AICS Digest Newsletter | Fractal Analytics

January 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.

The AICS team plays a critical role in translating the rapid advancements in AI into practical, high-impact business solutions that address our clients’ most pressing challenges. The team not only understands today’s client needs but also anticipates emerging challenges and works closely with Fractal’s R&D teams to design solutions that our clients will need in the future. This is a key differentiator, enabling us to remain highly relevant to our clients and stay ahead of the curve. As we move into the coming year, sustaining this focus is essential as one thing is certain, technology will continue to evolve rapidly, and client expectations of Fractal will only continue to rise.

Sandeep Dutta

Chief Practice Officer, Fractal

INSIGHT

Context engineering with OpenAI: How enterprises make AI agents production-ready

An LLM demo looks impressive, drafting emails, answering questions, even simulating reasoning. Yet when deployed into real workflows, the same system breaks down. It forgets prior interactions, cannot reliably access internal data, and begins to hallucinate answers. User trust erodes. Compliance risk rises. Adoption stalls.

The root cause is rarely the model itself.

Who controls the agent? Governing how AI thinks, acts, and decides

In traditional software systems, access control was binary and user-centric. A human authenticated into an application, and the system checked whether that user could view a page or execute a function.

In the Agentic Era, this model breaks down. 

OTHER READS

Retrieval-Augmented Generation (RAG) in Azure ML for insurance

Driving real-time insurance decisioning with generative AI and Azure ML–based RAG. Retrieval-Augmented Generation (RAG) addresses this challenge by combining semantic search with large language models (LLMs). Built using Azure Machine Learning, our RAG solution enables insurance teams to access precise, context-aware answers in real time, without manual document searches.

Evaluating AI Agents with Ragas: A Practical Guide

Evaluating AI agents requires a fundamentally different approach than evaluating traditional retrieval-augmented generation (RAG) systems. While RAG systems primarily retrieve and synthesize information, AI agents are designed to reason autonomously, make decisions, invoke tools, and interact with external environments.

Context engineering for LLMs: The five-layer architecture guide

Context engineering has quickly emerged as the defining discipline for production-grade AI systems. While much has been written about why context matters, far less attention is paid to how it should be engineered in practice. This gap is precisely where most enterprise AI initiatives struggle, not because the models are weak, but because the surrounding context is brittle, unstructured, or unmanaged.

Listening at scale: How AI-driven conversational intelligence is redefining enterprise CX

A conversational intelligence platform uses AI and large language models (LLMs) to analyze voice and chat interactions at enterprise scale, converting unstructured conversations into real-time, actionable customer experience insights while maintaining privacy, security, and governance.

LLMOps for enterprise generative AI: Architecture, observability, and scalable AI operations

Generative AI is rapidly reshaping how enterprises analyze data, automate workflows, and interact with users. Large language models now sit at the core of analytics platforms, conversational interfaces, and decision-support systems. Yet as organizations move from pilots to production, a critical realization emerges: LLMs do not behave like traditional software or even classical machine learning systems.

Enterprise-Scale MLOps for Large Organizations: Building a Future-Ready MLOps CoE with GCP Vertex AI

Large enterprises today are no longer experimenting with machine learning, they are racing to operationalize it at scale. As models multiply across business units, geographies, and use cases, the real differentiator is no longer model accuracy alone, but the ability to deploy, govern, monitor, and evolve ML systems reliably and repeatedly.

Contributors