AI in Project Management: How to Identify Your Most Effective Project Managers

While the hypothetical and exciting world of artificial intelligence (AI) in consulting has been abundantly covered, there’s little about AI in project management and how the advancement of AI can be a boon to delivery organizations. Which got us thinking. What is the current state of artificial intelligence and machine learning in project management and how can it be used as a way to improve success rates?

Could machine learning and AI in project management identify the most effective project managers (PMs) on your staff? Can professional development dollars have a greater impact when artificial intelligence helps inform investment? Moreover, can AI techniques help pair PMs with the projects they are the most likely to be successful at delivering?

We looked to answer a few of these questions with one of our clients—read on to learn about the tools we used, the AI driven analysis, the profiles of project managers that were identified, and the future of AI and project management.

Use Case for AI Tools in Project Management

We recently spoke with a client who uses Projector PSA (Professional Services Automation). Our conversation turned to some of the advantages of tracking historical time in the same system as they forecast projected resource schedules. In this organization, each project manager is responsible for identifying the staffing needs for their projects.

As we began to analyze the data, we realized that each PM’s resourcing forecast, as compared to the work their team actually did, was quite unique. So unique, in fact, that we found that we could identify individual project managers by looking at their forecasting “fingerprints.”

From there, we thought it would be interesting to group these unique patterns into five different base profiles. To make the analysis of the markers less manual, we turned to some AI tools for project management to analyze the data based on the project managers’ plans.

In particular, we taught a neural network-based machine learning algorithm to analyze the fingerprints and to identify the base profile that best described each project manager.

Analyzing Project Plans Using Artificial Intelligence in Project Management

Delivery managers can use a myriad of different metrics to measure how successful their PMs are. The data we looked at in this instance was how accurate and consistent each PM was at forecasting their projects. For several months, we studied how each PM projected their projects’ resourcing needs for the next 12 weeks into the future. Once those weeks passed, we compared the forecast against the actual hours that the project teams reported in Projector.

Using some basic statistical analysis, we produced a series of graphs to show the average number of actual hours reported (the solid white line) as compared to the baseline of the PM’s forecasts (the solid yellow axis). We also plotted the variation (the dotted white lines, representing the ±1σ and ±2σ confidence intervals) to get a sense of how tight each PM’s projections were. Finally, we compared actuals against projections one week into the future, two weeks into the future, all the way to 12 weeks into the future, and came up with something like this:

ai in project management

We then took a look at a handful of these graphs and grouped similar-looking ones into different categories. This became our training set that we fed into our machine learning mechanism. We then turned that AI algorithm loose on a wider data set to categorize all the remaining project managers.

In doing so we created an AI-based project management tool, taking the project resource plan forecasting and time tracking in Projector, and applying machine learning to understand accuracy to understand which PMs are most likely to fall into one of 5 project manager profiles.

AI and Project Managers – Get to Know the Team

The Deliverer

The first profile we happened to look at we named Deliverers. These PMs were the bread and butter of the organization—diligent, thoughtful, realistic planners who kept their resourcing needs up to date. They had good visibility into what their team needed to work on, especially on a six to eight week planning horizon. Deliverers also were eminently capable of dealing with the inevitable surprises as they came up.

Deliverers often were not working on the highest risk, most complex, or longest running projects. The short-term nature of their projects led to an increase in variability in the 11- to 12-week planning horizon. As such, they were largely responsible for the short- to mid-term predictability that the organization was looking for.

The Hoarder

Our second profile emerged quickly. In this profile, the white line (average actual hours) consistently sat below the yellow line (projected hours) at all planning horizons. We also saw a wide variation that didn’t change much, whether in the near term or far in the future (the dotted white lines). Looking closer at this profile, we identified PMs that were less experienced than the Deliverer cohort.

We characterized this set of project managers as Hoarders. Hoarders were a little less adept at risk identification and risk mitigation. As a result, they were more prone to allowing surprises to throw off their planning and execution. This in turn, led to the large variation.

On the plus side, Hoarders had a sense of the impact unidentified and unmanaged risks could have on their projects. As such, they tended to be overly conservative when defining resourcing needs. This showed up as the consistent difference between average actual versus projected hours.

This tendency to project higher resourcing needs than what was actually needed helped ensure successful delivery of projects managed by Hoarders. However, this conservatism wreaked havoc with the organization’s overall utilization. Resources spoken for by Hoarders “just in case” appeared unavailable to work on other projects. They then ended up underutilized when those worst-case scenarios didn’t play out.

