Will intelligent technology make project management software obsolete? If not, which tasks will be automated, and which ones will be managed by people?
The answers to these questions aren’t straightforward and require an in-depth analysis of how new and old technology will impact project management.
Project management and intelligent technologies need each other
Projects and processes were defined for humans and adapted to fit the software, not the other way around. We expect the same to happen with automation. It’s unlikely that robots and AI will be programmed to adapt to us since it’s much easier for us to change the way we work. Humans can quickly adapt, and most employees are easier to replace than robots or AI. As a result, it’s likely that automation will take over task management while humans will focus on higher-level project strategy and optimization.
The importance of project management as a business strategy will increase as traditional manufacturing companies focus more on services. For instance, a manufacturer of industrial lighting equipment may choose to provide services to install its products in warehouses or retail stores. While automation can cover the production of equipment, its installation still requires human involvement.
AI and automation should help project managers focus more on decision making, and less on repetitive tasks for project monitoring. This project management approach is ideal in theory, but brings about a few critical challenges.
Such challenges include:
Where does human intervention stop, and automation takes over?
Who decides to make changes when something unexpected happens?
When should AI override human decisions, and vice-versa?
Let’s take a look at these challenges and see how different types of technology and business strategies could address them.
AI can theoretically do everything a project manager does; in practice, AI isn’t great at handling the unpredictable. Since unexpected changes happen all the time in project management, AI needs human intervention to adjust.
Here’s a scenario of an infrastructure project that has three phases: design, planning, and execution. Let’s say that tasks T2.2 and T3.1 require the involvement of a contractor. If the contractor declares bankruptcy in phase 2, it may be easily replaced with another company. But what if this happens in phase 3?
If the new contractor is too expensive, try to manage the work internally. This may not impact deadlines and profitability, but the quality of the project could be compromised.
Redo phase 2 with the new contractor to make sure they’re ready for phase 3. This would delay the project, but the quality wouldn’t be compromised.
If they can’t find another reliable and affordable contractor, it may be preferable to lose the project than to lose the customer by promising something they can’t deliver.
AI may not be able to choose between these options because it needs more information to make decisions. It can recommend a few contractors by searching a database of thousands of companies, gathering data, or through data analysis from multiple sources. For the third option, AI will need data on the customer and similar types of projects to make a recommendation. AI may not know the personality of the managers in charge of project management on both sides and their work (maybe even personal) relationship can have a significant impact on the decision.
Humans have knowledge, experience, and intuition, which helps in the decision-making process without analyzing large volumes of data. We don’t always make the best decisions, but it’s sometimes better to react fast than to wait for an ideal solution.
On the bright side, one of the essential benefits of AI in project management is its ability to challenge our assumptions and subjectivity. A fascinating example is the use of natural language processing (NLP) to analyze how employees phrase status updates, which can provide insights into their level of confidence regarding the progress of a project.
The term co-bots refers to collaborative robots, which is a bit misleading because they’re not exactly collaborating with humans. Their main advantage is that they interfere less with human work, or: do not need to be secured behind a cage to keep humans in the workplace safe. While that’s great, it’s not collaboration.
Project collaboration, in an ideal situation, would be when robots and humans work together, sharing tasks, synchronizing their work, and helping each other. When people and robots collaborate, they can perform at a higher level than full automation. Research teams from the universities of Göttingen, Duisburg-Essen, and Trier observed that cooperation between humans and machines works better than just human or just robot teams.
The way humans and intelligent technology work together depends on the level of acceptance of AI or robots. As shown below, the acceptance varies significantly based on the levels of resistance to technology and personal intrusion. At this point, we are somewhere between accepting competence and accepting the decision of intelligent technology.
Internet of Things (IoT) has the potential to process orders from multiple locations and individuals instantly. For instance, decision-makers from different locations can remotely control a production facility, which is a great solution to cut project costs and improve output. The challenge is that fully automated factories aren’t always so great at dealing with conflicting instructions.
Here’s an example of three conflicting production orders:
Use raw materials that are about to expire
Make goods that need to be delivered as soon as possible
Start working on an urgent order from a priority customer
If all three managers place a production order at the same time through the IoT network, how will they be prioritized? The first received, even though there’s only a gap of seconds between orders? Maybe the one that has the highest value?
While all these details are usually analyzed during the production planning phase, errors may occur, especially when companies use multiple ERP systems for manufacturing. Also, priorities may change while new production plans are implemented.
What usually happens in these cases is that three managers try to negotiate or involve their superior to make a decision. How would technology make a decision? Recent research from MIT shows some concerning results regarding the way self-driving cars may choose who dies in a fatal crash (see below).
If similar technology is used in industrial environments, how will AI decide between a person and an expensive piece of equipment?
The good news is that automation makes it easier for people to react quickly to potentially dangerous situations. Real-time data and alerts are extremely valuable in industries like mining, manufacturing, and oil and gas. AI and the data generated through IoT networks can help companies predict incidents and work to proactively mitigate accidents. Finally, instant communication and data transfer allow supervisors to remotely shut down a fixed asset.
How will project management and intelligent technology impact each other
There are many things we don’t know about automation and its impact on our work. One thing we do know is that project management will change dramatically. Here are some trends we expect to see in the near future:
Automation will perform some repetitive tasks, significantly impacting all employees who don’t play a strategic role in project management. Automation will replace task management software, which will only be used by small companies who cannot afford to invest in automation.
As automation takes over simple projects, companies will have more resources to focus on complex ones, as well as project portfolios. We expect to see more project management office (PMO) teams across all industries, requiring experienced project managers. Standards, methodologies, andrisk management will also become critical since companies can’t afford to make mistakes.
While the role of automation becomes more apparent, employees must adjust. Unfortunately, we don’t know what adjustment actually means since the jobs created by automation require different experiences and skills than the jobs automation replaces.
Intelligent technology learns by practicing. Simulations and theoretical calculations are instrumental in defining how the technology works, but only real-life experience will allow this technology to learn and adapt.
Gabriel’s background includes more than 15 years of experience in all aspects of business software selection and implementation. His research work has involved detailed functional analyses of software vendors from various areas such as ERP, CRM, and HCM.
Gheorghiu holds a Bachelor of Arts in business administration from the Academy of Economic Studies in Bucharest (Romania), and a master's degree in territorial project management from Université Paris XII Val de Marne (France).