AI ServeSmart Digest
Insights at the intersection of AI and enterprise strategy, helping leaders turn innovation into impact.
Welcome to the AI ServeSmart Digest, designed for leaders who are shaping the future with AI. Each month, we bring you sharp insights and real-world stories on how applied AI is solving today’s toughest business challenges, creating measurable impact, and opening new growth opportunities. Think of it as your executive lens on what’s next in enterprise AI.
Our ability to build the best teams is a direct result of the powerful synergy between FAA and our AI Consulting practice. This partnership is a key competitive advantage.
Rasesh Shah
Chief Practice Officer
INSIGHT
Human-AI fusion creates new ideas from conversations
Every customer chat holds untapped insight. We set out to capture that value, transforming routine chatbot interactions into a real-time innovation engine. By fusing human perspective with AI-driven guidance, we’re turning engagement into co-creation.
SPOTLIGHT
GraphRAG: Transforming AI retrieval into intelligent understanding
GraphRAG is redefining the next frontier of enterprise intelligence. By fusing the power of knowledge graphs with Retrieval-Augmented Generation (RAG), it elevates AI from merely retrieving information to truly reasoning with context. Traditional RAG systems surface fragments of text through vector searches; GraphRAG, in contrast, structures information as interconnected knowledge networks.
OTHER READS
Transforming complex commercial RFQ reviews with AI-powered automation
In healthcare, access to timely, accurate data is critical but often limited. Traditional RAG systems link LLMs to external data for context-aware responses but work in a static, one-shot manner. Agentic RAG introduces autonomy through reasoning agents that can plan, retrieve, and refine information, turning passive Q&A into dynamic, multi-step problem-solving for more reliable, compliant clinical and operational intelligence.
From conversation to conversion
Traditional LLMs could answer questions, but they couldn’t manage complex workflows, validate their own outputs, or operate safely in high-stakes environments. The challenge was to turn static models into autonomous collaborators that could think, adapt, and uphold the highest standards of care.
Contributors
