The three basics of project management are timing, budget and outcome. Every project manager will tell you how essential it is to know these elements before you start a project. It’s the traditional way to manage projects. But there’s a catch: it doesn’t work for AI implementations. Yet, many AI companies hold on to this way of working, leaving their clients with a financial hangover and a system that barely works.
Why doesn’t traditional project management work for AI? Three reasons:
- AI needs data. But not every company has enough (good) data in the initial phase to feed an artificial intelligence tool. Many have to start from scratch to collect their data. This takes time, making it hard to set a feasible deadline.
- Gathering data, setting up sensors, and building a user interface: AI requires some tools to work and bring its results to us, humans. However, it’s difficult to say beforehand what you’ll need, exactly. Along the way, the requirements might change because you get more and more insight into your needs. This makes it near impossible to determine a clear budget for your project.
- AI models are very unpredictable. What works for one company, doesn’t necessarily work for another. In other words: it’s difficult to tell in advance what the outcome will be.
How to manage AI projects
Many AI companies dive into an AI implementation project without a second thought. They determine timing, budget and outcome—making promises they can’t keep. Because of this, you’ll end up spending more money without seeing a decent outcome. You get it: handling an AI project the traditional way isn’t going to cut it—AI needs a different approach. This is a good practice for AI implementations:
1. The feasibility analysis
You need a feasibility analysis to get a better idea of what you’re in for. A feasibility analysis is a comprehensive evaluation of how AI could work in your company. It focuses specifically on your company’s challenges, needs and desired results. Is artificial intelligence the best tool for you? How can it be integrated with your other systems? Will it improve your processes or output? The answers will help limit the risks of your investment.
2. The experimentation phase
Happy with the results of the risk analysis and ready to go? Then the experimental phase is next. Often skipped by AI companies because it takes up time and effort, the experimental phase is crucial for successful AI implementation. For example, Applicaite’s experimentation platform is an accessible way to quickly see what AI is capable of in your specific situation.
3. The proof of concept
If the experiments are a success, we dive a little deeper. A proof of concept, integrated with your processes, is a neat way to show you what your AI system will look like, what it will do and what it will deliver. With an AI system set in place and a short testing phase, you can check if the system delivers the hoped-for business value.
Affordable AI integration
These three steps are important to get a clearer idea of your AI project. The upside of the feasibility analysis, the experimentation, and the proof of concept is that they are budget-friendly. You don’t need to put all your money on the table just yet to see what AI could mean for your company. This greatly limits the risks of your investment. An affordable AI integration will give you the confidence you need to go ahead with AI adoption in your business.