AI IQ

Once a project begins, keeping it on budget and on schedule is the biggest challenge for project managers. Facing a variety of colorful status dashboards and a myriad of project metrics, what we really need is an indicator of potential future issues. Machine learning algorithms provide early detection of deteriorating budget and schedule performance, and those metrics must be included or prioritized. However, another metric may be of even greater importance and it is based on genetic algorithms, another form of machine learning. The new factor is called stickiness. Although this is a new concept for managing projects, entrepreneurs and new start-up companies often use it to retain customers. In marketing, stickiness refers to the likelihood that your customer will stay with your brand, make repeat purchases, and upgrade to a newer version of the same product.

From a project perspective, a stickiness factor adds resiliency to maintaining project performance. There are two steps in this process. First, the machine learning algorithm identifies the most critical metrics to prevent the project from deteriorating. Once the stickiness metrics are identified, the project manager determines what actions can be taken to increase the probability that those metrics will remain positive. Sticky factors consistently provide benefits to project progress. This might include a project complexity metric or a stakeholder volatility metric. The value is that the algorithm can identify the right metrics for each project rather than relying on a standard set of common metrics.

This might sound like key performance indicators (KPIs), but KPIs tend to be quantifiable metrics for each critical project area, such as budget, schedule, quality, or risk. KPIs are still important, but stickiness is what AI uses to keep the KPIs on track. Project management needs to be open to a new way of thinking. A machine learning algorithm finds the factors that result in a high level of stickiness, and a genetic algorithm determines the best actions to maintain the metrics.

AI is not the only technology being implemented for project management. The next wave of project performance improvement is likely a combination of AI and other technologies. In this blog, I review blockchain, the Internet of Things (IoT), and virtual reality.

Blockchain, known for secure Bitcoin transactions, offers advantages when deployed in the project methodology. Blockchain protects data using a unique yet distributed method for recording data. Blockchain provides a high level of security in transactions because once data is recorded, an approval process is required to change it. Projects can use this characteristic to preserve or share project data. Research into using blockchain for projects reveals that it increases trust, improves stakeholder communication, reduces disputes, and prevents fraud (see references). Making project decisions using AI can be supported by blockchain technology to provide a trusted and transparent process. For example, stakeholders can resolve disputes with contractors based on the analysis of secure data and avoid expensive legal alternatives.

IoT comprises numerous connected devices, such as video cameras and sensors, that share data. Cameras are already used on construction sites to capture images and deliver them to AI algorithms. For construction projects, a camera is embedded in the front of a construction hard hat, and the images are captured as the construction supervisor walks around the site. The images are sent to AI software to determine the project status, detect an excessive use of materials, and identify new project issues.

Virtual reality (VR) can turn managing a project into a video game experience and reinforce decisions that lead to successful project results. The VR world can be combined with digital twin software for projects and generate insight into improving project performance. Adding AI to a virtual project environment might provide early analysis of impending issues with a warning that requires action in the existing project to prevent performance deterioration or enhance the project objectives.

Technology development continues to outpace our ability to assess the value to project management and take action to improve project performance. Two of the most common impediments are the fear of change and the lack of knowledge about the new technologies. The advantage will go to those who address and overcome these issues.

A new field of study known as explainable AI seeks to make AI results easier to understand. A problem with algorithms using regression analysis on large datasets is knowing how the results were obtained.  Neural network algorithms adjust weights internally to achieve the highest correlation to determine the results. The issue is understanding which characteristics of the datasets are most significant.

For example, AI software might indicate that a risk is missing from the risk plan, more training is required for two resources, or a specific change request is highly probable. A stakeholder asks why, and the explanation is not readily available. Explainable AI is a field of study that seeks methods to determine how AI works and how to make the results more transparent.

Here are some methods I use to analyze AI results.

1. Manipulate the features. This is similar to performing a simulation but running the algorithm several times with slightly different data to determine what data is most relevant to the results.
2. The number of datasets used in project management to make predictions is usually smaller than in other disciplines. For smaller datasets, I use a genetic algorithm to determine the data features that have a higher impact on the results. Typically, the software shows several features and indicates the factors that account for over 90 percent of the output.
3. The last possibility is to change the project datasets by adding, reducing, or using different datasets. This is similar to my first point but requires a focus on complete datasets instead of data fields.

Why does it matter?

