Why Managing AI-Centric Projects is Challenging?

Updated on :October 19, 2023
By :Elena Walters

Artificial Intelligence is the toast of every technology-driven organization. Since its inception in 1956, it is a term that has been creating a stir. From banking to IT, AI has impacted every industry and business involved with it. While some people are skeptical about AI’s potential, a majority of them see it as a game-changer. Artificial intelligence is here to stay, but what does it exactly mean for project managers? How will project management software incorporate artificial intelligence and benefit from it? 

Well, project management generally comprises five steps: Initiation, Planning, Execution, Monitoring and Control, and Closing. AI’s capacity to analyze data can deliver real-time insights into these project metrics. It has the potential to allow project managers to make data-driven decisions based on previous experience. AI-centric projects involve Big data, ML, and Business Intelligence. Delivering such projects is challenging as it functions with different strategies and priorities. In a classical project, you have the data, you have the strategy/rules, and you derive the answer to deliver the project; but in AI-centric, you have the data, you have the answer, and you try to derive the rules that will work for your project to become successful.

In this article, we will investigate why managing AI-centric projects is difficult and the steps to overcome them.

Let us begin with having a brief look at how artificial intelligence is used in project management.  

How is AI Used in Project Management?

#1 Automated Risk Estimation

Every project involves risks. In project management, AI can accurately predict the number of defects or general quality. Utilizing AI models at various stages of a project can assist in identifying and alerting teams if the process contains any sort of risk. 

For instance, AI can monitor actual progress and compare it with the planned schedule. Based on this data, AI can give alerts of potential delays, show the underperformance, and suggest methods to bring the project back on track.

#2 Forecasting

Predictive analytics is a technique for forecasting future events. Based on past data and analytics techniques such as statistical modeling and machine learning, it can predict future trends. 

Using predictive analytics, project managers can obtain an understanding of the project’s risks and close any gaps that have been identified. They can prioritize important actions that improve project outcomes and reduce financial losses by improving overall project management.

#3 Adaptive Resource Management

To keep your projects on track, it's necessary to ensure that the right people work on them. AI examines the history of previous projects to provide real-time data on resource management. The manager, for example, can create the project team and assign roles and responsibilities to individual team members. 

Al also helps ensure that project managers manage the project efficiently and meet the deadlines. If there’s a discrepancy between the hours required and the projected availability, they can add extra hands, impart project management training to personnel, or remove people from a project.

AI has endless applications in the field of project management, and its use is only rising. Let’s look at how AI is one of the major breakthroughs and how it’s improving the way project managers carry out their tasks.

The Role of Artificial Intelligence in Improving Project Management

#1 Estimate Project Requirements

The key role of artificial intelligence in project management is improved estimations. Using AI, project managers can easily estimate whether a certain project will be successful, how much time will be required to finish a task, whether it needs more manpower, or just in general whether anything needs to be changed to get it back on track.

#2 Predict Project Risks

Project managers are able to consolidate project data from the past and present by applying AI machine learning. It allows examining all the aspects of a project, from the timeline and resources to the budget and skill level, and identifying areas of risk that may cause delays in the completion of the project. The usage of AI to identify risk allows the team members to work smarter and avoid potential issues.

#3 Manage Project Budgets

AI can help project managers to plan project budgets effectively and manage spending in real-time while also changing the budgets according to the requirements. By studying the previous projects and speculating on new project expenses, AI budget management support can assist firms in planning future initiatives.

#4 Manage Project Teams

The individuals on the team are perhaps one of the most unpredictable and challenging aspects of any project. With limited time to check in on each person and prevent micromanaging, consider using an AI system created specifically for team management. 

#5 Focus on Customer Needs 

Customer grievances and queries can be resolved quickly using AI-powered solutions. It helps address situations effectively, create personalized solutions, deliver a positive experience, and build strong customer relationships. AI tools enable project managers to reduce the strain on the customer service personnel, which leads to better customer handling and increased productivity.

