AI’s role in project management? A question of judgment

At the turn of the decade, Gartner predicted that by 2030, 80% of all project management tasks would be automated. It’s still too early to know whether this will be the case, but the rise of AI in the years since Gartner made this projection gives CIOs and project managers adequate food for thought. For instance, will project management aided and automated by AI create a new kind of 80-20 rule, and if so, what 20% of project management will still be performed by human project managers?

Answering that requires breaking down the key elements of project management and understanding what about them is necessarily human.

IBM offers some possible answers on balancing AI with human oversight: “[Project management] AI tools offer useful support, but they do not fully replace human judgment. Organizations must ensure proper human engagement and governance, and review AI-driven recommendations critically to ensure they’re in line with company policy and goals. Responsible implementation of AI requires communicating clearly to all stakeholders, conducting thorough risk assessments and investing in tools that adhere to best practices in data security.”

But that only scratches the surface.

The value of experience

As a veteran CIO and IT project manager, I’ve shared war stories with others in these roles, and I can confidently attest that we all agree that the biggest jobs of a project manager are communicating and making personal rounds with project stakeholders and staff members who have the potential to pull the plug or sabotage a project in an instant. A third would be making sure the information we base our decisions on is right.

Here are several examples from real-life situations:

A major computer vendor releases a new version of its operating system for its hardware and the OS crashes. The culprit: a single software module within the OS that someone forgot to compile. Would AI have drilled deeply enough and gathered enough conclusive information to see that a small module of software had not been compiled, and that it would bring the OS down? Possibly — if the vendor had skipped a QA of the module, the AI could check the project management checklist and catch that the module had not been quality-checked, for example — but we don’t really know.

A back-office order-entry manager jumps the gun and purchases a new order-entry system without consulting his staff. IT is asked to integrate and implement the system. The project drags on for over a year, with no clear cutover date in sight. Project tasks are getting checked off, but still the project doesn’t seem close. There seems to be an endless number of enhancement requests coming in from users, and many say the system will “never work.” The real culprit? Order-entry staff wasn’t included upfront in the system decision process. Is this a case of “soft” sabotage? Hard to tell — and even harder for AI to know — but an astute and experienced human project manager likely would know.

A stock brokerage system project is more than a year overdue and millions of dollars over budget. The real head-scratcher is that the project seems close to being finished because tasks have been checked off the list. The CIO gets a gut feeling that things aren’t really as they should be. He starts walking around and asking questions — not of his project manager, but of project staff members. They tell him they are still working on many of the tasks that have been represented as being complete. He confronts the project manager and discovers that the manager has been falsifying project progress because he was over his head and afraid he would lose his job. Could AI have uncovered this deception? Possibly — but AI can only operate on the data it is fed. It would likely take a real human (in this case, the CIO) to understand the project manager’s fear of failure and sense the project was having undocumented problems.

Where AI can help

Given these needs for human judgment, management, communication, and intervention, just whatcan AI do for project management?

AI can provide a natural extension for many of the forms of automation that project management software already has. Here are several key examples.

1. Plan for system dependencies. For software development projects, a plethora of mature tools can already detect what underlying IT infrastructure or systems may be impacted by a new project. Further downstream detection of underlying compatibility issues for a new application can also be uncovered during software regression testing. When added to these tools, AI will be able to deliver even more precision, and possibly even a certain amount of self-healing that can auto-solve detected incompatibilities without IT having to do it manually.

2. Plan for project task dependencies. Planning projects for task dependencies (i.e., which project tasks must be completed before others can be started) is an arduous, laborious, and often manual process. With enough information, AI can produce a “first cut” draft of the project task list and identify task dependencies. This would give IT the ability to review and revise a draft instead of having to develop it from scratch.

3. Monitor for risk. Project management software already evaluates for risk and issues risk alerts for project tasks and paths, but AI could probe deeper using additional rules the project manager defines for risk. The AI could also assist in risk mitigation by assessing a situation and making recommendations for getting a project back on track.

4. Look for talent. Companies (and IT departments) have made major strides in recent years by digitalizing employee skillsets across the enterprise. AI could review these digital repositories and identify across departments any employees who have the requisite skills to step in for a project task if the primary person assigned to the task becomes unavailable.

5. Perform project what-if scenarios. What if a vendor that is integral to your project postpones a deliverable? Or if one of your key performers leaves for other employment? This is where AI can be extremely useful in evaluating a given what-if scenario, projecting likely outcomes and assessing how to navigate them.

The upper limit of automation

Project management contains many intuitive elements that humans must decide, no matter how much automation and information AI delivers.

It is why the CIO of a European payment processor shared with me recently that he could now fully automate the failover process for his payment service, with AI even making the call to failover and then executing it — but he still insisted on being “the one to press the button.”

Perhaps over time, humans will become comfortable handing off this responsibility to AI. Until then, however, expect project management to keep a human touch.