AI & ML in Enterprise Software: From Experiment to Production
Artificial Intelligence (AI) and Machine Learning (ML) hold tremendous promise, but many enterprises struggle to move from proof-of-concept to full-scale production. At TechPex , we’ve shepherded clients through that exact transition many times. In this article, we outline how to take AI/ML from experiment to production in enterprise contexts. 1. Start with business-first use cases Rather than chasing the latest model or trend, begin with high-impact business problems (e.g., predictive maintenance, churn prediction, automation). Working backward from value helps focus efforts. We often hold cross-functional workshops (data + business + engineering) to prioritize feasible AI use cases. 2. Invest in data readiness and pipelines AI is only as good as your data. Early on, we assess data quality, availability, governance, and pipelines. At TechPex, we build ETL/ELT pipelines (using Spark, Airflow, or cloud-native data pipelines) to aggregate, cleanse, and transform data. Without so...