5 Reasons Why Every AI Project Needs Good Project Management
Artificial intelligence (AI) is reshaping the world as we speak. It allows machines to process data like never before with more capable AI models and complex equations. The development of AI projects for different purposes is also accelerating alongside the development of demand growth and implementation possibilities.
An AI project, however, can be complex by nature. Before an AI can be functional, there are tasks such as data collection and labeling to complete. Models depend highly on learning processes too. These are the kind of tasks that can benefit from good project management. Of course, there are plenty of other reasons why every AI project needs a suitable project management approach.
Strategic Alignment Is Important for any AI project
First, we have to look at how important strategic alignment is in a complex AI development project. You cannot expect the end result – the working AI implemented into a data processing system – to be suitable for use if certain details are not planned from the beginning.
Every development project needs to have clear goals in mind. Those goals need to represent the strategy incorporated into the development project as well. Good project management means making sure that each part of the project aims for the same set of goals.
It is also worth noting that project objectives may change as the AI gets developed. There are breakthroughs to anticipate and changes to adopt during the process. Being able to maintain alignment is crucial in this process.
Tasks Can Be Very Complex
As mentioned before, there are a lot of tasks that need to be completed correctly in an AI development project. Tasks also affect each other; in fact, the dependencies between tasks in an AI development project have the potential of altering the course of the entire project.
This is where approaches such as Kanban methodology comes in handy. Kanbanize, a modern Kanban platform, allows you to organize even the most complex set of tasks. It enables the management of multiple stages in an AI development project. In fact, Kanbanize has features that make organizing tasks and dividing them into smaller checklists easy. At the same time, you can utilize the Kanban backlog to make your tasks more actionable, better prioritize and refine them so you don’t generate waste once they are already in development
Next, we have the fact that an AI development project is often handled by a big team. When there are multiple AI researchers, data scientists, and engineers working on a single development project, things can get very complicated very quickly.
Sticking with Kanbanize as an example, assigning specific tasks to team members is a lot easier when the tasks are clearly defined on the digital Kanban board. In fact, the entire Kanbanize platform can be used as a communications platform throughout the project.
Comments, updates, and resources can be directly attached to individual cards or tasks. Anyone in the team can contribute to the completion of tasks without having to spend a lot of time rereading materials or going through the data.
Improved Time Management to avoid AI projects push backs
It is not a secret that developing a capable AI takes time. Even with existing frameworks ready to be integrated, engineers and AI scientists still need to invest time and energy into producing a working model and integrating the AI engine with other solutions.
Time is of the essence in any AI development project, and good project management is how you improve time management. Keep in mind that delays in completing one task could result in the entire project being pushed back by a substantial margin, so you know time management is important.
Creating task dependencies helps. It allows you to identify potential bottlenecks before they turn into serious issues and jeopardize the entire project. You also gain the ability to save time and boost efficiency by triggering dependent tasks as soon as the previous ones are completed.
AI Projects need Better Quality Control
Last but certainly not least, there is the fact that good project management allows you to optimize the output of the development project to a certain degree. Something as simple as allocating enough time for reinforcement learning and making sure that data sets are annotated correctly can be added to other tasks.
Even testing becomes an inseparable part of the process. Engineers can go through their Python or Go codes more meticulously and QA engineers can perform reviews and tests without having to wait for the entire project to be completed.
The result is a more seamless AI development project from start to finish. Tasks are no longer fragmented and can run concurrently if needed. You are not only saving time and resources but also improving the overall quality of the AI produced by the project.
These five reasons represent how important good project management is in any AI development project. If you haven’t incorporated project management tools like Kanbanize into your development workflow, now is the perfect time to get started.