According to the book, Human + Machine by Paul Daugherty and James Wilson, artificial intelligence (AI) is defined “as systems that exceed human capability by sensing, comprehending, acting, and learning.” As we think of AI, many believe the sole role is to gradually replace jobs traditionally done by humans with robots. To the contrary, AI’s foundational premise is to replace more tedious activities such as collecting data or doing a preliminary analysis, enabling humans more time to focus on resolving complex problems. When applied correctly, AI can give a project team the ability to process and analyze copious amounts of data from a variety of sources in real time. “AI technologies can lead to increased efficiencies and markedly improved outcomes.”
For large, complex projects, the ability to use sophisticated machine learning software to search for patterns in project inefficiencies and then to be able to generate the most effective mitigation strategy and management approach for that specific phase of the project, will enable organizations to better align their project strategy to business needs of the organization.
As an industry, when we look postmortem at the top factors related to project failures, consistently the lack of proper risk management is a key element. According to John Heintz, CEO of Aptage, “The risk in a project is always probabilistic and the human mind is not good at doing risk-based probability management, especially when we’re combining many different probabilities. It’s easy to confirm your own opinions; I got the answer I expected, and I agree with myself. We are prone to what I call ‘hoped based planning’.”
The key to project success is a realistic target combined with active management. The realistic target must include the known and unknown risks, the impacts of which are always underestimated during the always hopeful planning stage of starting up. Using AI to predict the outcomes of projects using data organizations already have, such as the planned start and actual end date of various phases of the project, enables the system to learn the completion rate of the team and predict the likelihood of delivering on time.
In addition to benefits with risk management, AI can be used to better predict success at project completion. Think of the opportunities to improve as an industry if we were not limited by the knowledge of an individual’s experience and could collectively benefit from large sets of data that contain hundreds or thousands of relevant project attributes. With a common and organized set of data the hope of leveraging AI is a near term expectation and the possibilities are endless.