Artificial Intelligence hasn’t quite arrived in the project management sphere yet, but it’s on its way. Gartner forecasts that 80 per cent of project management roles will be eliminated by 2030 as AI takes on traditional project management functions such as data collection, tracking and reporting. The same report highlights that programme and portfolio management (PPM) software is behind the times, and AI-enabled PPM is only just beginning to surface in the market.
However, while some tasks will inevitably be automated, it opens up other opportunities for project managers. It’s important to know the difference between how AI-enabled automation can change project management and how AI-enabled insights from massive databases can make a difference.
Project management is taking its first steps
In 2019, we’ve seen projects that have started to apply basic machine learning and robotic process automation (RPA) tools to improve the data they are collecting and how it’s being processed. Greg Lawton, founder and CEO of AI solutions firm Nodes and Links, gives the example of a client who was using people to count lengths of pipe. Turning that job over to an algorithm saved the firm 30,000 hours a month, but AI has the potential to do much more.
“My advice to project managers is to understand how they create value within their business model and then to be open to talking about that and discussing this with the new generation of AI companies. Only then will the art of the possible be known. And only then will solutions come to fruition that aren’t just features that completely miss the ball,” says Lawton.
Dr Ian Clarkson, head of organisational consultancy at QA, says that project managers need to diversify their skillset to prepare for the coming AI-led future. “You’ll need data analysis skills, creative, innovative thinking. Communication and relationship management skills will be very important.”
AI in action
Paul Taylor, executive technical director for programme management at professional services firm Stantec, explains that the business is experimenting with machine learning. It is currently focusing on standardising its fragmented data sets into a fit state to get AI processes working effectively.
“Once we’ve got that data sorted, we can buy in cognitive services from cloud companies. They can take the data sets and use AI tools to reanalyse them to give you predictions from the information you’ve provided. For example, you could look at resource analysis far more intensely or look at durations far more effectively. We’ve started testing that and have formed an in-house analytics team to build our own AI models. It’s proving very valuable.”
One project is looking at what triggers an alarm at a water treatment plant. “When it trips, is it something to be looked at, or is it only when three or four separate alarms trip that we have to get the operative out to look? The analytics team has been looking at that to improve the ‘find and fix’ work for water companies,” says Taylor.
RPA provides support
He adds that RPA will augment the day-to-day work of project managers, but not replacing them. Instead of RPA, Taylor is looking at introducing more fundamental AI processes through cognitive services. “Instead of replacing a specific function with a bot, let’s improve the base services with a better software package.”
Stantec will deploy these tools to help them make decisions about how the projects and programmes are performing. “We are expecting to bring these types of thought processes in from April 2020 and offer analytic services like these to our water clients in 2022 or 2023,” Taylor says.
Logistics in project management is one of the easiest things for AI to start looking at. “We are working with some other companies on this, where you tag the product with a code and, as it moves through the product life cycle, you get information about that product all the way through.”
Analytics like this are already being used on construction sites to determine when products should be delivered and whether it’s more cost-efficient to stand teams down, rather than paying them to wait.
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