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technologyreview.com·3h ago
Cloud & Infrastructure
The New Stack·24d ago·5 min read
The impressive AI demo is dead. Here’s what actually reaches production
TL;DR
Many engineering teams can create impressive AI demos, but they often struggle to transition these prototypes into production. The primary challenges include difficulties in collecting and processing real-time data from various sources, alongside a significant skills shortage in the workforce.
✦ Why It Matters
Focus on developing strong data collection strategies and invest in team training to ensure successful AI project deployment.
Key Takeaways
- Focus on improving data quality and governance for AI projects.
- Invest in data engineering skills alongside AI development.
- Build real-time data pipelines instead of relying on batch processes.
- Ensure data is fresh and contextualized for production AI.
Full Summary
Engineering teams can often create successful AI demos, but these projects frequently stall when moving to production. Research indicates that only 32% of organizations have agentic AI running in production, with data infrastructure and quality cited as significant barriers by two-thirds of respondents.
In production, AI systems must access real-time data from various sources, which is often poorly governed and not designed for immediate consumption. As a result, models that perform well in controlled environments yield unreliable results in real-world applications.
Additionally, a skills shortage complicates matters, as developers need to be proficient in data engineering to build reliable AI applications. Organizations that succeed in production prioritize data infrastructure from the outset, focusing on real-time data pipelines and quality checks.