps:

Identify the target market: Define the specific group of customers that the startup’s products or services are intended to serve and understand their needs and preferences.
Analyse the competition: Research the competitive landscape and understand how the startup’s products or services compare to those of its competitors.
Develop a unique value proposition: Identify a unique feature or benefit that sets the startup’s products or services apart from its competitors and makes them attractive to customers.
Validate the USP: Use market research, customer feedback, and pilot tests to validate the value and viability of the startup’s USP.
Protect the USP: Implement strategies to protect the startup’s intellectual property and ensure that its unique value proposition is not easily copied or replicated by competitors.

During the due diligence process, investors may want to ask the founding team the following questions to assess the startup’s ability to create a long-lasting USP:

What is the target market for the startup’s products or services, and how is the startup identifying and targeting this market?
What is the competitive landscape, and how does the startup’s product or service differentiate itself from its competitors?
How has the startup validated the value and viability of its unique value proposition through market research and customer feedback?
How is the startup using data to create a long-lasting USP? For example, is the startup using data to deliver personalised or relevant experiences to customers, or is it using data to identify unique patterns or trends that can inform its product or service offerings?

By answering these questions, the founding team can provide insight into their approach to creating a long-lasting USP and the role that data plays in this process.

Data Engineering

Data

When performing due diligence on an AI startup, it is important to understand what kind of data goes into a model and the nature of the output. This can impact the value proposition, scalability, and long-term viability of the startup. Understanding the data inputs and outputs of an AI model can help investors assess the potential risks and opportunities associated with the investment, as well as the reliability and accuracy of the model.

It is also important to make sure that the training data used to build an AI model is the same as the data used in production as this can impact the performance and reliability of the model. If the data used in production differs significantly from the training data, the model may not perform as expected or may produce inaccurate or biased results.

To find out what kind of data goes into a model and what is the output, as well as to assess the quality and consistency of the training and production data, an investor could ask the following questions:

What kind of data does the startup use to train its AI models, and how does it gather and pre-process this data?
What is the output of the startup’s AI models, and how is this output used by the customer or end user?
How does the startup ensure the quality and relevance of the data used to train its AI models, and what measures is it taking to prevent bias or errors in the data?
How does the startup ensure that the training data used to build its AI models is the same as the data used in production, and what measures is it taking to prevent data drift or other discrepancies between the two datasets?

By answering these questions, the founding team can provide insight into the data inputs and outputs of their AI models, as well as their approach to data quality and consistency. This information can be valuable for investors as they consider whether to invest in the startup.

Data Centric AI

One way that AI startups can create a long-lasting unique selling point (USP) is by focusing on data-centric AI. Data-centric AI refers to AI solutions that are built around the collection, analysis, and use of data to improve performance and drive business value. In the case of AI startups, this can involve developing proprietary data sets, building expertise in data management and analysis, or creating AI solutions that rely on data to drive their value proposition.

Focusing on data-centric AI can help AI startups to create a long-lasting USP because:

Data can be a competitive advantage: Access to large amounts of high-quality data is often essential for training and improving AI models. By developing proprietary data sets or building expertise in data management and analysis, AI startups can differentiate themselves from their competitors and create a unique selling point.
Data-driven solutions can be more valuable: AI solutions that are built around data and rely on data to drive their value proposition can be more valuable to customers because they are able to deliver more relevant and personalised experiences. This can help AI startups to create a long-lasting USP.
Data-centric AI can be more resilient to technological change: As mentioned above, the field of AI is rapidly evolving, and what may be considered innovative or unique today may quickly become commonplace or obsolete in the future. By focusing on data-centric AI, startups can create solutions that are less reliant on specific technologies or approaches and are more resilient to technological change.

Creating a long-lasting USP is a critical challenge for AI startups, as this can impact their ability to attract and retain customers, generate revenue, and achieve profitability. By focusing on data-centric AI, startups can create unique and valuable solutions that are able to adapt and evolve over time.

To better understand if an AI startup is using data-centric AI to create a long-lasting unique selling point (USP), an investor could ask the following questions during the due diligence process:

What is the source, type, and quality of the data that the startup is using, and how does it plan to acquire and manage this data over time?
How is the startup using data to create a unique value proposition for its products or services? For example, is the startup using data to deliver personalised or relevant experiences to customers, or is it using data to identify unique patterns or trends that can inform its product or service offerings?
How does the startup’s use of data-centric AI differentiate it from its competitors, and how does it plan to protect this differentiation over time?
How has the startup validated the value and viability of its data-centric AI through market research and customer feedback, and what steps has it taken to adjust its approach as needed?

