of stalled AI/ML projects
ADVISORY ARTICLE
By Yogi Schulz, BComm
Calgary, Alberta, Canada
Organizations observe too many AI/ML projects stalling and eventually being cancelled. This article discusses the ten most common causes of this unfortunate situation and what project managers can do to correct the problems. Project managers and teams can evaluate their AI/ML project work in light of these ten causes, address them in their project plan and take corrective action to avoid stalling their AI/ML projects. The ten most common causes are:
- Deteriorating business case
- Underestimating model training
- Lacking data quality
- Addressing data integration
- Managing data volumes
- Incorporating iterative development
- Responding to data shift
- Underspecifying the model
- Validating results
- Complicating the algorithm
Deteriorating business case
Organizations approve an AI/ML project based on an appealing business case. As the project proceeds, some events, such as the following, can undermine the business case:
- Discovery of additional complexity in the solution leading to a significant increase in the cost-to-complete forecast and the annual operating cost estimate.
- Recognition that the solution requires more data the organization does not own and must pay for.
- Changes in customer expectations or preferences.
- Actions by competitors require a more elaborate response.
The best practice response for project teams is to update the business case and determine if continuing the project is still appealing. Allowing projects that are unlikely to produce a net benefit to drag on wastes the organization’s resources.
Underestimating model training
Organizations often underestimate the work that goes into training AI/ML models. AI/ML project teams tend to underestimate the following:
- Data scientists’ efforts required to train models.
- Business expertise and effort needed to collaborate with the data scientists.
- Software development effort even when significant use of open-source libraries is planned.
- Data volume and variety required.
The best practice response is for project teams to arbitrarily double the model training estimate during project planning. If this increase materially undermines the project’s business case, the project manager should recommend cancelling the project.
Lacking data quality
Too frequently, data quality issues in internal datastores stall AI/ML project progress. The common issues are:
- Inaccuracy – The available data is not accurate enough to produce reliable results from the model.
- Insufficient richness – The available data often lacks values and rows required to train the model effectively.
Improving internal data quality requires the organization to recognize the value of its data and improve its data stewardship processes. Improving data richness to achieve high-quality training data requires the project team to create synthetic data. These corrective actions add schedule and cost to the project.
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How to cite this article: Schulz, Y. (2023). Responding to the top 10 causes of stalled AI/ML projects, PM World Journal, Vol. XII, Issue X, October. Available online at https://pmworldlibrary.net/wp-content/uploads/2023/10/pmwj134-Oct2023-Schulz-top-10-causes-of-stalled-AI-ML-projects.pdf
About the Author
Yogi Schulz
Calgary, Alberta, Canada
Yogi Schulz has over 40 years of Information Technology experience in various industries. Yogi works extensively in the petroleum industry to select and implement financial, production revenue accounting, land & contracts and geotechnical systems. He manages projects that arise from changes in business requirements, from the need to leverage technology opportunities and from mergers. His specialties include IT strategy, web strategy and systems project management.
Mr. Schulz regularly speaks to industry groups and writes a regular column for IT World Canada and for Engineering.com. He has written for Microsoft.com and the Calgary Herald. His writing focuses on project management and IT developments of interest to management. Mr. Schulz served as a member of the Board of Directors of the PPDM Association for twenty years until 2015. Learn more at https://www.corvelle.com/. He can be contacted at yogischulz@corvelle.com
His new book, co-authored by Jocelyn Schulz Lapointe, is “A Project Sponsor’s Warp-Speed Guide: Improving Project Performance.”
