Data And Artificial Intelligence In Project Management

Over the years, irrespective of Project Management techniques we adapted to, various models is a well-known fact that the majority of projects are delivered late or fail to finish at all. In fact, according to Standish research, only 33% of all projects completed successfully. The time and cost overruns of delayed or failed projects cost the global economy ranged anywhere from tens to hundreds of billions of dollars each year.

Since many years, when project managers planned projects using old school way with pencil and paper, to the invention of waterfall and later to the agile project management method, people have tried to come up with new methods for making project planning and tracking more accurate predictable and reliable.

But still, in 2018, projects deliver late and cost more than they were planned for. It seems new methods for project management are not the answer to project management’s root problem. Maybe we need to think out of the box to understand and figure out a better way of planning, tracking and managing projects and managing better with predictability.

With that in context, AI (Artificial Intelligence) could answer this question and solve the chaos better. Google is using AI for better search results, Amazon is using it to better match buyers with the products it sells. Incorporating artificial intelligence into project management software can enable us to learn from the past and current projects and applies the learning to future projects. This is turning a great asset to Project Managers in terms of assessing risks, managing better and increasing the rate of success.

Having said that we are not talking about replacing the project manager with software, but giving the managers tools to better plan and estimate the work that needs to be done on a project. The application will help to find issues as soon as they happen in the projects and will present several options to reasonably resolve those issues.

Imagine in the future how there will be project management software that can monitor and study each member of the team and when they work on a project. It can figure out the capabilities and inadequacies of each one of them, and for the entire team. It can figure out if the project is progressing too slow to make the deadlines or not following the optimum path—and warns the manager and the team to take corrective action before it’s too late. It could use the organization’s data from similar projects in the past and guide the teams as of how to progress for the best result based on resources the team has at any given time.

This is close to what a team coach in a sport does, when evaluating his team’s capabilities (and the competition’s) and decides how to play against a competitor in the next game based on past games’ performances. The coach also alters the game plan as the game proceeds based on the strategy deployed by the other coach, and how both teams are performing and overcoming the past failures or strengthening the team’s game plan.

Here the key question is how will such software collect all the data it needs to create a model for each player and the team as a whole? How does it know the current state and important data of the project and match it against the project plan? After all, the quality of this analysis and the results totally depends on the quality and integrity of the data collected. So the data always plays a critical role here.

Obviously, this doesn’t mean that someone inserts probes into team members brains to find out what they do or think and how they respond to risks. The only way to have this data is to have everything done in the project—from features to stories, tasks to bugs, documents to conversations—in one common central application that collects the data and analyses constantly and uniformly it as it happens.

This could soon convert the project management software as a knowledge base of how to plan, track and manage projects. It will help the project manager find the best path to get optimal results by enhancing the predictability, success rate and also overall transparency to a stakeholder. It will warn about imminent problems and will give several options on how to fix those problems. It will incorporate issues and risks in the planning of projects—and prepare plans in case certain risks become the project’s reality. With these traits in an organization, AI can make the entire Project Management Office act as a consistent army to deploy projects with FTR (First time Right) approach.

The possibilities can’t be imagined completely when the combo of Data and Artificial Intelligence work as a powerful tool in Project Management space.

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