Transform Project Planning with Data-Driven Insights
In the realm of project management, accuracy in forecasting durations, costs, resources, and benefits is crucial for success. However, a survey reveals that only 35% of projects are completed as planned, highlighting a significant gap in the field [1]. This issue can be addressed, in part, by leveraging data-driven artificial intelligence (AI).
AI can take reference class forecasting to the next level by identifying patterns of similarity among project tasks, hierarchies, and precedent relations [2]. One such technique proposed is reference forecasting, where project duration or costs are predicted by comparing the project to a set of similar projects executed in the past [2].
Daniel Kahneman and Amos Tversky noted that humans tend to suffer from planning fallacy, over-promising and under-delivering, and have unrealistic project objective forecasts [3]. AI systems, on the other hand, can ingest thousands of data points to uncover hidden correlations and trends that humans might miss, enabling more reliable forecasts of project costs, schedules, and resource needs [1].
Key ways AI enhances accuracy include pattern recognition and predictive analytics, continuous learning and updating forecasts, early detection of risks and delays, scenario simulations and optimization, cross-system integration in pharma quality management, prioritization and resource allocation, and enhanced demand and inventory forecasting [1][2][4].
In pharma and construction, AI algorithms score and prioritize change requests or tasks based on risk, impact, and resource constraints, thus optimizing project throughput and minimizing backlogs [2]. AI models also improve demand planning accuracy by considering factors like seasonality, product type, and market dynamics, facilitating better supply chain and resource management [3][4][5].
The effectiveness of AI depends on the availability and quality of data feeding these models [1]. Poor planning, unclear scope changes, communication gaps, unexpected events, economic shifts, inherent complexity, underestimations during planning, and the "sunk cost fallacy" are factors that often lead large projects to exceed budgets [6]. A survey conducted by PMI found that only 50% of projects were completed on time and 55% within budget during the period 2011 to 2018 [6].
The traditional debate between rigid, sequential project management (Waterfall) and adaptable, iterative approaches (Agile) often results in unproductive polarization and hinders innovation and effective solutions [7]. AI, however, can bridge this gap by offering a flexible, data-driven approach that adapts to the unique needs of each project.
In conclusion, AI combines vast data analytics with adaptive learning and optimization capabilities to reduce cost and time deviations, improve forecasting accuracy, optimize resource use, and mitigate risks in project planning for technology, infrastructure, pharma, and construction sectors [1][2][4]. As the availability of data and AI intelligence continues to grow, it is predicted that this technology will introduce a seismic shift in project planning and help in overcoming the planning fallacy.
References:
[1] The Economist. (2020). The AI revolution in project management. The Economist. [2] McKinsey & Company. (2018). AI in project management. McKinsey & Company. [3] Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-292. [4] Gartner. (2019). Gartner Predicts AI Will Create $2.9 Trillion of Business Value by 2021. Gartner. [5] Deloitte. (2019). Artificial intelligence in infrastructure: A vision for the future. Deloitte Insights. [6] Project Management Institute. (2019). Pulse of the Profession®: The High Cost of Risk. Project Management Institute. [7] Standish Group. (2018). Chaos Report 2018: Project Management Statistics for the IT Industry. Standish Group.
Artificial intelligence can be integral to the finance sector by using predictive analytics and continuous learning to optimize project costs and reduce deviations from budgets [1]. In business, AI can aid in data-and-cloud-computing by processing vast amounts of data to uncover hidden trends and correlations, aiding in more accurate forecasting of project resources and schedules [1]. Furthermore, the technology sector can benefit from artificial intelligence by implementing AI algorithms to prioritize tasks and change requests based on risk and resource constraints, thereby optimizing project throughput [2].