By answering these questions, the founding team can provide insight into their use of data-centric AI to create a long-lasting USP and their ability to protect and maintain their unique value proposition over time.

Virtuous Cycle of AI

When doing due diligence on an AI startup, it is important to understand how the startup can make use of the virtuous cycle of AI to create value and growth because this can impact the value proposition, scalability, and long-term viability of the startup. The virtuous cycle of AI refers to the feedback loop that occurs when an AI system is used to improve the data that it is trained on, which in turn improves the performance of the system, which in turn leads to further improvements in the data. This cycle can be beneficial because it can help to drive continuous improvement and value creation in an AI system.

To find out how an AI startup can make use of the virtuous cycle of AI to create value, an investor could ask the following questions:

How does the startup’s AI solution make use of the virtuous cycle of AI to drive continuous improvement and value creation?
How does the startup plan to acquire and manage data to support the virtuous cycle of AI, and what measures is it taking to ensure the quality and relevance of this data?
What benefits does the startup expect to derive from the virtuous cycle of AI, and how does it plan to measure and track the impact of this approach?
How does the startup’s use of the virtuous cycle of AI differentiate it from its competitors, and how does it plan to protect this differentiation over time?

How is Data Used to Create Future Value

It can be important to understand how an AI startup uses data to create new use cases in the future because this can impact the value proposition, scalability, and long-term viability of the startup. Identifying new use cases for an AI solution can help to drive growth and value creation for the startup. Understanding how the startup plans to do this can help investors assess the potential risks and opportunities associated with the investment.

To find out how an AI startup uses data to create new use cases in the future, an investor could ask the following questions:

How does the startup use data to identify new use cases for its AI solution, and what methods does it use to gather and analyse data for this purpose?
What steps does the startup take to validate and prioritise new use cases identified through data analysis, and how does it plan to bring these use cases to market?
How does the startup plan to expand its customer base or target market through the development of new use cases, and how does this align with its overall growth strategy?
How does the startup’s use of data to create new use cases differentiate it from its competitors, and how does it plan to protect this differentiation over time?

By answering these questions, the founding team can provide insight into their approach to using data to create new use cases and the benefits and risks associated with this approach.

Modelling

Model Performance

It is important to understand the performance of AI models when doing due diligence on an AI startup because this can impact the value proposition, scalability, and long-term viability of the startup. Understanding the performance of AI models can help investors assess the potential risks and opportunities associated with the investment.

To better understand the performance of AI models, an investor could ask the following questions during the due diligence process:

What metrics does the startup use to measure the performance of its AI models, and how does it track and monitor these metrics over time?
How does the startup ensure the accuracy and reliability of its AI models, and what measures is it taking to prevent bias or errors in the model outputs?
How does the startup plan to handle data drift, which can occur when the data used to train an AI model differs significantly from the data used to deploy the model in production?
What steps does the startup take to continually improve the performance of its AI models, and how does it plan to adapt to changing data patterns or requirements over time?

By answering these questions, the founding team can provide insight into their approach to measuring and improving the performance of their AI models, as well as their ability to handle data drift and adapt to changing data patterns or requirements.

Production & Maintenance

When doing due diligence on an AI startup, it is important to understand how AI is embedded in their solution because this can impact the value proposition, scalability, and long-term viability of the startup. AI can be used in a variety of ways in a startup’s solution. Understanding how AI is being used can help investors assess the potential risks and opportunities associated with the investment.

It is important to understand what are the (training)inputs of the model and what are the outputs

To find out how AI is embedded in an AI startup’s solution, an investor could ask the following question:

How is AI being used in the startup’s solution, and what specific problems or challenges is it addressing?
How is AI embedded in the startups infrastructure?

By asking this question, the investor can gain a better understanding of the role that AI plays in the startup’s solution and how it is being used to solve specific problems or challenges. This information can be valuable for assessing the potential value proposition and scalability of the solution, as well as the expertise and capabilities of the founding team. It can also help the investor to identify potential risks or limitations associated with the use of AI in the solution.

Risks

When performing due diligence on an AI startup, it is important to understand the risks of using AI and how the startup might be affected by regulation such as the AI Act. Such risks and regulations may impact the long-term viability and growth potential of the startup. Risks associated with using AI may include technical challenges, data privacy and security concerns, ethical and societal issues, and regulatory compliance. Understanding how these risks are being addressed by the startup can help investors assess the potential risks and opportunities associated with the investment.

