From software development to construction to logistics and finance, every company has projects that need planning, managing and monitoring. But the tools we use to do that are often complex, designed for specialists and don’t do as much as they could to warn about potential problems. Could AI-powered decision support systems and automation make more of your projects successful by reducing costs and mistakes, analyzing risks, making things more efficient or keeping things on time and on budget?
Here is an early look at how artificial intelligence, machine learning and predictive analytics could affect project outcomes in the years to come.
Thinking about risk
Managing a project well takes more than just making a great plan in advance and sticking to it. Interdependencies within your project and external changes make outcomes unpredictable. Estimates and many forecasts are at best intuition; at worst, guesses and handwaving. Modern management techniques such as agile and continuous delivery aim to reduce uncertainty by working incrementally, but that still doesn’t guarantee final delivery. Portfolio management selects a mix of projects that balance risk and reward (because it’s hard to stay competitive if you only play it safe), but that means assessing risk accurately, which is hard.