Machine learning (ML) is gaining a lot of attention in the business world because of the potential benefits: greater automation, increased efficiencies and enhanced processes—to name a few.

But in many cases the technology is failing to deliver value for organizations because they’re not using it correctly to address actual business problems, or they’re neglecting to apply ML to processes in which it could make a real difference.

One example is project management. ML can be an ideal technology for improving the way organizations run projects such as software development. In many cases, problems arise during the course of a development project that can lead to delays in delivering the product or to poor quality software.

Rather than waiting to address these issues after the project is complete and the damage is done, companies can use ML in advance to proactively discover patterns in historic data from previous software development projects. This can be project data from the past year or two or even further back—depending on how much is available.

They can then use this data to build ML models, and leverage the models to anticipate real problems before they actually occur during the development phases. In some cases the models can predict issues weeks ahead of time.

By taking advantage of the predictive capabilities of ML, teams can take steps to address problems in advance, improving the development process. ML used in this way can be a valuable tool for project managers and can lead to benefits such as faster time to market for new products and increased customer satisfaction.

Applying ML to processes such as software development projects requires a few key best practices.

  • One is to deploy ML models in a timely manner. If they’re delayed, they won’t enable teams to discover and address problems in time.
  • Another is to involve domain experts who can identify key variables such as which specific events can create risks to project performance and how far in advance models need to be able to make predictions.
  • A third important practice is to deploy automated feature engineering, which involves using domain knowledge in order to extract patterns, or features, from the raw data. This is vital for creating useful models to predict problems.
  • Finally, organizations need to put ML models through multiple rounds of validation and testing before using them to predict problems.

Project management is just one example of where ML can make a real difference and deliver actual business benefits for companies.

Want to learn more about how to reap benefits from machine learning? Check out our white paper, “Machine Learning 2.0.”