An MVP is the minimum viable product. It sounds useless in a world where the digital realisations of companies are producing state of the art systems that are revolutionising the way organisations are able to operate and make money.
But when deciding to add a new digital system, especially when it involves AI or RPA, scaling back the first delivery can be essential and add real benefits to a project. The reasons are as follows:
Adding funds to tackle problems using AI means there is an opportunity cost attached. Aiming to develop the biggest and most advanced system ever created in the first push means it often leads to valuations and quotes that are scary and daunting. Start small and take one step at a time. That not to say that the system could grow and evolve to a larger system, its just he costs are relatable to progress and much more easily managed
You get to see it working
Any mystifying views of what AI is or how it can change a business gets broken when the tech is quickly in place and working. This can give confidence in a much shorter time span that it can do what was promised by a consultant. Get the most simple version on the table and it can explain what the most complex system could also achieve.
It can be integrated more efficiently
Often systems have to work alongside people who didn’t ask or want to work alongside an automated AI engine. By allowing teams to start working with version 1.0 they can become adapted and efficient, so that when 9.0 gets pushed into the production cycle, its already second nature. Everyone can then get it.
Any expansion of the tech are because of testing and research
When taking the next steps in improving or adding to the MVP, its not because of an idea at the begging of development to what may and may not work; its because the MVP has been tested and logical assessments can be made to its function and operation. It grows from knowledge and experience, not guess work.
Fixing of any bugs happen first
Instead of running 1 million mies an hour at the goal of building a monster AI, which would lead to a monster list of bugs to close, the issues that get closed during development are only as big as the software itself. therefore development ends with a more operational system that is more likely to be made active.
You can ensure the foundations are strong
When building an AI, it’s the same as a house. Ensuring the core functionality is working and adding value is a great way to be able to add more functions and tasks to the AI; this way as the system gets more complex, you can rely on it working more of the time.
It can add value much faster
Time is precious. Save time by having something that can add value quicker. Even if development has to happen after the MVP is delivered, the MVP can add value in the background, also being useful in researching what works!
The best things to focus on get targeted
The first thing a consultant should focus on is the ‘use cases’ these are an output or an example where the system must accomplish something ie send this type of email, or give a decision on this specific task. Usually creating a most important list is the best of these use cases. Then pick the top few to focus on for the MVP. This ensures that the fist and most valuable things get closed first, essentially adding the most value first!
Development can stop and take stock
With development running as fast as possible to get to delivery, it is often most efficient to stop and understand what is going well and what is going badly. This ensures that the next steps moving forward are better than the last. The next bits of development can then either cost less or happen faster.
AI is fast moving and needs building into reality (unlike most software)
Most software is designed and gets used. Simple. AI is different, it needs to work in unison with the real world. Thats what this documents use of an MVP is even more necessary. The world of AI changes and fluctuates as it evolves and becomes more intelligent, this needs to be captured in any project it concerns. Also AI relies on real world to provide benefits, each integration with the real world needs to be checked and adapted to ensure success. Accuracies need to be tweaked to ensure that the AI meets expectations. The best way to do this is starting with an MVP