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Artificial Intelligence in Project Management
Previously I have written about the potential use of AI in the management of large complex projects and several issues which arise in such usage and outlined the need for effective verification and validation (V&V). In this paper some areas of special concern are highlighted and thoughts on how to approach aspects of the V&V process offered. This paper considers the work of others in the broader verification and validation community as well as my own thoughts as to V&V in AI enabled project management. The intent of this paper is to foster a discussion of this important area as various project management AI efforts move apace.
The management of large complex projects is entering an era of unprecedented challenge and one which warrants further attention and examination. While this paper is written from the perspective of large complex engineering and construction projects the key points and challenges are broader. The specific challenge this paper focuses on arises from the increased incorporation of artificial intelligence (AI) of all forms (AI, machine learning, natural language processing, etc.) into the various elements of project execution as well as the broader corporate frameworks in which these projects reside. This article in no way intends to suggest that we should avoid the incorporation of AI into our day to day project activities. Rather it is intended to highlight the extent and breadth of its development in the engineering and construction field and to highlight the challenges to the industry and profession which must be addressed. This paper is not intended to be a primer on AI and the project management profession would benefit from education on the opportunities and risks that AI will create. Short Background on AI AI makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Examples are computers learning to play chess or Jeopardy using AI, for intelligent assistants (Siri; Alexa) or for self-driving cars. Big Data and AI are interlinked. Data is being generated at an exponential rate. Analyzing these large sets of structured and unstructured data requires self-learning computers to recognize patterns using concepts like 'deep-learning', 'machine-learning' and 'neural networks'. Big data and AI go hand-in-hand, one will not be useful without the other and the two reinforce each other. Although most think AI is driven by Big Data analytics, the scope of the technology under the umbrella term that is AI falls into three distinct categories: Big Data, vision, and language. In essence, vision and language are related to machines being able to imitate and enhance human perception capabilities, while Big Data is related to how machines 1 How to cite this paper: Prieto
Large scale projects, especially capital construction projects, are notoriously difficult to manage. Project managers, or other stakeholders, require techniques to quickly assess a current state of their project and to better anticipate likely performance trajectories. Ideally, project managers would be able to quickly compare their project against historical projects or known best practices in order to benefit from the wealth of prior experience that exists. Unfortunately, there are very few techniques currently available to project managers that allow them to recognize the project’s state as being similar to circumstances related to other projects.
The growth in project complexity and scale provides growing challenges for today's project managers. 1 Equally, these challenges provide increased challenges for program and portfolio managers who must look at not only the "sum" of individual project performance but also broader portfolio wide performance patterns 2 . Improvements in traditional project management tools must be coupled with advanced analytics 3,4 and newer tools geared to detection of negative performance precursors. In this paper we examine one possible tool, sentiment analysis, and its application to detection of negative performance precursors. Semantic Analysis Early prediction of potential negative trends in project performance is aided by early identification of precursors to sustained negative performance 5,6 . Among the sources of potential precursors that can be utilized is a wide range of project electronic correspondence and reports. These reports may be analyzed in many different ways but one approach is to conduct a semantic analysis of the language utilized in the reports. The appearance of semantically negative terms is an early indicator of potential project issues and often is a prelude to formal identification of an issue with defined impacts in structured project reports. The semantic analysis to be conducted is preferentially (but not exclusively) focused on three primary sources of textual data in order to provide higher computational efficiency and increased confidence in the semantic findings: Select pairs of correspondence Periodic structured reports Periodic narrative management reports
The regular review of projects by individuals outside the direct project execution team is a core aspect of effective project management. Throughout my career I have seen the good, the bad and ugly. This core management process and its objectives are essential to both deliver the client's outcomes we have contractually committed to deliver but also to deliver to the company the anticipated profits that were anticipated at the time of contract execution. Project companies (the overwhelming majority of the engineering and construction industry) have essentially only one source of profits, namely, the projects that they execute. Effective project review meetings are not the industry norm, one just needs to look at the erosion of project gross margin that we experience. This is in addition to the decade's long continuation of project cost growth and schedule slippages especially for large complex projects. Many of the most successful firms in the industry have processes that are very similar to those who experience continuing and unacceptable margin erosion and losses. So process is not enough, but it is essential to being successful, regularly. There are even instances of very successful firms developing a pattern of margin erosion over time even though the basic project processes, including the project review processes, are largely unchanged. So what is going on? Let me start by distilling down what I view as essential elements in effective project review, focusing only on those aspects related to the performance of the project execution company itself (the engineering or construction company). I have deliberately avoided generating an extensive listing in favor of something more succinct. Essential elements of effective project review include: A standard, regular (monthly) project status report that captures in one place all available project data, in a consistent format across all projects. o For specific projects, many of the "sheets" will be unpopulated with data (not relevant to specific project) or the active sheets will change over the lifetime of the project (engineering moving into procurement moving into construction moving into start-up and commissioning)
There is a growing and proper interest in the deployment of artificial intelligence (AI) into the management of projects, especially large, complex projects. I have previously written about this 2 highlighting some of the limitations, cautions and transparency required while at the same time outlining the benefits available. I suggested that data looking at the broader project environment (stakeholder, regulatory, labor etc.) may provide even earlier insights given the propensity of large, complex projects to frequently be adversely impacted by external factors, outside the project team's direct control. In a subsequent paper 3 I highlighted some of the ethical challenges one may face in the proper use of AI. For projects these included clearly understanding the scope and limitations of training data and ensuring that the specific AI algorithms being deployed are appropriate to the use case at hand. Transparency, and arguably certified validation and verification processes, are essential to confident use of AI in predicting project trajectories and likely performance. Many of the AI efforts aimed at project management today are focused on performance prediction stopping short of addressing its role in a changed project management system. This is the equivalent of a state of the art fire detection system that detects when a fire begins much earlier than traditional detectors, but stops there, without the balance of the "system" responding to assess the situation, suppress the fire, and confirm the fire is out removing other similar flash points. Proper reliance on artificial intelligence in project management requires a comprehensive project management system encompassing.
I have written previously about the need to look at every problem and every opportunity from a myriad of perspectives. The use of AI in this “debating” role offers the possibility of an AI enabled assistant constructively providing fact based challenge from a number of different perspectives, enriching the ultimate human decision process.
The management of large complex projects is entering an era of unprecedented challenge and one which warrants further attention and examination. While this paper is written from the perspective of large complex engineering and construction projects the key points and challenges are broader. The specific challenge this paper focuses on arises from the increased incorporation of artificial intelligence (AI) of all forms (AI, machine learning, natural language processing, etc.) into the various elements of project execution as well as the broader corporate frameworks in which these projects reside. This article in no way intends to suggest that we should avoid the incorporation of AI into our day to day project activities. Rather it is intended to highlight the extent and breadth of its development in the engineering and construction field and to highlight the challenges to the industry and profession which must be addressed.
Artificial Intelligence (AI) enabled systems, machines and algorithms undertaking cognitive tasks raise a myriad of ethical issues. These range from ensuring that the AI enablement does not lead to direct or indirect harm to humans or the broader environment which we are part of. Broader ethical questions also arise with respect to the moral status of AI and creating AI more intelligent than humans. These later items are not addressed in this paper. The primary perspectives in this paper are twofold. First, the management of large complex projects and the issues associated with use of the predictive capability of AI, primarily machine learning. Second, a civil engineering perspective, where AI may be employed in design and other optimizations. A recurring question should arise as we consider the use of AI by both project managers and engineers. Should we require AI ethics just as we require engineering ethics for engineers? This question and other related ones are being debated today around projects, taking place under the auspices of the IEEE Standards Association and their Global Initiative