Dear Advisor: How do I prepare for AI Technical Due Diligence?

9 Focus Areas to Help Start-Ups Prepare for AI Technical Due Diligence

Originally published in July, updated in August -- November, December 2023

Congratulations! You signed (or are about to sign!) the term sheet and are now told to prepare for technical due diligence focused on AI. Sigh. To help you be more prepared to fundraise, close the round faster, and stand out from the crowd, I’m here to share what I look for during this process.  

Goals for Technical Due Diligence focused on AI

Before we dive into its potential scope, the goals of due diligence are:

Spoiler alert: The goal is not to evaluate AI per se, but to see how it ties into your product, to bring your customers value – and help you scale. It's less about the specific algorithm -- and more about how you're thinking about the algorithm(s).

As you may know, most technical due diligence, until recently, didn’t seem to focus too much, if at all, on the start-ups’ focus on AI. And because it seems that now every start-up is either thinking about or already trying to incorporate "AI" into their products, VCs are now adding AI into the scope of technical due diligence, to help them evaluate if it’s a potential moat or a detractor for the start-up. and may need support in figuring out what's hype and what's real. 

When I help VCs with technical due diligence focused on AI (why me?), I typically wear the customer hat (e.g., does this solve my pain point and how?), and the MLE hat (e.g., if I had to develop this myself, what do I need to watch out for?). Because the code we write today is tomorrow’s legacy code, I focus my assessment on progress toward the solution and milestones you’re pitching, not perfection.  

As a result, there are about the key areas I’ll typically discuss with you. Because every start-up, product offering, and technical implementation is different, the 5 focus areas are not an exhaustive list, or a list of required questions, but a jumping-off point for additional things to discuss as they come up. Having said that, it should be enough to help you prepare to put your best foot forward during technical due diligence focused on AI. 

#1 Revenue Risk

Is the proposed revenue model actionable? That is,

#2 How is AI Core to Product?

How is your company becoming more efficient and scalable while -- at the same time -- bringing your customers more value, with the help of AI? e.g., How is AI a means-to-an-end?

#3 Show, Don’t Tell

You’ve pitched and discussed how you solve your customers’ pain point(s) end-to-end – and said that you have a working product.

Please demo your solution to your customers’ problem(s)! Walk me through it start-to-finish: from getting started (including any on-boarding, if it exists), to reaching your customers’ end goal, whether that’s helping with a diagnosis – or making a real-time recommendation. 

Please ensure your audience can follow along -- and we can see themselves using it or get excited about your potential customers using it. Consider UI over Terminal demos whenever possible.

Screenshots are not Demos

I see demos that are app screenshots. I’d like to know what’s actually developed and what works, which may not always be the same thing…

If the product makes real-time recommendations, I’d love to see it live! I may ask to see how the product responds to specific, typical customer behavior in the product. I’d love to see what still works and what errors out or silently fails, to help understand your knowledge of your customers and how robust the technical implementation is.

Sample Demos

If you’ve introduced your audience to a specific solution(s) to your customers’ problem(s) in prior meetings, I recommend starting with a demo that touches on that, and we’ll dive in from there. 

If you haven’t shared specifics in prior meetings, consider adding it to your pitch deck! I may also ask you to walk me through how to solve a common request your customers may have based on what you’ve pitched previously. 

#4 Glaring Data Bias

Depending on what you’re trying to predict – and how, if there’s a fundamental bias in your data -- or you're potentially missing key data sources, your ML may be moot. Be ready to discuss this!

#5 AI Risk

Please be ready to discuss AI scope, focus and risks as part of customer on-boarding and as a part of everyday customer workflow, where appropriate, including:

#6 Efficiency and Costs of Cloud Storage and Computing

Come prepared to walk me through your data stack and cloud costs, especially if you store/analyze data for your customers and/or your product is based on a high volume of data, such as IoT (including sensors and satellite imagery), ad-tech and healthcare that’s accessing EHRs. Is it the case that you pay for every data touch-point, from storage to calculation to download? I’d be curious what those monthly costs are, at a minimum, for each focus area. Is that $10K+ per month? More? Less?

#7 MLOps

Getting real-time analytics/AI into the product is never a one-and-done. To make sure that AI is adding value and helping the company grow, maintenance is key. This includes:  

Bonus points if you score highly on the ML test. I may not ask for your score, as it’s a very high bar to pass, and none of the companies I’ve worked at -- or start-ups mentored -- have gotten there yet (!). We may touch on topics outlined in the test as they relate to your product, to evaluate how prepared you are to defend the AI moat that’s helping your company grow.

#8 People

Since AI is core to the product, do you have the right people for the job – or a plan, to help you defend this AI moat? This may include any fractional leaders, full-time employees, remote and off-shore contractors and/or talent to help align AI to answering business questions, implementing algorithms, and MLOps.

#9 Potential Gaps in Solution

Depending on how your previous conversations went, this may become a big focus area of due diligence. It's one of the topics I discuss with VCs, to help us scope out AI due diligence; e.g., what hasn't been clear -- and are there any priorities/gaps they'd like to see us dig into?

In addition, it may also be the case that the solution you proposed to solve your customer(s) problem seems, at first glance, to either be:

Be prepared to dive into this, at a 10K-foot view along with some details!

Hope this helps you close your round faster! Good luck!

Next Steps for More Support

Frequently Asked Questions

You may also like

References