The top reasons why AI projects fail are: bad performance of prompts, exploding costs to run the models and agents, unacceptable prompt output quality, and challenges to stay compliant!
According to Accenture's AI Investment Report, 84% of executives believe they need AI to grow business impact and improve efficiency. However, according to McKinsey, only 33% of all AI applications currently make it to production. By the end of 2027, Gartner predicts that 40% of AI projects will also be canceled. This is a lot of lost opportunity, money, and competitive advantage.
AI Observability is the practice of tackling those problems as it provides automated, real-time insights into AI systems. The data allows developers, data scientists, and operations to optimize their models, prompts, agentic algorithms, and deployments to run faster, more cost-efficiently, while also staying compliant
In this video, Andreas (Andi) Grabner @andreas_grabner, CNCF Ambassador and DevRel at Dynatrace, walks you through some of the best practices for using Dynatrace for AI Observability. Andi will also show you the out-of-the-box experience for AI Observability on the Dynatrace Playground, which is free for you to learn and follow!
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📖 Chapters 📖
00:00 - Introduction
01:36 - AI Project Challenges: Performance, Cost, Quality, Compliance
02:54 - AI Observability: Full Stack Approach
03:22 - Dynatrace Integration Overview
03:44 - Use Case Walkthrough
05:24 - Live Demo on Dynatrace Playground
07:47 - Wrap Up
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🔗 Additional Links
Visit the AI Observability Launchpad
Dive deeper into AI Observability
Sources
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