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Cloud & Infrastructure
The New Stack·Jun 16·5 min read
The siloed-data era is over. Here’s what comes next for AI agents.
TL;DR
Many AI projects fail due to high costs and unclear business value. The rise of agentic AI, which automates tasks and manages complex workflows, is changing this landscape.
✦ Why It Matters
Engineers should focus on clear business value and risk management to improve AI project success rates.
Key Takeaways
- Unify structured and unstructured data for better AI performance.
- Utilize retrieval-augmented generation to improve AI outputs.
- Leverage Heroku for flexible integration with Salesforce.
- Implement governance and compliance for scalable AI solutions.
How It Works
The eBook emphasizes the need for a comprehensive data foundation that combines structured CRM data with unstructured enterprise data. This integration allows AI agents to operate with the necessary context, improving their decision-making capabilities.
The use of retrieval-augmented generation (RAG) ensures that AI models receive up-to-date and verified information, enhancing their accuracy while maintaining operational speed.