Technology, Business and Society
Tag: information systems
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”
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”
It starts innocently. A notification at 7:15 a.m. A Slack message during breakfast. Three video calls back to back, a software update that breaks your favourite spreadsheet, a password that expires at the worst possible moment, and an inbox that seems to refill itself every time you blink. By evening, you are not physically tired, but you feel like a smartphone at 3 percent battery, dimmed and desperately looking for a charger.
Continue reading “How do People Bounce Back from Digital Stress”
Imagine you open an app and an animated character you have been chatting with sends you a message: “So, my friend XXX was playing dodgeball in P.E. class, and while trying to dodge the ball, he tripped and fell. Oh gosh.” You chuckle. Poor XXX. You type back, “Is he okay?” and before you know it, you have exchanged twenty messages about a gym class that never happened, involving a boy who does not exist, told by a character who is entirely made of code.
Continue reading “My Chatbot Has a Best Friend – Ways AI Tries to Feel Human”
When businesses discuss digital transformation, the conversation usually revolves around software. Cloud platforms replace local servers, artificial intelligence assists employees with everyday tasks, and automation moves information between applications without manual intervention.
Continue reading “The Smart Workplace Is Going Physical: How Everyday Office Tools Are Becoming Connected”
Search for “small kitchens” on Pinterest, and you will see how the autofill function completes the rest of your sentence for you. This is what Pinterest is all about. It is basically a search engine which uses images to search instead of keywords.
Continue reading “Pinterest Keywords for Beginners: 8 Questions Answered”
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”
Artificial Intelligence (AI) has rapidly evolved from a niche technological innovation into a foundational capability that influences nearly every aspect of society. Organizations increasingly deploy AI to automate decisions, augment human intelligence, personalize services, improve operational efficiency, and create new business models. AI systems now influence decisions related to healthcare, finance, education, governance, manufacturing, transportation, recruitment, and public safety. While these systems promise unprecedented opportunities for innovation and productivity, they also introduce significant ethical, legal, social, and technical challenges. Poorly designed AI systems can amplify biases, compromise privacy, generate misinformation, make unsafe recommendations, or produce decisions that are difficult to justify. Consequently, the conversation has shifted from merely developing more powerful AI models to ensuring that these models are developed and deployed responsibly. Responsible AI has to be a targeted objective by design and be central to design principles.
Continue reading “Responsible AI: Why the Pillars of Responsible Artificial Intelligence Matter”
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”For firms, pursuing responsible AI is no longer a matter of public relations or ethical signaling—it is becoming a core strategic decision that shapes long-term competitiveness, risk exposure, and organizational credibility. As artificial intelligence moves from experimental use to mission-critical deployment, the question is not simply whether firms should adopt AI, but how they should design, govern, and integrate it into their operations in a way that is both effective and trustworthy.
Continue reading “Why go for Responsible Artificial Intelligence frameworks during Digital Transformation”
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