Artificial Intelligence and Project Management
If you follow project management trends, you’ll be aware of the impact that artificial intelligence (AI) is having on project management software. Have you seen any impacts yet to the work you do?
Don’t worry if you haven’t yet. Leadership teams are on the look out for smart ways to integrate tech-driven tools into project management apps – and you shouldn’t be worried about being out of a job.
Many people think of AI simply in terms of robots doing the work of humans, but it’s less sinister than that! The AI applications we are seeing at the moment are more about replacing the drudgery of data collection and input with smart, workflow-led tools. Machine learning can carry out the initial analysis or compare large banks of data to spot trends that might inform future performance.
The goal of AI in project management is not to get rid of the role of scheduler, planner or project manager but instead to free up those people to do more value-driven work, leaving the repeatable, programmable, functions to machine learning tools. It is a way of changing how time is used to allow our most valuable resources – people – to spend more time doing what they do best.
How are AI tools used in project management?
AI-driven applications can process large amounts of data in a way that would take humans with an Excel spreadsheet far too long. For example:
- Reviewing lessons learned from past projects to surface insights that are relevant for the current project stage
- Analyzing the accuracy of past estimates to predict the likelihood that future estimates will be reliable and making adjustments accordingly based on the accuracy track record of the estimator
- Scanning project documentation, collaboration tools and emails to identify and surface potential risks, issues, changes and actions
- Reviewing the efficiency of historical risk management activities and suggesting where current risk management plans could be improved based on that analysis
- Looking for patterns in schedule delays and suggesting steps that would address these
- Making smart recommendations for the use of skilled resources to better manage capacity planning across a portfolio.
We’re already seeing the smart application of AI in the Oracle toolset with options like Reconstruct, a visual data analytics platform that pulls data from the project schedule in Primavera P6 PPM as well as videos and images captured by crane cameras and drones. The schedule can be visualized in a smarter way than simply as a Gantt chart. The visual data can be compared to plans and analyzed for slippage, new risks or issues. AI software and new ways of thinking provide a new way of looking at project progress and sharing that with stakeholders.
AI for improved project success rates
All of these applications for AI in project management are about being able to actively manage the project and improve the chances of a successful delivery. When you are making decisions from a position of evidence and data, the outcomes should be better. While we’d all like our projects to do really well, hope is not a strategy! Putting in the energy to set them up correctly at the beginning will remove some of the uncertainty and risk around the very early stages of project initiation.
AI-based information can be really helpful at this point because it can consolidate vast data sets from your company, your industry and beyond to surface insights that will help plan effectively. External data sets – those that collate project data from outside your company – are starting to be made available.
Project Management Office teams can opt into sharing non-sensitive information as part of various industry consortiums where data is collected for this purpose. Over time, the amount of information that we can draw on to inform project success (and reasons for failure) will increase, leading to a wider adoption of the data set and new uses for the information.
Overall, the community spearheading the adoption of AI in project management seems to be driven by improving project success rates, and that’s an idea we can all get behind.
The purpose behind bringing AI into our toolset as project leaders, is not to replace our staff, but to give them the time, space and data required to solve complex problems. We’re excited about the future of software that could provide decision-support information and make use of the mountains of data we have inside enterprise project management solutions like Primavera. What about you?