Technology, Business and Society
Category: Emerging Technologies
Remember when knowing “prompt engineering” was enough to look like the smartest person in the meeting? That skill now has the shelf life of a mango in a Delhi June. In just three years, AI models have gone from witty chatbots that wrote poems about your cat to software agents that read your codebase, call APIs, and file the ticket before you finish your chai.
Continue reading “The AI Model Glow-Up: What AI Professionals Must Learn Next”
For the last few years, the AI industry has behaved like a teenager at a gym: bigger, bigger, bigger. Every new language model arrived with more parameters, more data and a bigger electricity bill than the last. Then a quiet rebellion began. Researchers started asking a very Indian question: “Kitna deti hai?” How much performance do we actually get for every rupee of compute? The answer gave rise to Small Language Models, or SLMs. These are the compact, fuel-efficient cousins of giant models like GPT or Claude. They will not win a trivia contest against their big relatives, but they can run on your laptop, answer in a blink, and keep your data at home. In this post, let us open the bonnet and see what SLMs are, how they work, and why businesses are suddenly so fond of them.
Continue reading “Small Language Models: Big Brains in Small Packages”
In every boardroom discussion on generative AI these days, two acronyms get thrown around like confetti at a Delhi wedding: SLMs and RAG. Someone says, “We should go for an SLM,” someone else says, “No, RAG is better,” and everyone nods wisely while quietly Googling both under the table. Here is the twist. Comparing Small Language Models (SLMs) with Retrieval-Augmented Generation (RAG) is a bit like comparing a compact car with Google Maps. One is the vehicle; the other is a way of finding where to go. They solve different problems, and understanding that difference is exactly what helps managers make smarter AI investments.
Continue reading “SLMs vs RAG: Small Brains, Big Libraries”
Every office has that one colleague. The report is late, and it was the printer. The meeting was missed, and it was the calendar app. The client email went to the wrong person, and it was, naturally, autocorrect. Humans have been blaming machines for as long as there have been machines, and probably blamed the weather before that. But what happens when the machine sitting next to you at work is an AI chatbot that talks to the same customers you do? It turns out you now have the perfect scapegoat: a colleague who never argues back.
Continue reading “Sorry, the Bot Messed Up: When Customer Service Staff Blame the AI”
Insurance has always been a business of words and numbers. Policies, endorsements, exclusions, claim forms, adjuster notes, medical reports, loss histories: the industry runs on mountains of documents that humans have spent decades reading, summarising and re-keying. That is exactly why generative AI, a technology built to read, write and reason over language, is landing in insurance with unusual force.
Continue reading “How Generative AI Is Rewriting the Insurance Playbook”
Tarangini Bank, a mid-sized private sector bank founded in 1946 in Mangaluru, serves 14 million customers through 1,150 branches. By 2026, UPI, fintech competition, a squeeze on low-cost deposits and new data-protection rules are reshaping Indian retail banking. The bank’s segmentation still rests almost entirely on account balance. As a result, campaign response rates are poor, offers miss the mark, and valuable customers leave without warning. Ananya Deshpande, the bank’s new Chief Digital and Analytics Officer, proposes a segmentation built on demographics and on how customers actually spend across categories and channels, using k-means clustering. Her team must deal with duplicate customer records, stale demographic data, unclear UPI transactions, privacy obligations and a sceptical branch network. A pilot produces six clear segments that cut across the old balance tiers. Deshpande must now recommend to the board how quickly to roll out the new segmentation, which method to use, and how to turn the insight into a more customer-focused bank.
Continue reading “Tarangini Bank: Knowing the Customer using Artificial Intelligence”
This case study examines how NovaMart, a large omnichannel retailer, leverages artificial intelligence and machine learning to transform customer management from a largely intuition-driven activity into a data-driven and predictive process. The case focuses on the complementary application of unsupervised and supervised learning. NovaMart first integrates customer information from transactions, digital platforms, loyalty programmes, and customer-service systems to create a unified analytical data foundation. Using K-means clustering, the company identifies five behavioural customer segments: Premium Loyalists, Promotion Seekers, Occasional Explorers, Digital Enthusiasts, and At-Risk Customers. Classification models are subsequently developed to assign new customers to these segments and predict outcomes such as customer churn and campaign response.
Continue reading “Customer Management using Artificial Intelligence: The Case of NovaMart”
The remarkable success of Generative AI is not driven by a single algorithm but by the evolution of several complementary model architectures, each designed to solve specific computational challenges. Early deep learning systems often struggled with unstable training, poor-quality outputs, limited diversity, and high computational costs. Over the past decade, researchers have progressively addressed these limitations by developing increasingly sophisticated generative models. Today, four dominant architectures—Generative Adversarial Networks (GANs), Transformers, Variational Autoencoders (VAEs), and Diffusion Models—form the technological backbone of modern Generative AI. Each architecture approaches content generation differently and has distinct strengths that make it suitable for particular applications ranging from computer vision and language processing to scientific research and healthcare.
Continue reading “Beyond ChatGPT: Understanding the Models Powering the Generative AI Revolution”
The world is now witnessing the wonders of automation, and the software industry is right in the middle of it. If you are still doing manual testing, you are lagging behind your competitors. As AI Multiple Research reveals, about 33% of companies are planning to automate between 50% to 75% of the testing process, and almost 20% of the companies are planning to automate over 75%.
Continue reading “Top Cloud-Based Test Automation Tools to Watch in 2026”This fictitious case study examines the strategic dilemma faced by Luxe & Co., a premium fashion and lifestyle brand navigating the transition from a relationship-driven physical retail model to a data-intensive digital environment. By 2025, the company had successfully shifted a majority of its revenue to e-commerce; however, this transition exposed deeper structural challenges. Rising customer acquisition costs, stagnant customer lifetime value, and declining repeat purchase rates signaled a weakening connection between the brand and its customers. Recognizing that the issue lay not in product quality but in the erosion of personalized engagement, Chief Marketing Officer Sarah Chen initiated a €15 million pilot program to test an artificial intelligence–driven personalization platform, StyleAI.
Continue reading “Case Study: LUXE & CO.: CALIBRATING AI PERSONALIZATION IN PREMIUM RETAIL”
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