Impacts of Artificial Intelligence

on Management of Large Complex Projects



By Bob Prieto

Chairman & CEO
Strategic Program Management LLC

Florida, USA



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 can analyze large amounts of data much quicker and more accurately than humans, find correlations, and even make predictions of how systems will behave in the future.

AI encompasses a wide range of core technologies but this paper is focused primarily on:

  • Machine learning (ML) type of AI that involves using computerized mathematical algorithms that can learn from data and can depart from strictly following rule-based, pre-programmed logic. ML algorithms build a probabilistic model and then use it to make assumptions and predictions about similar sets of data.
  • Deep learning (DL) is a form of ML that uses the model of human neural nets to make its predictions about new data sets.
  • Natural language processing (NLP) enables computers to understand human language as it is spoken and written, and to produce human-like speech and writing.
  • Computer vision (CV) attempts to identify images of objects that can be seen. It can also include attempts to use the same technology to identify patterns in data, such as seismographic readings, that humans cannot readily see.
  • Machine reasoning (MR) attempts to simulate human thought processes by using a computerized model of language to acquire knowledge, and then make decisions. Instead of being programmable in the traditional sense, expert systems are designed to build the model’s own understanding of the world based on the relationships between words and concepts.
  • Strong AI also referred to as artificial generalized intelligence (AGI) attempts to simulate general human thought processes by using a computerized model of concepts to organize knowledge and then act on it. Instead of being programmable in the traditional sense, strong AI seeks to make sense of the world by relying on human language’s inherent model of reality buttressed by the discipline of logic.

Extent and Breadth of Potential AI Use Cases

The rate of technology adoption is accelerating and soon AI will be leading the way. This will have a broad impact on both the projects that we do and how we do them. The Artificial Intelligence (AI) market will reach $36bn in 2020, and then almost quadruple to $127bn by 2025. AI is projected to be the single largest driver of tech spending over the next 5 to 10 years. We are only at early stages of Big Data monetization where only 1% of data is stored and analyzed, and only 8% of companies have deployed machine learning beyond the initial testing phases.


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How to cite this paper: Prieto, B. (2019). Impacts of Artificial Intelligence on Management of Large Complex Projects. PM World Journal, Vol. VIII, Issue V, June. Available online at https://pmworldlibrary.net/wp-content/uploads/2019/06/pmwj82-Jun2019-Prieto-Impacts-of-Artificial-Intelligence-on-Management-of-Large-Complex-Projects.pdf



About the Author

Bob Prieto

Chairman & CEO
Strategic Program Management LLC
Jupiter, Florida, USA


 Bob Prieto is a senior executive effective in shaping and executing business strategy and a recognized leader within the infrastructure, engineering and construction industries. Currently Bob heads his own management consulting practice, Strategic Program Management LLC.  He previously served as a senior vice president of Fluor, one of the largest engineering and construction companies in the world. He focuses on the development and delivery of large, complex projects worldwide and consults with owners across all market sectors in the development of programmatic delivery strategies. He is author of nine books including “Strategic Program Management”, “The Giga Factor: Program Management in the Engineering and Construction Industry”, “Application of Life Cycle Analysis in the Capital Assets Industry”, “Capital Efficiency: Pull All the Levers” and, most recently, “Theory of Management of Large Complex Projects” published by the Construction Management Association of America (CMAA) as well as over 600 other papers and presentations.

Bob is an Independent Member of the Shareholder Committee of Mott MacDonald. He is a member of the ASCE Industry Leaders Council, National Academy of Construction, a Fellow of the Construction Management Association of America and member of several university departmental and campus advisory boards. Bob served until 2006 as a U.S. presidential appointee to the Asia Pacific Economic Cooperation (APEC) Business Advisory Council (ABAC), working with U.S. and Asia-Pacific business leaders to shape the framework for trade and economic growth.  He had previously served as both as Chairman of the Engineering and Construction Governors of the World Economic Forum and co-chair of the infrastructure task force formed after September 11th by the New York City Chamber of Commerce. Previously, he served as Chairman at Parsons Brinckerhoff (PB) and a non-executive director of Cardn0 (ASX)

Bob can be contacted at rpstrategic@comcast.net.

To view other works by Bob Prieto, visit his author showcase in the PM World Library at https://pmworldlibrary.net/authors/bob-prieto/