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.
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). You also need to put in some information on the source of risks: “Don't just blame the last person holding the problem; figure out what's going wrong,” as Heintz puts it.
That’s information most teams will have, he suggests. “Teams that don't have a tremendously rigorous process can still use our tool right away. If a team has a backlog that are seven things written on a napkin, we can still give them some help. If a team has a complete best case/worse case analysis and work breakdown structure for the whole project, we can give even more advice,” Heintz says.
Aptage uses visualisations of confidence, feasibility and whether the risk is going up or down over time to help you switch between what Heintz calls fast and slow thinking. “We had to create these visuals because we need to connect with the fast-thinking, intuitive mind to help people see things in a way that lets them make good intuitive decisions. If the project starts getting a whole lot more red, the lizard brain should have some fear. Maybe we still decide to go forward with that project, but we’ve thought about it and we’ve been triggered to think about the right things. ‘That might hurt but we have a safety net; if we have to spend 20% more on this project, we'll still have a good probability [of success]; let's take the risk.’”
The algorithms and models Aptage uses were designed for software development but also fit construction projects.