Here is an early look at how artificial intelligence, machine learning and predictive analytics could affect project outcomes in the years to come. 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.
“The risk in a project is always probabilistic and the human mind is not good at doing risk-based probability management, especially when we’re combining many different probabilities,” Aptage CEO John Heintz tells CIO.com. It’s easy to confirm your own opinions; “I got the answer I expected, and I agree with myself.” We’re also prone to what he calls “hope-based planning.” “It’s natural: We’re to some degree all optimistic; we all see the positive path forward, the way this could work, and we don’t have evidence to prove it can’t work, so we hope it’s going to go the way we want it to,” Heintz says.
Aptage uses machine learning to predict the outcomes of projects using data you already have, such as the planned start and end date of various phases of the project (and, if you have them, estimates about any backlogs) to learn the completion rate of the team and predict the likelihood of delivering on time. Estimates are always uncertain, so you can put in upper and lower bounds for how long tasks will take (or the software can model it using the golden ratio). Read more from cio.com…
thumbnail courtesy of cio.com