#6 Complete Reliable Projects

AI helps in completing projects reliably with continuous tracking and monitoring. AI systems allow project managers to effectively handle scheduling, reminders, and follow-ups to reduce the need for human input. They can avoid missed deadlines, eliminate resource shortfalls, enhance overall project planning, and gain better business and project alignment for strategically aligned benefit realization.

All these roles that AI plays in improving project management provide a lot of benefits to the project and business as a whole. Some of them are listed below -

Benefits of Artificial Intelligence in Project Management

#1 Predictive Analytics

In order to run a successful strategic initiatives project management office (PMO), it is crucial to refer to lessons learned from previous projects. You can get definite insights into what will work and what won’t and estimate key project outcomes using AI’s capacity to synthesize massive amounts of past datasets across multiple systems of records (SoRs) from numerous projects.

#2 Big Data Analytics

AI helps in the utilization of your data wisely. The most difficult activity in project management is resource allocation and planning. Big data analytics gives you instant access to what’s available, how much more is required, and most importantly when they are needed. This enables pre-emptive handling of resource conflicts. In other words, managing your projects and operating your programs becomes much easier and more profitable.

#3 Insightful Automation

Project managers have to continuously track and monitor actual vs. planned schedules. When done manually, a lot of assumptions come into effect and neither of them is backed with reliable numbers. With the powerful combination of AI and machine learning, this changes drastically. The overall project management posture is improved by the ability to deliver near-accurate productivity rates and time projections based on the study of previous project performance.

#4 Cost Reduction 

There are a number of ways in which AI-centric projects help save money. Better estimations on time and effort help in the reduction of costs. Automation of repetitive tasks aids in the reduction of human effort, resulting in cost savings. The in-depth predictive analysis aids in risk reduction and mitigation, leading to reduced project contingency expenditures and increased revenues.

While AI is growing and gaining popularity fast and has urged businesses to invest in the R&D of AI applications, it is still facing several challenges. Below are the ten most common problems that AI-centric projects face.

10 Reasons Why Managing AI Centric Projects is Challenging

Challenges of Managing AI-Centric Projects

#1 Determining the Right Data Set

The use of high-quality data drives and develops AI systems. This is the reason why the usage of appropriate data sets should be the first step in the AI implementation process. To do so, organizations may need to connect with AI experts who can assist them through the proper path and ways to accomplish the required results and enable revolutionary digital experiences. 

#2 Lack of Technical Knowledge

To integrate, install, and apply AI applications, the company must have a detailed understanding of present AI advancements and technologies, and their drawbacks. The acceptance of this specialty sector in most organizations is hampered by the lack of technical know-how.

At present, only a few companies are facing a smooth ride in the adoption of AI technologies. To identify the loopholes in the deployment process, an enterprise needs specialists. Skilled personnel would also assist the team in tracking the ROI of implementing AI/ML solutions. 

#3 Integration of AI Into Existing Systems

Integrating AI into existing systems and project management workflows is a challenge for many enterprises. In fact, it is the most common challenge faced by enterprises when trying to integrate AI into their systems. Right from contraption to deployment, the help of AI solution providers holding extensive experience in the field of AI will be required for businesses looking for successful implementation of AI into their current systems.

#4 Data Security And Storage

For making intelligent decisions, most AI applications utilize a large amount of data. The disadvantage of using big amounts of data is that it may cause organizations to run out of storage space. Additionally, data-driven automation in enterprise activities might result in data security risks. 

As a result, if companies wish to deploy AI, they must embrace the best and most appropriate data management infrastructure. Such a data management environment will not only increase the security of sensitive data, but it will make it easier for companies to access siloed data of AI and machine learning initiatives too.

#5 Ethical Challenges

Another huge challenge in managing AI-centric projects is related to ethics and morality. The developers’ technological grooming of AI chatbots to the point where they can perfectly simulate human conversations is making it increasingly difficult to differentiate between a computer and a real customer support representative.

