Magazine article
From Models to Impact: Tackling ML Operational Challenges in the Enterprise
CXOTech magazine
25 Feb 2026
Abstract
Machine Learning has made the leap from hype to high-stakes, becoming a core agenda item in boardrooms worldwide. Retailers personalize recommendations, banks detect fraud in real-time, and manufacturers predict equipment failures before they occur. Creating a model is the easy part; making it work consistently in production is what separates the pros from the rest. This “last mile” challenge deploying, monitoring, and scaling models, is where many companies stumble. Industry surveys suggest that over 90% of ML models development failures stem from poor productization practices and difficulties integrating models into production systems (McKinsey et al., 2024). Below, let’s unpack the most common hurdles organizations face when operationalizing Machine Learning, along with practical ways to address them.
Metrics
1 Record Views
Details
- Title
- From Models to Impact
- Creators
- Pragati Awasthi - Drexel University, Information Science
- Publication Details
- CXOTech magazine
- Resource Type
- Magazine article
- Language
- English
- Academic Unit
- Information Science
- Other Identifier
- 991022193392604721