Thought Leaders

Why The Secret To Successful Project Management Is In AI

artificial intelligence

Project Managers are well aware of the effect that even small delays and time mismanagement can have on whether a project runs on track for success, or whether teams are met with unanticipated setbacks that sacrifice productivity and morale.

Yet, for some reason, they largely accept many of these delays and inefficiencies as inevitable. What if this wasn’t the case?

While there are many project management tools on the market, those that are enhanced with Artificial Intelligence (AI) and Machine Learning (ML) capabilities are proving to be extremely useful in giving project managers those all-important early warning signs that a deadline might be missed – and even helping them to avoid missing them altogether.

Whether the industry is software development, construction, or finance, every company can seriously benefit from these kind of predictive insights.

In fact, according to Gartner, AI will have generated $2.9 trillion in business value by 2021. Clearly, this is a technology that project teams need to get on board with — and fast.

Technologies that incorporate AI and ML are enhancing project managers’ capabilities by predicting when tasks can be finished, notifying them when a project is likely to run behind schedule, and even automating low-value administrative tasks to improve efficiency.

Far from threatening the role of the Project Manager, AI and ML are working wonders for it, and should be embraced by forward-thinking organisations that want to gain levels of awareness and productivity that they’ve never experienced before.

Current cracks in Project Management

The biggest problem many Project Managers face today is a lack of awareness of any issues or bottlenecks that may be arising. Project Managers can only act on the information they have, and it’s not uncommon for team members to encounter a potential problem, but not bring it to the attention of their colleagues or the Project Manager. This means the issue can snowball until it’s too late to rectify it.

Even the delay of a small task could impact the progress of the whole project if not addressed as soon as the issue is uncovered. And the earlier you know about the problem, the easier it is to solve. It’s important to remember here that Project Management is not an exact science, teams can predict how long each task will take but this is often not the reality. So, the need for early warning and flexibility is absolutely key.

Another key issue that Project Managers are encountering across industries is devoting too much of their attention to low-value activities. According to this Accenture study, on average managers actually spend 54 percent of their time on project management-related administrative tasks.

Taking these tasks out of Project Managers’ hands and passing them along to new technologies gives them the opportunity to focus on where their efforts are really required.

Are Project Manager roles in danger of being automated?

AI and ML are still new in project management, so a lot of teams are wary of the impact they could have on workplace dynamics. In many cases, employees are nervous about their jobs being automated and themselves being rendered obsolete.

However, AI is not going to replace the jobs of Project Managers any time soon. Project management is about human connection, leadership, and strategic thinking outside-the-box — skills only a human can provide. Technologies like AI and ML are here to augment, not replace, Projects Managers

They need to work in tandem with human intelligence, as AI alone does not guarantee success. But when they are deployed purposefully, AI and ML can act as an accelerator for projects, ultimately driving success rates and keeping teams on track with tasks to hit those all-important deadlines.

AI and ML-aided planning and forecasting

The true power of ML is its ability to find patterns and relationships between events and actions by processing enormous amounts of data and analysing dozens of variables. In the context of project management, the data and variables used by ML can include: who is working on a project, their past performance history, how quickly tasks are being closed, who is the manager, along with the history of similar projects and the project message history.

After processing and analysing all of this data (ideally at least 6-8 months’ worth), the ML-powered solution can inform the Project Manager of the most likely project end date and its confidence in this prediction. Even just having a range of several days in which a Project Manager can expect a project to be completed is hugely beneficial.

They can see early on if there is a high probability of missing their initially planned deadline, allowing them to take corrective actions and potentially save their organisation thousands of pounds in contractual obligations, overtime payments, and unhappy clients.

AI can also help teams avoid snowballing problems by identifying bottlenecks within processes, bringing these to the attention of Project Managers, who can then act on them to avoid serious delays down the line.

For example, the technology may show the Project Manager that during the legal approval stage in April there’s usually a high chance of a delay, meaning it’s better to initiate the legal review earlier to minimise risk and avoid overall project delay.

AI systems can also help Project Managers in their decision-making by clearing through the abundance of data and showing them trends that could be critical to the organisation, but are hard for humans to identify on their own. The same can be done with resource engagement: AI can indicate to Project Managers at which stages of the project they are likely to need to scale up or down their resources, again allowing for optimised planning.

In addition, Machine Learning has the power to help Project Managers ensure that they have the right people working on their projects. Getting the best team for the job at hand is crucial to making sure projects remain on track and are executed successfully. Here, ML can again delve into the history of past projects to figure out who is best for what role based on the success of past tasks that they worked on.

AI for improved efficiency

A significant amount of the work that Project Managers do is routine checking on the status of tasks, be it by email, phone, or in-person, or even arranging meetings themselves. Small tasks like these add up to a huge amount of the Project Manager’s time, taking it away from higher-value activities like team management and leadership, or strategic planning.

Luckily, AI solutions exist that can automate these tasks. For example, an AI-powered chatbot can act on the Project Manager’s behalf, and contact each team member to confirm the status of tasks and ask about any potential problems.

The use of these systems not only saves team members’ time, but it also eliminates human error and helps to ensure no stone is left unturned on the status of tasks and possible problems.

Not only this, but intelligent bots are also able to automate duties such as project documentation and quality check activities. Again, this removes the potential for human error, and drastically cuts costs and time spent on low-level administrative tasks.

Far from automating away the crucial roles of managers and their team members, AI and ML are here to give them invaluable predictive capabilities, and take off their hands tasks that don’t require skilled attention.

And with the rapid rise of AI and ML across countless industries and organisations, Project Managers that don’t want to get left behind have no reason not to integrate these technologies to improve productivity and efficiency across the board.

Vadim Katcherovski is CEO of Easy Projects.

Vadim Katcherovski
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