Based on the training supplied to it, an AI program makes predictions. The program will label items according to the data assumptions it was trained on. As a result, it will simply ignore the accuracy of data; for example, if the algorithm is trained on data that indicates racism or sexism, the forecast output will reflect this rather than providing unbiased outputs. 

#6 The Required Human Skillset

The lack of skilled data science professionals is a common challenge while implementing AI in businesses. Due to skill shortages, assembling a strong data science team can be costly and time-consuming. Small and medium-sized businesses, in particular, lack the resources to recruit data science and data engineering personnel to help them implement AI.

Companies shouldn’t expect to do much with AI unless they have a highly trained team and business domain expertise. Enterprises must weigh the costs and benefits of forming in-house data science teams versus outsourcing data science and engineering to domain specialists. 

#7 Complex Algorithms and Training of AI Models

AI algorithms have a significant role in the function and performance of business intelligence activities. Enterprises considering AI implementation should have a good understanding of how AI-based solutions or technologies function and how they might improve their results. Once you’ve implemented or produced AI-based algorithms, you’ll notice that maintaining ML or AI models require a  team of skilled AI professionals, which can be difficult for businesses to recruit and retain.

#8 Lack of Computation Speed

AI, deep learning, and machine learning solutions require high-speed computations, which are only available on high-end processors. Requirements for larger infrastructure and costs associated with these processors have become a barricade to AI technology’s extensive adoption.

A cloud computing environment with multiple processors working in parallel is a viable option for meeting these computational needs in this case. The computational speed requirements increase as the amount of data available for processing increases exponentially. 

#9 Legal Challenges

A company may encounter legal issues as a result of an AI application with an incorrect algorithm and data governance. A flawed algorithm created with the wrong set of data can negatively impact an organization’s profit. It will always produce inaccurate and unfavorable results and predictions. 

Weak and poor data governance can lead to issues like data breaches as to an algorithm, a user’s PII (Personal Identifiable Information) acts as a feedstock which may slip into the traps of hackers. 

#10 The Money Factor

When it comes to adopting AI, small and mid-sized companies struggle a lot as it is a costly affair. Even multinational corporations, including Facebook, Apple, Microsoft, Google, and Amazon, set aside a separate budget to adopt and develop AI technologies.

In order to overcome the above challenges and make the use of AI more effective for project managers, the following points can be kept in mind -

How to Manage Your AI-Centric Projects and Overcome the Challenges?

How to Overcome the Challenges of AI-centric Projects?

1. Identify the Business Problem

The first step to take care of when it comes to managing AI projects is identifying the problem. It is crucial to focus on the right business case from the starting point of AI adoption. Seek answers to these questions: “What is that you are willing to solve?” and “What is the desired outcome?”.

A majority of enterprises make the mistake of beginning their Ai project with a need for technology rather than a business need. Also, you have to be specific with regard to each solution. It is important to understand that AI itself is not a solution but a means to meet the requirements.

2. Experiment with models

You should make experiments as you use different machine learning models to solve a problem. Unfortunately, you’ll not be able to reach an acceptable result sometimes. Thus, in AI-powered projects, especially for data and machine learning problems, you should create a detailed risk mitigation plan. 

3. Keep your End-user at the Center of your Project

While experimenting with algorithms and working on finding a solution for your business problem, keep the needs of the end-user at the forefront. Collect the necessary data that is required to find the right solution to your customer’s problems. Understand what the end-user requirements are and circle all the other steps around it. 

4. Preparing and Managing Data

Start by dividing the data into structured and unstructured forms. Although the stage is relatively simple while dealing with a startup or company with limited data, developing various applied AI solutions for enterprises is more difficult. Data engineers should start organizing and cleaning up the data, where in principle, they define a chronological order and apply labels whenever required. 

