EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
MIT Technology Review·Sep 3·5 min read
Scaling agentic AI pilots across the enterprise
TL;DR
As agentic AI transitions from pilot projects to enterprise-wide deployment, organizations must align AI initiatives with business strategies and ensure cohesive systems integration. This approach can enhance decision-making and operational efficiency across workflows.
✦ Why It Matters
Evaluate your current workflows and identify areas where AI can be integrated for strategic impact.
Key Takeaways
- Align AI projects with clear business objectives.
- Redesign workflows to integrate AI effectively.
- Ensure AI agents have access to necessary data.
- Address governance and security as AI roles expand.
Full Summary
Agentic AI, which refers to AI systems capable of making autonomous decisions, is increasingly being adopted by Fortune 500 companies, yet many still operate in isolated pilot phases. Arun Chandra, COO at NiCE, emphasizes the need for organizations to connect AI initiatives to specific business objectives, such as revenue growth or cost reduction.
Instead of merely adding AI to existing workflows, companies should redesign these processes to fully leverage AI capabilities. Effective scaling requires agents to access relevant data and systems, as fragmented information can hinder their performance.
Additionally, organizations must address governance and security concerns as AI agents take on more significant roles. By fostering a connected strategy focused on high-value use cases, businesses can enable AI agents to proactively address customer needs and collaborate with one another.