๐ง๐ฎ๐ธ๐ฒ๐ฎ๐ช๐ฎ๐ฌ ๐๐ฟ๐ผ๐บ ๐๐ช๐ฆ ๐๐ฒ๐ป๐ฒ๐ฟ๐ฎ๐๐ถ๐ฉ๐ฒ ๐๐ ๐๐ฒ๐ป๐ I recently went through the AWS Generative AI Lens. My biggest takeaway is that enterprise AI is not about adding chatbots everywhere. It's about deciding where AI should assist, reason, and take action.
- AI should assist in specific tasks
- AI should reason in complex scenarios
- AI should take action when necessary A chatbot is mostly a conversation layer. Agentic AI goes further - it understands goals, collects context, uses tools, and continues workflows. Start simple, keep the flow predictable, and add autonomy only where needed. For example, AI can classify support tickets, extract details, and suggest next actions without being fully autonomous. The same applies to document processing, knowledge search, and report generation. Use cases like autonomous call centers and generative BI are not just AI features - they are business workflows redesigned with AI. The hard part is not the interface - it's trust. Incident response is a strong example. An AI-assisted system can collect context, summarize changes, and suggest causes. But autonomy should mature slowly - investigate, recommend, automate low-risk actions, and then allow higher-impact actions with approval. A central AI platform can provide reusable capabilities and solve cost tracking issues. Cost optimization is key - generative AI cost behaves differently from traditional software cost. Model selection matters - not every task needs the largest model. My final takeaway is that AI should be part of the production architecture, not a side experiment. The companies that succeed will understand where AI fits into the workflow, how much autonomy is safe, and whether the cost is justified. Source: https://dev.to/amitkayal/takeway-from-aws-generative-ai-lens-14dj Optional learning community: https://t.me/GyaanSetuAi