5. Choosing the Right Algorithm

  • Supervised learning: Choose classification algorithms when you want to understand the chances of an event occurrence, for example, chances of rainfall in the coming week. Go with regression algorithms when you have to quantify the scenario, for example, the chance of an area drowning.

    Several other algorithms can be used by data engineers depending on the project requirement including naive Bayes classification, random forest, logistics regression, and support vector machine.
  • Unsupervised learning: As the data is not organized or of a specific type, the algorithm selection would be considerably different. Clustering algorithms or association algorithms might be used to group objects together and find links between them.

6. Training the Algorithms

After selecting the algorithm, move on to training the model where you put data into the model, keeping the importance of model accuracy into consideration. Setting a minimum acceptable threshold and applying statistical discipline are critical elements in accelerating AI development in such a way that minimal fine-tuning is required afterward. 

7. Seek Experts Advice

For training the algorithms and taking the next developmental steps, employ tech experts who excel in Python, R, Java, and C++. In the project planning stage, take consultation from ML experts to ensure that there are no loopholes in project implementation.

8. Deployment of the Project

For product launch and deployment needs, ready-made AI platforms for project management can be used. These platforms are created to simplify and facilitate AI and assist the deployment phase of an AI project. 

The Future of AI in Project Management

“AI is going to revolutionize how program and portfolio management leaders leverage technology to support their business goals.” —Daniel Stang, Vice President of Project Management Research at Gartner

It’s no surprise that artificial intelligence is growing more popular and being highly used in workplaces than ever before. Project teams have always been under pressure to deliver projects more efficiently and quickly, and this pressure has fueled the creation of the best AI-powered project management tools, which have increasingly automated project managers' work. It speeds up both execution and delivery by teams.

AI-enabled project management technologies aid decision-making and could be critical in attaining successful project management in the near future in the following areas:

AI will have a major impact on the workforce in the near future, including the way people manage projects. A report by Gartner says that approximately 80% of today’s project management tasks will be eliminated by 2030 due to AI. Let’s have a quick glimpse of what the future holds for AI in project management.

Benefits of AI Enabled Project Management Technologies

Better Business Insights

AI-enabled project management tools will offer better insights with more relevant knowledge, help create optimized work schedules, research new trends, and provide recommendations on prioritizing projects and improving portfolios.

Effective Risk Management

In determining risk responses, probabilities, and their impact, AI offers more accurate forecasts than traditional software solutions. AI can offer corrective actions based on past data and track progress in real-time to alert the project manager when risk arises. 

Human Resource Optimization

AI may help with human capital optimization in project management in a plethora of ways, such as integrating smart educational content to fit the needs of the individual at any given time and incorporating this into life-long learning opportunities.

Enhance Benefits of Other Technologies

AI has the potential to revolutionize project management by enhancing the benefits of previous technology advancements like analytics, robotic process automation, the Internet of Things (IoT), blockchain, and quantum computing.

Active Assistance

Status and progress reporting to various stakeholders and upper management is one of the most common tasks carried out by the PMO. AI will aid project managers by automating administrative and tedious tasks, thereby enhancing their abilities. These tasks could involve data entry, preparing or updating project schedules, and generating reports.

To Wrap Up

AI is transforming the way businesses operate, and project management is a key area that has been positively affected. There’s no denying that adopting AI in businesses can be difficult, and you’ll notice these difficulties as you develop an AI strategy for your company. However, the process of implementing AI whether, in a project management software, project planning software, or project tracking software can help you simplify the processes to some extent if you use a step-by-step and planned approach.

A new data-centric mindset, combined with MLOps (Machine Learning Operations) tools that enable industry leaders to participate in the creation, deployment, and maintenance of AI systems, will ensure that all industries can reap the rewards that AI has to offer.

Elena Walters
Elena Walters

Elena Walters, a writer at Goodfirms, is a professional content writer holding more than 4 years of experience in curating varied content pieces for multiple niches. Whether it is web copy, social media posts, or blogs, she has worked on all aspects of writing. You can get in touch with her on [email protected].

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