May 2026
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.
Agentic AI is reshaping the insurance enterprise faster than any prior transformation. Insurance is shifting from being process‑driven to intelligence‑driven. The insurance industry is at an inflection point where the fusion of domain expertise and Agentic AI can redefine every part of the value chain, from underwriting precision to claims excellence.
The semantic intelligence building, agentic workflows, and governed AI platform will define how carriers assess risk, settle claims, and serve customers for the next decade. Fractal is helping insurers not only adopt AI, but harness it responsibly and at scale to build an enduring competitive advantage
Onil Chavan
Client Partner, Insurance
INSIGHT
AI unplugged: Why intelligence is moving out of the cloud
As AI becomes embedded in everyday devices, industrial machines, and edge infrastructure, the limitations of centralized approaches are becoming impossible to ignore. The future of AI is not concentrated in distant data centers. It is distributed, operating where data is created.
Three distinct paradigms have emerged: Edge AI, Cloud AI, and Hybrid AI. Each represents a different answer to where intelligence should live.
Intelligence decentralized: Why on-device LLMs are rewriting the rules of AI
There is a quiet architectural revolution underway in AI, and it does not involve a new foundation model or a record-breaking benchmark. It involves location. Specifically, where intelligence runs.
For most of the past decade, the dominant pattern was simple: data leaves the device, travels to the cloud, is processed in a data center, and is returned. Virtual assistants, recommendation engines, and conversational AI; the intelligence always lived somewhere else. That model worked. It also created a single point of dependency, a privacy liability, and a latency ceiling.
All three are now being quietly dismantled.
OTHER READS
From static to adaptive: How feedback-aware AI is redefining enterprise intelligence
When a senior analyst corrects a metric definition, when a domain expert clarifies a business rule, or when a team lead specifies how outputs should be structured, those signals vanish at the end of the session. The next query starts from scratch. Restated expectations and repeated corrections create friction that compounds at scale.
Predictive intelligence for choosing the right enterprise AI
As organizations adopt multiple LLMs across diverse use cases, selecting the most suitable model becomes increasingly difficult. Traditional benchmark-based evaluations often lack business relevance, fail to predict performance on new tasks, and ignore operational considerations such as cost and latency. Evaluating every model for every use case is expensive, slow, and difficult to scale.
Reinventing enterprise compliance with AI-powered intelligence
How a multi-agent AI system now detects expense misallocations, flags policy violations, and delivers audit-grade compliance insights in seconds - for a global enterprise managing multi-zone OPEX spend across 4 packages and 64 subpackages.
Why your AI POC most probably won’t make it to production
Many organizations successfully demonstrate AI capabilities at pilot phases but struggle to translate them into enterprise-scale deployments. While model performance may meet expectations, failures arise from unclear objectives, poor data readiness, weak governance structures, and a lack of accountability. These stall initiatives don't generate business impact despite investment.
Contributors
Ayushi Singh Chhetri
Senior Data Scientist
Vibha Pant
Senior Data Scientist
Vishnu KT
Manager
Karan Samani
Lead Data Scientist
Abhijit Guha
Client Partner
Sumukh Bhalchandra Sule
Data Scientist
Prosenjit Banerjee
Principal Data Scientist
Chandramauli Chaudhuri
Client Partner
Soumo Chakraborty
Principal Architect
Anindya Sengupta
Client Partner
Sujit Shahir
Principal Data Scientist
Swarna Jha
Associate
Triparna Chatterjee
Associate
Parul Chaudhary
Data Scientist
Mandar Patil
Lead Data Scientist
Anik Chakraborty
Principal Data Scientist
Tanmay Garg
Lead Data Scientist
