Applications of AI in Project Management

Last updated on October 12, 2018

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Artificial Intelligence (AI) is a term that’s been creating ripples ever since it was first contrived in 1956. Because of it’s proven ability for helping businesses prosper, many firms are tapping into its potential to automate mundane tasks. Since then, it’s slowly spilling into every sphere, broadening the scope of what was once deemed impossible.
So, you must be wondering; what use are you going to get out of knowing how AI runs in the background?

The answer lies in the efficiency and speed with which you can accomplish so much more. What’s more, you don’t waste time on futile projects that delay schedules and overrun the budget allocated.

That being said, many people fear being replaced by technology, explaining their reluctance to learn more about what AI really does. However, life gets easier, especially for a project manager, when you base your project estimates on precise numbers.
Besides learning what we teach it, it also helps us learn from the mistakes it makes. Each mistake is a stepping stone to the next groundbreaking discovery.

It’s high time we dispel the negativity around AI with some useful applications it poses to project planning and management!

An image depicting the possible applications of Artificial Intelligence in Project Management.

AI in Project Planning

The human brain is a multi-layered powerhouse of knowledge. Each one of us has distinct cognitive ability that differentiates us from one another. Which explains why no two project managers think alike. This very ability to interpret information uniquely has spurred on the computing power of artificial intelligence. After all, any machine replicating the human intelligence has to factor in different permutations that the average human brain’s reasoning will take.

Here’re some applications of AI that aid in the project planning cycle :

1. Knowledge Based Expert Systems

As an experienced project manager, you’re already aware that sudden changes to project requirements can cause failure. A KBE system consists of a knowledge base and inference engine.
It makes use of a series of IF-THEN statements, which give you mission- critical conclusions for the planning and scheduling phases of a project. A project manager’s experiential knowledge can be fed into the KBES. The system can then accurately estimate project duration and resource requirements for several project activities.

2. Artificial Neural Networks (ANN)

ANN finds heavy use in construction projects. For one, it predicts cost overruns based on several parameters, which are project size, contract type, and the competence of project managers. It automates project activity sequencing based on the functional requirements provided in the project initiation document.

3. Fuzzy Logic

Loosely based on the Boolean true or false logic, Fuzzy determines the project priorities in the Portfolio management processes. It’s widely used in construction projects to optimize cost-time trade offs. This works out favorably for construction project managers who want the least cost routing to work out the logistics of having material supplied on-site. Plus, you can model probability distributions to assess risks in construction projects.

An image showing an infographic of applications of AI in project management

How AI helps Project Managers

Project managers can turn wayward projects around by adding applied intelligence to performance monitoring tools. This way, you can track progress and be warned of potential risks that threaten project delivery. What’s more, you get to rule out projects devoid of any profitable outcome.
With your main goal being to avoid any surprises as you near the end of project delivery, the 3 areas where AI provides conflict-resolution are:

1. Risk Estimation

You can factor in budgeting and scheduling constraints to make informed decisions on risk management. And when you start out, healthy completion levels seem likely for your project. However, in reality, project interdependencies and external environment (like changing market trends) can result in different probabilistic scenarios. As humans, we’re limited by our capacity to store and reproduce information at will.

With Machine learning, however, you can retrieve parametric information as and when required. For instance, you can can use past data such as planned start and end dates to predict realistic timelines for future projects. The system can add an upper and lower bound to these dates to account for delays within reason. If the system indicates high confidence in a particular project, successful delivery is guaranteed. .

2. Resource Management

To ensure your projects remain on track, it is essential that the right people work on them. AI delves into the history of past projects, which give you real-time information on resource engagement. Based on this, you’d know if your resources are ready to be deployed. You can even add extra hands or take people off the project if a disparity rises in the hours required versus projected availability.

AI helps your staff remotely access real-life training material which helps them enhance their skills and knowledge quickly. This reduces the time taken to onboard them on to new projects. As a result, project delivery is quickened with your clients gaining clarity on project deployments.

Resource optimization is an important consideration when reshaping your enterprise structure. By allocating optimal workloads, you ensure that your staff’s utilization rates remain at a healthy 100% all year long. Moreover, when repetitive tasks are automated, your staff are left with more time to make project-centric decisions that positively influence its delivery.

3. Predictive Analytics:

Intuitive forecasting is a statistical approach that validates your project. Not only does it point you to the right number and type of resources (both human and technical) needed, but also reduces labor costs. The project team you create can then diagnose and solve technical and/or personal conflicts that arise.

Additionally, predictive analytical tools contain the exception handling feature, which points you to an excess or shortage of the right resources.
The idea behind predictive forecasting is to strategically identify and block risks before they take over the project. This helps you prepare the optimum project schedule.

We’ve arrived at a stage in history where we’ve benefited directly from the reaches of AI. For one, more tasks can be completed in a shorter time frame. With data at the heart of everything, AI tools like a data warehouse has the storage capacity to hold more information than ever.

For example, BIM 360 Field is used in construction to capture photographic evidence of project sites. This is handy when your construction project teams have to conduct terrain feasibility and environmental impact studies. similarly, the Internet of Things(IoT) is an emerging market that’s disrupting the automobile industry. With over 200 million cars slated to be connected to the internet by 2020, manufacturers can use performance data to detect and repair hardware issues. With vehicular safety of paramount importance, automobile project managers can enhance fleet management with the use of smart sensors.
When it comes to AI, the sky’s the limit!

Is your curiosity over the possibilities of nuanced IT Resource Management getting the better of you?  Reach out to us and we’ll be happy to assist you with more information!

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Namratha Mohan

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