There are several reasons to be concerned about AI delivering results that project managers don’t understand. One of the reasons is ethical issues such as biased data and determining how bias affects the results. As AI becomes ingrained in project decision-making, explainable AI is required to detect and explain abnormal results. Academics and programmers continue to progress with different approaches, such as measuring the level of trust in the result.

Conclusion

As project managers learn to collaborate with AI software, explainable AI will play a critical role in building confidence that decisions can be taken based on AI results.

References

Hoffman, R. R., Mueller, S. T., Klein, G., & Litman, J. (2018). Metrics for Explainable AI: Challenges and Prospects.

Feiyu Xu, Jun Zhu, Wei Fan, Yangzhou Du, Hans Uszkoreit, & Dongyan Zhao. (2019). Explainable AI: A Brief Survey on History, Research Areas, Approaches and Challenges.

Have you ever finished a project that did not go well and looked back at all the issues and surprises? It would be valuable to know them before the project begins. Prediction is one of the main capabilities of AI software. The objective of using AI is to predict all those surprises and difficult issues before the project begins and allow project managers to evaluate them. A good project manager is proactive. In other words, we want to take action to prevent serious problems before they occur. It's not always possible, but even knowing about them in advance can help prepare the project manager for what to expect.

We already predict many everyday events.  We know that if we go outside when it is raining, we will get wet. We know if we drive through a red light, there might be serious consequences. Predictions are made without much thought because of our experience and knowledge. AI takes prediction to a higher level and makes predictions based on project environmental data that a project manager cannot discern. Projects can be complex, with many factors that are difficult to evaluate.  

Predictions based on AI software use three main methods to predict what will happen on the project.

Supervised learning is based on historical data. The software compares an image of your project to previous successful projects to determine if the same adverse events are likely to happen. The prediction can cover all aspects of managing a project, including resource issues, risks, cost increases, schedule delays, communication problems, quality issues, and unexpected interactions with other projects.

Unsupervised learning does not need historical data. The AI-based software clusters or groups items by project based on their characteristics. The similarity in groupings is used to make helpful project predictions. Risks can be grouped to determine if there is a common cause or if they can be managed with a similar risk response plan. Tasks can be grouped to investigate the level of complexity. Resources can be grouped to assess the variety of technical skills. The objective is to predict the potential for project issues in these areas quickly.

Reinforcement learning relies on events from previous similar projects. This is similar to having a lot of project experience, but the software remembers it all. When issues arise, the software predicts which actions will be ineffective. It might offer a solution to resolve the issue if one is available. If not, at least you know what will likely not work and can look for alternative solutions.

The concept of predictions is to move knowledge of what happened during the project to the beginning of the project or before a critical decision is made so the project manager can decide how to proceed. Several AI software vendors have this capability, and it is a lack of awareness that prevents organizations from taking advantage of the powerful opportunity of prediction.

Genetic algorithms are a software representation of the theory of evolution and can be useful in solving many significant and different issues in project management. Humans reproduce, and the offspring tend to look similar and have similar—but not identical—traits to their parents. There is no typical pattern to this process. Offspring might receive 80 percent of their genes from one parent and 20 percent from the other, or they may receive 65 percent from one and 35 percent from the other. In a further twist of nature, a random percentage can be included, known as a mutation. As with the development of any species, those most able to adapt to the environment survive. This survival concept is known as a fitness factor and is important in the project setting because it is used to represent the project objectives. The genetic algorithm simulates evolution by creating all possible combinations of a solution until the one closest to the desired result is found. 

How is this used in project management? In projects, the objectives are known, typically the scope, budget, and scheduled end date. Given the desired result, the algorithm searches for all possible combinations to achieve the objective. The value is that a genetic algorithm is not constrained by human bias, knowledge, or experience. The algorithm churns through all possible combinations of potential decisions, including solutions that a human project manager may never consider.

A review of research on using genetic algorithms in project management reveals that project scheduling is the most significant opportunity (Ancveire & Poļaka, 2019). The studies describe results for resolving scheduling conflicts, optimizing resource leveling, developing a scheduling strategy, improving critical path planning, and determining solutions to resource constraints.

This is an example where AI-based algorithms can solve numerous project issues. Collaboration is required between project managers and software developers to find ways to unleash the power of genetic algorithms to significantly improve project performance. These types of algorithms can be more complex to understand but are an example of another wave of machine learning that is available to solve project issues.

 Reference

Ancveire, I., & Poļaka, I. (2019). Application of Genetic Algorithms for Decision-Making in Project Management: A Literature Review. Information Technology & Management Science, 22, 22–31.