Poor monitoring and allocation of resources causes almost two-thirds of professional projects to experience severe delays. Teams are beginning to use AI in project management to better spot these time wasters.

In order to get to the bottom of things, I decided to apply an intelligent layer over my current routine for a whole month. I had been ignoring significant bottlenecks for months, but this testing showed that my regular software was really disguising them.

We were unable to get our most important job done because of generic alerts

My previous AI-assisted pm & qa program excessive noise production was the first item that caught my attention. My phone would ring whenever a teammate made a remark or moved a card. 

Priority recognition was hardcoded into the system when we shifted to a bespoke technique. Three notifications were sent to me instead of fifty. I received an alert informing me that the allocated individual was still occupied with another activity, and that the deadline was only two hours away.

Lack of flexibility in the board layouts stifled team innovation

At one point in my career, I believed that the conventional kanban board was indispensable. But the columns began to stifle my creative team. You couldn't just put certain things in the "to do" or "doing" pile. Changing your work style to accommodate the tool is typically a requirement of standard software. People end up wasting time moving cards instead of accomplishing the real task because of this strange friction.

The trial period showed me that AI in Quality Assurance could track our organic progress through a project. We switched from a rigid grid to a bespoke interface that enabled a more cyclical process.

We were in the dark since the software wouldn't communicate with the others

While testing, we switched to a different app from the one we were using for tasks. The issue was that data was never sent between the two platforms. Someone has to go back into the project manager and fill in each issue detected during testing. The problem with much of the software out now is that it tries too hard to keep you within its own little world.

On one particular Tuesday, I was using the Automated quality assurance app instead of the testing app, and as a result, I missed a big customer request. We enabled seamless data transfer between the two parties by using a bespoke construct. An AI-powered quality assurance system saved the day in this situation.

The real causes of our delays 

I used to check a chart showing the number of jobs we completed every Friday. We continued to miss our major deadlines, even though everything seemed to be alright every time. These so-called vanity metrics are provided by most software; they are useful for meetings, but they do not reflect actual performance. I could see that we were getting through a lot of the simple jobs, but the more difficult ones were left unattended for weeks.

Identifying the precise locations of friction is made possible by custom software development.

We were unable to grow appropriately

The features remained unchanged despite the price increase. When a specialized or expanding team outgrows the capabilities of standard software, the associated costs might quickly become prohibitive.

When we built our own solution, we were the proud owners of the tool. We were no longer charged on an individual basis. As we added more data, the AI-powered QA testing truly grew better, as I discovered. It was not a motionless instrument. Based on what it learnt from our new employees, it adapted the project timelines accordingly.