AI IQ

Supervised learning is a commonly used technique in AI-based machine learning. This type of algorithm has two main capabilities: prediction and classification. Prediction occurs when the algorithm receives labeled datasets to predict an outcome. For example, a large number of images labeled as either a cat or a dog is provided as input to an algorithm, and the algorithm builds a model of what a cat is supposed to look like and what a dog is supposed to look like. Now the algorithm can accurately classify a new unlabeled image as a cat or dog. The capability is beyond simply matching, as the software learns all variations to arrive at the correct result.

Think of a project as an image. All the characteristics of a completed project are input to an algorithm, and the project is labeled either a success or failure. The algorithm builds an image of what a successful project looks like. A new project can then be evaluated to verify how close it is to the image of a successful project. Typically, the output is a probability. If the probability is above 90 percent, the project is set up for success. If the probability is lower, several actions can be taken. An organization may want to add the probability of success as another project selection criterion. The starting image of the project does not guarantee success since projects can encounter variances very quickly. Supervised learning can also be used to proactively minimize variances or determine how to resolve them.

There are many situations when an accurate prediction is useful. A machine learning algorithm can predict whether a risk will occur on a project or, even more importantly, whether the risk will cause project failure. An algorithm can predict communication issues, stakeholder management problems, and numerous other potentially failure-inducing situations. Supervised learning allows proactive measures to be taken to keep the project on a path to success.

As evident from the descriptions above, historical data is crucial for an algorithm to create a model. Supervised learning normally requires a significant amount of data to perform accurately. In the field of medicine, vast amounts of datasets are collected. In the world of project management, it is yet to be determined what amount of data is sufficient. Some academic research suggests that as few as 50 datasets are acceptable. However, the number of characteristics captured in each dataset is also essential. The theory that AI requires large amounts of data is based on supervised learning, yet it is not necessarily true for other AI capabilities.

Computers have surpassed humans in the ability to store and process data. Supervised learning is a clever concept that can be effectively deployed to improve project performance.

An explanation of the basic functionality is required to understand how AI can be used in project management. While there is no clear definition of AI with boundaries and limits, describing what AI looks like and how it can be used is helpful. AI for project management has two main components: machine learning (ML) and natural language programming (NLP).

Machine Learning

ML is software that uses datasets to create a model, and based on that model, the algorithm produces a prediction or performs classification. Prediction is the probability that the answer or decision is correct. The results are valid for a single point in time but need to be updated with new data as the project progresses or the data changes. What can you predict? Anything where you have data such as overall project success, quality results, or the probability of achieving the budget.

For ML, the most common learning models are supervised learning, unsupervised learning, and reinforcement learning. These algorithms are incorporated into the software used by organizations to improve project performance.

Natural Language Processing

NLP converts words to data and is extremely useful for document analysis, sentiment analysis, translation, and using a virtual assistant. NLP reviews documents for accuracy and consistency. Sentiment analysis checks verbal or text input and identifies positive or negative feelings in the project team. There may be ethical issues if this is not implemented and used correctly. Virtual assistants determine the intent of a request and deliver a response. Commonly used virtual assistants are Siri, Alexa, or Google Assistant. NLP is also at the forefront of recent developments with large language models (LLMS) such as ChatGPT, Llama, and Bard. However, large language models use NLP and ML algorithms to achieve their amazing capability. The value of NLP is the ability to extract insights from large amounts of data, such as a database of historical projects.

Summary

ML and NLP are valuable AI components that can be combined when using LLMs. Further blogs will review how these algorithms work and how the project methodology is changing to take advantage of this opportunity.

The scope statement defines the product, service, and results to be delivered by a project. The scope statement outlines the boundaries for the project work and provides the input to create the budget and schedule. AI is applied to scope for two primary purposes: to help create the scope statement and to review the content for consistency, checking for errors and omissions.

As Professor Bent Flyvbjerg points out in his book How Big Things Get Done, in an analysis of 258 projects with a budget over $1 billion, only 8.5 percent met the budget and schedule, and a minuscule 0.5 percent were delivered on time, within the budget and achieved the expected benefit. These results suggest that the project methodology needs to be reconsidered. Fortunately, new technology, specifically AI, offers hope for improving project results, and it starts with the scope statement. Both agile and waterfall projects can benefit.

Agile

For agile projects, a study by Accenture found that the root cause of 35 percent of defects in production was due to errors in the requirements document. Based on the wording of user stories, natural language processing (NLP) tools find errors and omissions. For example, the requirements might define the details of a feature, but that feature is never tested. Alternatively, there may be testing for a feature that is only vaguely defined. AI-based scope review algorithms find these inconsistencies up to 20 times faster than a human and are especially useful for requirements that are hundreds of pages long. This is not a distant reality or exaggeration. A government department recently created a software consolidation project, moving the functionality from a variety of different applications into a single software solution. A previous similar project was significantly over budget and schedule. However, the main issue was that it did not perform as expected, creating negative public criticism. For the current software consolidation project, the government acquired an AI-based NLP tool to thoroughly review and correct the user stories before starting the project.

Waterfall

Construction projects have two areas where AI can help provide a more accurate scope document. First, a draft scope document can be automatically created using a large language model (LLM) or a database of previous documents. This saves time and may include items overlooked when the scope is created manually. The second area is like the agile concept. Once a scope statement is documented, an AI-based algorithm using NLP reviews the document to look for errors, omissions, and gaps.

One of the discoveries from the work of Professor Flyvbjerg is that projects that start well are less likely to become project disasters. An accurate scope statement supports that finding. Applying AI to ensure an accurate and comprehensive scope statement significantly improves the project methodology.

Project procurement management is critical to the success of many projects. There are three important areas where artificial intelligence (AI) technology will change current practices: the contract statement of work, vendor selection, and tracking procurement progress.

The contract statement of work (SOW) takes on additional significance when outsourcing work to a vendor. The SOW is the basis for requesting bids, evaluating potential vendors, and finalizing a contractual agreement to deliver the SOW contents. Therefore, the SOW needs to be accurate and comprehensive. Natural language processing (NLP) is an essential tool to provide this requirement. NLP uses documents from similar projects and the wording in the SOW to find errors or omissions. NLP performs entity recognition to extract objectives, deliverables, and stakeholders. Sentiment analysis is performed to analyze the tone or content of communication and ensure no issues or conflicts. NLP identifies and extracts keywords critical to project success, then compares that data to find omissions.

The vendor selection process is one of the most challenging areas to maintain ethical behavior. NLP software can remove human bias, similar to current software that scans a candidate’s resume to determine how well they meet job requirements. In addition, seller bid submissions will be automatically checked and ranked based on the selection criteria.

To track vendor progress, the performing organization needs to observe vendor behavior and, if possible, access vendor project data. Robotic process automation (RPA) software creates reports and determines the most critical metrics. A machine learning algorithm detects anomalies and predicts future performance. Unsupervised learning performs clustering of data, compares trends in performance, and highlights variances that may result in unacceptable deviations. This allows both the vendor and project manager to be proactive to avoid any deterioration in the probability of project success.

The future of project procurement will be based on applying AI technology.