Agility in AI Projects: How to Adapt Your Approach to the New Technology

AI is one of the most exciting and challenging technologies available today. AI can help us solve problems, improve processes, create new products and services, and more. But AI projects are not like other projects. They require a different way of thinking, planning, and executing.

Why are AI projects often exploratory, experimental? You need to manage high levels of uncertainty. You dont't know what you don't know. You don't know in advance what risks thare are, results you will get, how long it will take, or what data and resources you will need. You need to be prepared to learn, adapt, and change along the way.

That's why you need an agile approach for your AI projects. An agile approach means that you work in short cycles, deliver value continuously, involve customers and users, and constantly evaluate and improve your solution.

But how do you do it in practice? How can you adapt the way of working for your AI project? Here are a few tips:

Define a clear purpose and goal. What do you want to achieve with your AI project? What problem do you want to solve or what need do you want to meet? Who are your customers and users? What are their expectations and requirements? How do you measure success?

Break down your project into smaller pieces. AI projects can be complex and extensive. It's easy to get lost in details and technicalities. Therefore, it's important to break down your project into smaller pieces that you can manage and deliver quickly. Use user stories, for example, to describe what you want to do, why you want to do it, and for whom you want to do it.

Prioritize and plan your backlog. A backlog is a list of all the parts that you want to do in your project. You need to prioritize your backlog based on what provides the most value to your customers and users, as well as what is most uncertain or risky. You also need to plan your backlog based on what is possible to do with the data and resources that you have available.

Work in iterations. An iteration is a short period of time in which you complete a part of your project. An iteration can be anywhere from a day to four weeks, depending on your project and your organization. During an iteration, you should design, develop, test, and deliver your solution to your customers and users.

Deliver value continuously. You shouldn't wait until your project is finished before you show it to your customers and users. You should deliver value continuously throughout the project. This means that you should create working prototypes or minimal viable products that you can get feedback on. It helps you to validate your solution, learn from your mistakes, and improve your quality.

Involve customers and users. Your customers and users are the ones who know best what they need and want from your solution. You should involve them in the entire process, from ideation to delivery. You should communicate with them regularly, listen to their opinions, requests, and problems, and ask for their feedback, input, and approval.

Evaluate and improve your solution. You shouldn't settle for the first or best result that you get from your AI solution. You should evaluate and improve your solution continuously. You should measure your performance, quality, and impact using relevant methods and tools. You should also reflect on your approach, identify what is working well and what can be improved, and implement the necessary changes.

As you can see, it is possible to adapt the way of working to the demands of AI projects as they requires a great deal of adaptability, an open mind, and a willingness to learn and change. It is not always easy, but it is worth it.

If you want to learn more about how you can use the agile approach in your AI projects, or if you need help implementing it, you can contact us at Aliby. We are experts in exploratory approaches and we can guide you in creating amazing AI projects that deliver value to your organization.

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