AI MVP Development​: A Step-by-Step Guide on How to Build an AI MVP

It’s no secret that for your AI software to have a good and successful start, you need to create a minimum viable product (MVP). This step will allow you to assess how much your idea is in demand by the audience (and whether you have reached the right audience at all) without large investments of resources. However, in order to achieve the desired result, it’s not enough to just write a few lines of code, come up with a simple design, and launch the MVP to the masses. Even though the AI ​​MVP is a small product, it still requires a strategic approach.

In this article, we will tell you how to properly prepare an AI MVP to achieve your goals and what things you need to take into account during this process.

Some numbers to consider

Before we jump into the strategy itself, let’s take a look at some statistics regarding MVP as a part of software development. Here they are:

• Around 90% of startups fail, unfortunately. Top three reasons for it are running out of fundings, no market need, and getting outcompeted. All three of them can be solved with the help of an MVP.

• The startup failure rate seems to be around the same across industries. Meaning you will need the MVP no matter what industry your business belongs to.

• 3 in 4 VC-backed startups never return cash to their investors. In order to be the fourth one, you need to know whether your project will thrive. And MVP will definitely come in handy here.

So, a well-built MVP can determine your business’ success. Not only that, but it also will help you save your time and money on the idea that is not relevant right now. The advantages of building an MVP in AI are hard to underestimate.

Six steps to building an AI MVP 

Now we are going to move on to the main part: the guide itself. We are going to describe what steps you need to complete to create a perfect MVP that will 100% complete its purpose. Here’s what you have to do:

Step 1: Define the problem and validate the idea

This is the first step in every MVP development process, not just the AI-powered one. Before you write a single piece of code or draw a single wireframe, you need to understand what problem exactly your solution will solve. Sure, there can be multiple options, but for an MVP, you should only choose one. It will determine the set of features you will need to focus on.

A good way to do it is to complete a full-scale market research. It includes identifying your target audience, analyzing your competitors, and reviewing industry trends to see what exactly you need to build (or whether you need to build anything at all).

Step 2: Plan your AI MVP

The next stage is planning. Here, you and your team will need to define what AI model you are going to use and what tech stack will be the most suitable for your MVP. Do you need machine learning or ruled-based AI? Do you need a pre-trained model or you will train it from scratch on your own data? Then what data to prepare? What AI framework to use? What success metrics and KPIs to set? All these questions should be answered before you start the development process.

Also, on this stage, you should prioritize the features and choose which ones will go into the first version. There are plenty of ways you can do it, for example, MoSCoW method (Must have, Should have, Could have, Won't have), feature priority matrix, or story mapping. All these approaches will help you understand what your future users should see first and what can wait till further updates.

Step 3: Design

Now we can start designing your MVP. The main thing you should remember here is that your design should be simple and usable. Start with black-and-white wireframes that will outline main screens to see the UI/UX clearly. Think about the ways users are going to interact with AI, like a chatbot or a dashboard. Then add more colors and create a prototype to check if the userflow is smooth and doesn’t have any bottlenecks. You can use it during stakeholders meetings to show the progress or bring it to the control group of users to see what they think about it and gather feedback.

Step 4: Develop your AI MVP

Finally, we are going to write some code. With the tech stack you defined previously, your team should bring your idea to live. You should start collecting and preprocessing your data so the AI model will have enough material to train on. Then the experienced team of software developers (either in-house or outsourced, depending on how you want to approach it) write the code and review it.

Another important part of this step is testing. Your team needs to test everything before the release. Despite the fact that MVP is a small product, it still needs to go through manual, automated, and security testing. Whatever stage your AI software is on, you must ensure that it works like clockwork.

Step 5: Launch and gather feedback

When all previous steps are done, your AI MVP is ready to meet real people. The best way to approach it is to start a soft launch to the limited audience of early adopters. This step is necessary for easier activity monitoring and feedback gathering. When it comes to AI, you should follow the metrics like accuracy, response time, and user satisfaction. And for feedback, use questionnaires, interviews, and in-app tools to collect the feedback.

Step 6: Iterate and scale

Now it’s time to scale. When you have enough user feedback, you can identify the most common issues to fix and most wanted features to implement. It will set the course for further updates and help you understand how to proceed with scaling. And if your AI MVP received a lot of negative feedback, don’t worry, MVP is built for experimenting. The more you try and iterate, the better the final result will be.

Key considerations for building an AI MVP

Besides what we already mentioned, there are plenty of things you should consider to strategically approach your AI MVP development. Here are three key factors to pay attention to:

Choosing the right development partner

Your AI MVP’s success highly depends on who builds it. Sure, you can hire an in-house team of AI experts and software engineers, but if you don’t plan on building any more AI projects, it can turn out expensive compared to the income from your app. A good approach (especially if you have a limited budget) is to hire a software development agency that will be ready to provide you with MVP development services. You should look for one with enough AI and industry expertise so they can quickly understand your business and seamlessly join the team.

Budgeting and resource allocation

AI implementation can be costly, even for just an MVP. You will still need to prepare and store data, train and integrate the AI model, and build sufficient infrastructure for everything to function properly. And expenses don’t stop there: You will need to continuously monitor the way your AI model functions and update it if necessary. All these processes require plenty of time and money, so it’s better to be prepared.

Ethical and legal implications

AI can still make mistakes. If artificial intelligence is the backbone of your MVP, you need to take full responsibility for how it acts and what it says. You need diverse and unbiased data to minimize possible unfair treatment of your users. Also, your AI should know how to handle sensitive information. If you deal with AI, security and ethics should be your highest priorities.

Conclusion

The strategy we provided may seem simple, but it includes a lot of resource-consuming processes. The fact that it’s just a minimum viable product doesn’t mean you should treat it like a side quest. This solution can define the success of your idea, so you need to put enough effort into creating it. But once it’s all done, you will be left with a perfect foundation to start your business growth.

If you need a hand in building your AI MVP, contact us! We are a software development agency with extensive experience in artificial intelligence and MVP development. We are ready to make your idea come to life.