Tradeshift Replaces Legacy BI with Agentic AI, Boosts Performance.
Summary
Tradeshift successfully transitioned from a traditional BI tool to Amazon Quick with agentic AI, achieving 30x faster query responses, a 40% cost reduction, and new revenue from embedded analytics.
Why it matters
Professionals can see a clear example of how adopting agentic AI can lead to significant performance improvements, cost savings, and new revenue streams, moving beyond traditional BI limitations.
How to implement this in your domain
- 1Assess current BI tools for performance bottlenecks and cost inefficiencies.
- 2Explore agentic AI platforms like Amazon Quick for enhanced analytical capabilities.
- 3Develop a migration strategy from legacy BI to modern AI-driven analytics.
- 4Measure and benchmark improvements in query response times and operational costs.
- 5Identify opportunities to productize embedded analytics for external revenue generation.
Who benefits
Key takeaways
- Agentic AI can dramatically improve analytical query performance.
- Migrating from legacy BI tools can lead to significant cost reductions.
- Modern AI solutions can transform embedded analytics into a revenue source.
- Tradeshift's experience highlights the tangible benefits of AI adoption.
Original post by Raphael Bres
"In this post, we describe how Tradeshift deployed Amazon Quick with agentic AI capabilities to replace our legacy BI tool, resulting in query response times up to 30 times faster, a 40 percent reduction in total cost of ownership, and turned embedded analytics into a product that…"
View on XOriginally posted by Raphael Bres on X · view source
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