Category: Emerging Technologies

Sorry, the Bot Messed Up: When Customer Service Staff Blame the AI

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.

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How Generative AI Is Rewriting the Insurance Playbook

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.

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Tarangini Bank: Knowing the Customer using Artificial Intelligence

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.

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Customer Management using Artificial Intelligence: The Case of NovaMart

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.

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Beyond ChatGPT: Understanding the Models Powering the Generative AI Revolution

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.

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Top Cloud-Based Test Automation Tools to Watch in 2026

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%.

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Case Study: LUXE & CO.: CALIBRATING AI PERSONALIZATION IN PREMIUM RETAIL

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.

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