On time and on budget

How data analytics and AI will shape the next decade of program and project management

The challenges in program management are well-documented.

From HS2 in the UK to Hanford’s nuclear remediation in the US, the news is full of big projects which are delayed and over-budget. In fact, as many as 80% of large infrastructure projects worldwide are estimated to overrun.

But as we look to the future there is a massive opportunity for change.

Improvements in data management technology and AI breakthroughs offer an opportunity to overcome many of our industry’s traditional challenges, getting programs running quicker, and helping us accurately identify and manage cost, schedule and quality risks earlier.

We are also experiencing a big rise in demand for program management services, with megaprojects in the Middle East and huge public works programs in the US and Europe. Approximately $130 trillion is predicted to be invested into decarbonization and infrastructure renewal projects alone by the end of 2027.

So how can we capture this value?

Data

Everyone wants us to do things faster, more efficiently, and more predictably. And the key to this is data.

Barring extraordinary extenuating circumstances (such as global pandemics) most project overruns are caused by a lack of good quality data. This can either take the form of not enough information at the start of the project, preventing accurate planning and costing, or not enough information during construction, so that teams struggle to identify and respond to challenges and risk in a timely manner.

Data analytics and visualization tools have improved in leaps and bounds over the last few years, significantly improving our ability to manage and plan. But without comprehensive and up-to-date underlying data we still are not realizing their full potential.

Take schedule analytics. To assess an ongoing project against its schedule, you would usually export the data from Primavera P6 or Microsoft Project, run a DCMA quality check, and then use a tool such as PowerBI to interpret and display it. The challenge is that this process can take a long time to do manually, meaning that by the time data has been properly assessed it is out of date.

By improving our data models and connecting the backend of these project management systems, we can automate this process so that it takes just five minutes. This has the potential to significantly improve the speed at which program management can identify and solve problems, making it easier to keep programs on schedule and control costs.

Our data solution – the Advanced Analytics Control Center – has also revealed significant benefits in ease of reporting, allowing clients and stakeholders to easily view authoritative, up-to-date information on program progress.

Technology

If we can get this underlying data infrastructure in place, it will help us to realize the value of solutions like Machine Leaning (ML) and Artificial Intelligence (AI).

ML and AI is predicted to revolutionize our industry. Tech consultancy Gartner predicts 80% of project management tasks will be completed by AI in 2030, with data collection and analysis making up a big part of this. AI also has the potential to help us set up and launch projects faster, dramatically improve our prediction capabilities, and allow us to simulate project performance under a wide range of scenarios.

AI is only just starting to be adopted across the program and project management industry, and its value can be extremely limited where there isn’t the data infrastructure in place to support it. Yet we’ve already seen value in our own business using machine learning algorithms to improve our Advanced Analytics solution’s prediction capabilities, and I’d expect to see more widespread adoption in the future.

People

If we want to realize this potential, we need a real mindset change in the way we approach program and project management.

We are a traditional industry, and it can sometimes be challenging to persuade people to adopt new techniques – particularly when risk is involved. However, with such significant benefits on offer, change is inevitable. As the advantages of this digitally enabled approach become more obvious, more practitioners will begin to embrace the opportunities it can provide, but there is also plenty that individual organizations can do to encourage adoption (more on this in our next newsletter).

We also need to start attracting more talent into program management. Successfully adopting these technologies requires both a willingness to learn and people with expertise in data management and AI. That means working with governments and institutions to attract more graduates and encourage STEM learning, but also working together to upskill and empower our own people. This can be challenging but will really benefit the industry as a whole.

At its core, program management is quite simple, it’s just got a lot of moving parts. And by sorting out our data infrastructure and embracing the value that technology can provide, we are within reach of a future where the majority of projects can be delivered on time and on budget.