Technology, Business and Society
Tag: information systems
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”
As a business scales, its operational needs become exponentially more complex. What begins as a manageable technical setup quickly evolves into a sprawling infrastructure requiring constant oversight. For years, the default solution for growing companies was to build a dedicated, in-house IT department. However, this traditional approach is increasingly proving to be a bottleneck rather than a strategic advantage. Today’s dynamic business environment demands agility, robust security, and specialised expertise, prompting business leaders to rethink how they manage their digital operations. The speed of technological advancement simply outpaces the capacity of most internal teams to adapt organically.
Continue reading “Why Growing Companies are Shifting Away from Traditional In-House IT”Specific IT roles are likely to be replaced with the advent of generative artificial intelligence. We wanted to undertake a brief survey of which roles are likely to be more impacted and hence, professionals in these roles should look into reskilling their portfolio and competency. Reskilling in AI/ML use could be a way to strengthen competency within this area itself.
Continue reading “Which IT roles will AI automate most”
This fictitious case study examines the multi-year strategic transformation of NovaTech Solutions, a global IT services enterprise, as it repositions itself from a traditional labor-arbitrage model to an AI-augmented and AI-native software engineering organization. Confronted with margin compression, automation-driven competition, and client expectations for exponential productivity gains, NovaTech’s leadership initiated a comprehensive enterprise transformation centered on AI-assisted coding, AI-enabled DevOps, and advanced AIOps infrastructure. Over a three-year horizon, the company restructured operating models, redesigned performance metrics, recalibrated talent strategy, and invested heavily in AI tooling and cloud infrastructure. The transformation created measurable productivity improvements and margin recovery, yet introduced complex cultural, technical, and governance challenges. Individual contributors grappled with identity shifts and skill displacement anxieties, while managers struggled to redefine productivity metrics and performance systems. Integration challenges across legacy systems and toolchains proved more demanding than anticipated. DevOps pipelines required architectural reengineering, and AIOps deployment introduced model drift, alert fatigue, and operational risk considerations. This case provides a comprehensive examination of the organizational, technical, financial, and strategic implications of enterprise-scale AI adoption in IT services.
Continue reading “Case Study: Re-Architecting an IT Services Firm for AI-Native Software Engineering”
Note: This is a hypothetical case study for classroom discussion in the course Management Information Systems. The objective is to teach students learn about cloud computing ERP and challenges in emerging models of ERP adoption in enterprise applications. This is not a real case study.
Continue reading “FinAxis Technologies: A Case about Migrating from Monolithic ERP to Microservices in a Regulated Fintech Environment”
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