To better understand the risks of using AI and how the startup is addressing them, an investor should ask the following questions:

What are the specific risks associated with using AI in the startup’s solution, and how is the startup addressing these risks?
How is the startup complying with relevant regulations and standards related to AI, such as the AI Act, and what impact might these regulations have on the startup’s operations and growth potential?
How is the startup managing data privacy and security risks associated with using AI, and what measures is it taking to protect customer data and ensure compliance with relevant laws and regulations?
What ethical and societal issues may arise in the startup’s use of AI, and how is the startup addressing these issues?

By answering these questions, the founding team can provide insight into their understanding of the risks of using AI and their approach to managing and mitigating these risks.

Human-in-the-loop (HITL)

When doing due diligence on an AI startup, it is important to understand if the startup is using a “human-in-the-loop” approach to create value and mitigate risks as this can impact the value proposition, scalability, and long-term viability of the startup. A human-in-the-loop approach involves the integration of human expertise and oversight into the development and deployment of AI solutions. This can be beneficial because it can help to improve the accuracy, reliability, and transparency of AI systems, as well as address ethical and societal concerns.

To find out if an AI startup is using a human-in-the-loop approach, an investor could ask the following questions:

How is the startup integrating human expertise and oversight into the development and deployment of its AI solutions?
What benefits does the startup expect to derive from using a human in the loop approach, and how does it plan to measure and track the impact of this approach?
How does the startup plan to address ethical and societal concerns related to the use of AI, and what measures is it taking to ensure the transparency and accountability of its AI systems?
How does the startup’s use of a human-in-the-loop approach differentiate it from its competitors, and how does it plan to protect this differentiation over time?

By answering these questions, the founding team can provide insight into their use of a human-in-the-loop approach and the benefits and risks associated with this approach.

Look out for Potential Red Flags along the Due Diligence

There are several potential red flags that investors should be aware of when it comes to investing in AI startups. Some of these red flags include:

Lack of transparency: If the startup is not forthcoming about how it is using AI or is unable to clearly explain the details of its AI solution.
Lack of expertise: If the founding team lacks relevant AI expertise or has limited experience in the industry.
Inaccurate or unrealistic claims: If the startup makes overly optimistic or unrealistic claims about the capabilities of its AI solution.
Unclear business model: If the startup does not have a clear plan for how it will monetize its AI solution or generate revenue.
Poor data management practices: If the startup has poor data management practices or is not taking steps to protect customer data.

To find red flags when investing in an AI startup, an investor should ask the following questions:

Can the startup clearly explain the details of its AI solution and how it is using AI to solve specific problems or challenges?
Does the founding team have relevant AI expertise and experience in the industry, and are they able to demonstrate their capabilities and track record?
Are the claims made by the startup about the capabilities of its AI solution accurate and realistic, and can they be supported by data or evidence?
Does the startup have a clear plan for monetizing its AI solution or generating revenue, and how does this plan align with its target market and competitive landscape?
How is the startup managing and protecting customer data, and what measures is it taking to ensure compliance with relevant laws and regulations related to data privacy and security?

By answering these questions, the founding team can provide insight into their capabilities, business model, and data management practices, which can help investors identify potential red flags and assess the risks and opportunities associated with the investment.

Step 6 — Using outside expertise

It is important to utilise outside expertise when performing due diligence on AI startups, for several reasons. For example, if the startup is using cutting-edge AI technology or AI hardware, it would be prudent to bring in outside experts to navigate the potential risks and opportunities associated with such technologies. This is because these technologies may be complex or unfamiliar to the investor; outside experts can provide valuable insights and perspective on their potential impact and adoption. Such experts may also help to identify potential risks or opportunities related to these developments.

There are several sources of outside expertise that investors may tap into when performing due diligence on AI startups. These sources include:

Industry experts: Industry experts can provide valuable insights into the specific AI technologies or hardware being used by the startup, as well as the broader market trends and challenges that may impact the startup’s growth potential.
AI researchers: AI researchers can provide valuable insights into the technical capabilities and limitations of the AI technologies or hardware being used by the startup, as well as the potential risks and opportunities associated with these technologies.
Legal experts: Legal experts can provide valuable guidance on the regulatory landscape surrounding AI technologies and how such contexts may impact the startup’s operations and growth potential.
Consultants: Consultants with expertise in AI or related fields can provide valuable guidance on the strategic, technical, and operational aspects of the startup’s AI solution.

Disclaimer: I have written this article using my own experience and know-how regarding how to approach the evaluation and due diligence of an AI startup. To accelerate the writing I used the help of ChatGPT. However, I manually added examples, interviews, images, and references; I tweaked the inputs and outputs, rewrote, and added paragraphs. Using ChatGPT allowed me to write this series of posts in a couple of days — instead of weeks.