(photo by DoeLay)
What strategy will best determine how quickly or well prepared your organization is to incorporate AI tools for project management? The agile method is based on iterations that produce a result and then make changes to the project as required. This constant feedback loop provides more real time data and thereby allows an AI algorithm to perform instant analytics, simulation analysis and predict an outcome that may or may not require further intervention in the project management process. On the other hand, the typical waterfall approach has significantly more data in the form of project documents and easily lends itself to the concept of data mining and predictive analytics. Massive amounts of complex data with subtle interconnections can easily be managed by an AI tool that can then define adjustments that need to be made for project success.
How will AI tools for project management work? The common machine learning premise is based on two factors: learn and predict. The AI algorithm learns how to perform a function, based on data, and then can predict the outcome of an event that is new and not part of the training database. This is ideal for project management where, by definition, projects are unique.
If an AI tool can make better decisions in a faster paced agile environment then it may result in fewer iterations or less change. On the other hand, faster does not always equate with better. Data normally has two major problems when being used for predictive analytics. The first is the problem of outliers. If data is within a normal distribution then small adjustments may be made successfully. If an outlier occurs and skews the data provided to the AI tool then reacting too quickly to such an anomaly could bring disastrous results. The second issue is biased data and this is a problem for all AI tools. Historical data is not always the greatest predictor of the future. For example, if you want to select the best project manager based on historical data, the odds will probably be in favour of a male because female project managers are underrepresented in this field.
The answer to which project management approach will be able to adapt more easily to AI tools is debatable. It will likely depend on the scope and purpose of the tools developed. The waterfall approach is more naturally prepared to incorporate AI tools that embrace a holistic approach to managing projects. Agile will adapt more readily to tools that target specific issues in the agile processes. Regardless, organizations that understand their data and deliberately prepare for AI will be the first to find value in the impending AI tools that are going to change the way project management is performed.