Machine Learning Applied to Project Management
Through the internet of things, automation of processes, smart devices and even tools that anticipate our needs before they even occur, humankind has become extremely comfortable with living amongst “robots” that help us make our daily tasks easier. When it comes to these innovations, however, there is always one thing that differentiates us from machines, and that is the ability to learn.
But what if thanks to the most advanced innovations ever seen, we could teach these tools to behave more like humans by analyzing behaviors and then using them to make accurate predictions? That’s what machine learning is all about.
Why machine learning matters
According to DigitalTrends.com, “machine learning is an approach to artificial intelligence that’s focused on making machines learn without being explicitly programmed”. In other words, machines are able to understand behaviors and better predict outcomes before they even happen.
A good way to think about machine learning, is thinking about our email inbox. When we go through our emails, we determine which ones are relevant to us because they come from people we know, and which ones should be marked as spam because they either come from a stranger or use trigger words such as FREE or CASH.
Once we mark these last emails as spam repeatedly, our email provider’s algorithm learns that these words and these email addresses should be tagged as risky, and it creates a rule to always send them to a spam folder moving forward.
Now this simple concept can be made a lot more complex throughout time, and it can definitely be applied to project management.
How is machine learning shaping project management?
Machine learning, much like other technologies that are being implemented in the workplace, is shaping our day-to-day by improving efficiency and generating better results. Additionally, it helps eliminate manual tasks and reduce daily errors. Here are the main ways in which project management is being impacted by machine learning:
1. Prediction and task assignment to the rightful team members
Once you tag the same person to the same type of task multiple times (e.g. always assigning every design task to John, the graphic designer) and allocate a certain amount of time to specific assignments (e.g. always allocating 1 hour to content development), the machine will learn this and predict how long a task will take and who should own it.
2. Automatic user tagging in comments that are relevant to them
When the machine understands which person is relevant to each task, it will potentially know when someone needs to be notified about an update.
3. Visualization of notifications and updates based on their relevance to a particular user
Through machine learning, machines can also learn to prioritize updates depending on how important they are for each user.
4. Prediction of when deadlines aren’t going to be met
If the machine knows how long each task should take and what other tasks need to be completed before, it will be able to accurately predict and determine whether completion will happen on time.
5. Correction of task time estimates
At the beginning of a project machines can estimate a certain amount of time for a task based on the information it has at the time. As the projects moves forward, however, the machine will learn new information that will allow it to correct estimates and have better predictions of project completion.
The Bottom Line
Machine learning is shaping the future of project management, yet we are still years away from finding a perfect science that will completely eliminate the need for human interaction. The key? Finding the best possible tool that fits your company’s needs and will encourage your employees to excel at their jobs.
Contact Kimonus today to find out how we are impacting the future of project management through state-of-the-art technology.