greyfly.ai, a company that provides delivery intelligence to increase project success, has announced that its Intelligent Project Prediction (IPP) tool is performing better than human project managers in predicting project outcomes and identifying underlying risks. The tool uses limited data points and Artificial Intelligence (AI) to make predictions and has been found to unlock capital savings up to 20%. This could potentially save executives time and help corporations make more informed decisions about which projects to pursue.
greyfly.ai’s Intelligent Project Prediction tool uses limited data points and AI to make accurate predictions to unlock capital savings up to 20%. It enables executives to save time and focus on the projects really at risk.
According to the leading project profession body (PMI, 2021), project performance hasn’t improved with on average, at least 35% of projects not delivering on time, budget or experiencing scope creep. This is emphasized as projects are getting bigger and more complicated. Indeed, the government’s Infrastructure and Projects Authority annual report 2021 on major projects showed that 135 [73%] of the 184 projects with whole life cost £542bn were reported Red and Amber (where there are at least significant issues) and data shows this situation worsening.
Meanwhile, research reveals that AI adoption will increase and another project professional body, APM (2022) claims 76% of survey respondents would utilize AI to analyse the large volume of data in complex projects to improve project decision making.
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greyfly.ai is using AI to increase the likelihood of delivering projects successfully. IPP ingests project data to predict future project performance, even in cases before a project has started. No matter which tools a company currently uses, the system is agnostic and gathers data from different sources. Once data is prepared it passes through to the AI analytics engine and machine learning algorithms and predictive analytics are applied to make sense of the hidden patterns in the data and to articulate future performance and potential risks.
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