The Optimist

Like our previous profile, Optimists tended to be less experienced than Deliverers. This resulted in some of the same wide variations as Hoarders. Optimists, however, tended to use resources more than they planned because, unlike Hoarders, they tended to not anticipate risks at all. Rather, they assumed that everything would go according to plan. This meant that their teams constantly found themselves in unplanned firefighting mode.

What is ironic with Optimists is that many organizations help them to develop professionally by providing tools, methodologies, and training to help identify and surface risks. As they develop better risk identification skills, however, Optimists are in danger of turning into Hoarders unless they simultaneously develop risk mitigation and management capabilities.

The Controller

So, as we’ve seen with some of the previous profiles, too much variation and too large a consistent difference between projections and actuals is a bad thing. That must mean that zero variation and zero difference is good, right?

Maybe not. We identified a Controller. The Controller’s data was unusual, to say the least. Their projections were always perfect. Their teams always reported hours exactly equal to what they predicted. They never had over budget projects (and were never under budget, for that matter).

While there was some speculation about whether they were truly psychic, after some further research, we found out that they had a unique way of managing their teams. They sent out a weekly email to each team member with the number of hours that person was scheduled to work on their projects for the week. The email was, of course, conveniently timed to arrive just before the week’s time entry reporting deadline.

No wonder they were never over budget. But, they (and the organization as a whole) were also prevented from learning anything about better estimation, delivery, or risk management techniques. Their project management style was effectively encouraging their teams to lie about what work they truly did.

AI project management

The Fixer

Finally, we came upon a profile characterized by huge variation that spoke to an inability to forecast resourcing needs with any level of accuracy. A first instinct might be to recommend remedial estimation, planning, and risk management training. Possibly adding in domain familiarization to help them better understand the projects they were managing.

But here is where using artificial intelligence in project management can be tricky.

Turns out, these were already the best trained, most thoroughly ramped, most highly experienced PMs in the company. They were the managers that the organization pointed at projects that were already in trouble. They were the veteran PMs expected to help delivery teams get themselves out of a tight spot. They were the people who knew how to roll up their sleeves, get their hands dirty, and get a project back on track. They were the Fixers.

Rather than expecting to be put on personal performance improvement plans, Fixers were often the PMs who could expect the largest bonuses at the end of the year.

The Future of AI in Project Management

So, what’s the bottom line on using AI in project management to identify effective project managers? Obviously, the organization should be looking at much more than these forecasting accuracy profiles to make hiring, firing, staffing, bonusing, and professional development decisions. And that’s exactly the recommendation we suggest when looking at using predictive data models for capacity planning.

What this exercise did do, however, was point to some of the real benefits that arise from managing time tracking and resource scheduling in the same system. These profiles, along with the machine learning and AI in project management categorization models used in this exercise, may help identify where training is needed or where particular expertise is hidden. Incorporated into the right projection models, these profiles may even help to identify employee attrition risk.

Finally, the approach also pointed out some of the potential pitfalls of relying solely on data without having a deep understanding of the business, the people, and the culture. In particular, it highlights how much of a human endeavor the services industry is and that the secret to effective professional services resource management is as much an art as it is a science. At the end of the day, achieving the right outcome is a combination of having the right plan, the right tools, and the right team.

Learning More

If you’re interested in learning more about PSA software for services organizations, take a look at our recently published e-book, Professional Services Automation: A Quick Primer. In it, you’ll find additional information about how Professional Services Automation solutions can improve the performance of a services organization. You’ll also see information about some of the decision points you’ll need to make when selecting a PSA tool.


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Frequently Asked Questions About AI in Project Management

How AI is used in project management?

AI can be used in project management to make data-driven decisions. For example, if you want to know how long a project will take, you can use AI to analyze historical data and give you an estimate.

How AI can improve project management?

AI can help with project management by providing a more streamlined, automated, and optimized workflow.

Will AI take over project management?

Achieving optimal outcomes for project delivery requires a combination of human experience and intelligence, the right technology, and the right team. AI can assist project managers in doing their jobs more effectively.

What is automation in project management?

Automation in project management is a way to streamline your processes and make them more efficient. It can take the form of software or hardware that helps you manage your team’s progress, track time, and measure progress against milestones.

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