Dear Advisor: What should (not) be YOUR AI roadmap? 

(or) Why You Don't Need AI in your SaaS MVP

Post was originally published in April 2022 and has been updated in May + August 2022, and May + November 2023 for relevancy

It seems that every start-up is doing AI, and getting VC money for it! How can you get started?

I’m flattered that start-ups recognize my expertise in AI – and that they reach out for support on AI strategies and roadmaps. It seems that everyone else is telling them to add AI capabilities to their MVPs. 

I think it’s too early! They’re usually surprised to hear this -- especially when they're ready to hire me and I talk them out of it (!), by sharing this contradictory advice and the reasoning behind it. This tends to build rapport. Then we end up tabling discussions of AI capabilities in favor of strategies for developing data-driven products that understand and solve customers' pain points. Here’s what I typically share.

Part 1: Definitions

First, let’s start with my definition of “AI”. 

AI: Software tool to help you automate outcomes you understand, by predicting when it may happen next, based on observed events.

Let’s unpack that.

If this sounds like that’s hard to do; it is. If you’re not able to answer the above questions affirmatively, it’s OK. You’re too early for AI! Please see the next section for advice on how to try to start getting ready.

There are many different opinions on what should and shouldn't be in scope for an MVP. In my mind, a MVP should do the following:

MVP: (Typically) Mock-up/wireframe to help you demo and test out the idea of your product, including:

That's not an easy ask. Because the goal of an MVP is (typically) a product that solves a small part of your customer’s big pain point, that – at the same time – also adds value and builds rapport with your customers, you’ll still be learning about your customers at this point. Especially in the beginning, there may not be enough signal – or enough customers – or enough runway – to develop a predictive model with actionable outcomes to uncover/confirm these trends for you. It’s too early for AI!

Part 2: Alternatives to AI for MVP

I’ve just told you AI should not be a high priority early-on – and maybe you mostly believe me :). While it may be too early for AI, it’s never too early to be data-driven. Here are some recommendations (in no particular order) on how to set-up your start-up for success in helping make data-driven decisions, which may in the long run pave the way to AI.

Approach 0 (tackling the cold start problem): Is there another industry (or even competitor) that you can borrow the initial hard-coded/rules-based recommendations from, to get your customers to start engaging with the platform? 

Approach 1: Take a step back and reflect on what your product would look like at scale, with product market fit – and try to work backwards from there. That is, what would you like to be able to do, when you reach this end goal? 

Approach 2: Evaluate your capability to make data-driven decisions based on historical data, by trying to answer a currently pressing business questions, following advice here:

Approach 3: Identify any repeatable steps/processes/decisions – these can be very small, that you can use a rules-based/workflow approach to make automated (such as via Zapier, non-affiliated), simple suggestions of next steps or outcomes. This will be the smallest step you can take now, to help you scale your product offering. 

By identifying these rules-based steps or workflows, you in turn will be creating “labels” of desired outcomes. In the long run, if these outcomes are of interest, you’ll have labeled customer and/or product attributes to work with, to help you validate an “AI”, predictive model.

Part 3: What if I'm still not convinced – and still need it?

That’s OK! This is just one person’s opinion. :)

If you do decide to continue developing AI for your MVP, I’ll leave you with parting advice. Remember:

Part 4: You’re a Product Market Fit and AI consultant. How does AI come into PMF play? Where do you come in?

Great question! Remember: the goal is not about getting AI to work, but how to scale your product offering. We’ll start small:

AI may (or may not) be the tool to get us there, based on the customer’s needs as well as the business and technical requirements to make real-time decisions. 

I’m here to guide every step of the way: to help you grow your product with market fit, and discuss trade-offs and strategies for AI once you know what's happening in your business currently and historically. Please reach out.

You may also like