Technology, Business and Society
Tag: Artificial Intelligence
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”
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”
Picture a small business owner in Nashville, or Noida, who needs a logo for her new bakery. She posts the job on a freelancing platform before her morning coffee. By lunchtime, forty proposals have landed in her inbox. Each one is beautifully written. Each one says the freelancer is “passionate about bringing your vision to life.” Each one promises “attention to detail” and “timely delivery.” She reads ten of them and realises, with growing despair, that she cannot tell a single applicant apart. It is a bit like speed dating where everyone has rehearsed the same script from the same self-help book.
Continue reading “When Every Freelancer Sounds Like ChatGPT, Who Gets the Job?”
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”
A scalable IoT network starts with the work each device must do. Sensors may send small readings, while cameras and edge computers can move large amounts of data. The network also needs stable power, safe access, and a clear path to business systems. Teams comparing outdoor wireless access point options should first map device locations, traffic, power sources, and physical barriers.
Continue reading “Reliable Network Infrastructure for Scalable IoT and Edge Deployments”
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”
The rapid evolution of digital technologies has fundamentally transformed how businesses interact with customers, suppliers, employees, and other stakeholders. Among these technologies, Generative Artificial Intelligence (Generative AI or GenAI) has emerged as one of the most transformative innovations of the digital era. Unlike earlier artificial intelligence systems that primarily focused on prediction, classification, or automation of repetitive tasks, Generative AI possesses the capability to create human-like text, images, videos, software code, and business insights. This ability has significantly lowered the barriers to digital transformation, particularly for small firms that often struggle with limited financial resources, technical expertise, and workforce capabilities. By democratizing access to advanced cognitive capabilities, Generative AI enables small firms to become more agile in their digital interactions, allowing them to respond quickly to market changes, personalize customer engagement, streamline internal communication, and continuously innovate their business models.
Continue reading “Boosting Small Business Agility with Generative AI”
The history of cybersecurity has largely been a history of reaction. Organizations have invested billions of dollars in firewalls, intrusion detection systems, endpoint protection, identity management, and security operations centers (SOCs), yet cybercriminals have consistently stayed one step ahead. The challenge is no longer the absence of security technologies but the inability of human analysts to process overwhelming volumes of data, identify sophisticated threats, and respond at machine speed. The rise of Generative Artificial Intelligence (GenAI) represents perhaps the most significant opportunity in decades to change this equation. Rather than serving merely as another security tool, GenAI has the potential to become an intelligent collaborator capable of augmenting every stage of the cybersecurity lifecycle.
Continue reading “The Cybersecurity Copilot: How Generative AI Is Reshaping Digital Defense”
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