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”Author: AK
Case Study: Re-Architecting an IT Services Firm for AI-Native Software Engineering
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.
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FinAxis Technologies: A Case about Migrating from Monolithic ERP to Microservices in a Regulated Fintech Environment
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”Case Study: The Silent Signal—Addressing the Talent Exodus at NexaConnect Telecom
Case Study: The Silent Signal—Addressing the Talent Exodus at NexaConnect Telecom
I. The Context of Stagnation: A Cultural and Technical Autopsy
To understand why employees are leaving NexaConnect, one must first understand the “Telco Trap.” For decades, telecommunications companies operated as protected monopolies or oligopolies. Success was defined by uptime, regulatory compliance, and massive capital expenditure (CAPEX) in physical hardware. At NexaConnect, this history created a “Fortress Mentality.”
Continue reading “Case Study: The Silent Signal—Addressing the Talent Exodus at NexaConnect Telecom”What is Prompt Engineering: An overview
Prompt engineering refers to the practice of designing and structuring inputs to large language models so that they produce accurate, useful, and reliable outputs. As language models have grown more capable, the way prompts are written has become an important skill, blending aspects of linguistics, logic, and problem formulation. Over time, several distinct types of prompt engineering have emerged, each suited to different tasks and levels of model guidance. The following discussion presents the main types of prompt engineering in an essay-style narrative, with an example woven into each explanation.
Continue reading “What is Prompt Engineering: An overview”What is a Large Language Model for a Chatbot | TechMadeSimple
Generative artificial intelligence is the talk of the town, and everybody wants to know what is large language models that powers the generative artificial intelligence chatbots. However, understanding generative artificial intelligence requires a very technical understanding of deep learning models.And most of the time, practitioners were not coming from a computer science background are unable to understand what it exactly is. We try to simplify the tech for non tech users and have found a very nice video which does the same.
Continue reading “What is a Large Language Model for a Chatbot | TechMadeSimple”Case Study – Gendered Culture at BrightWave Advertising
1. The Spark Behind the Investigation
In March 2025, a confidential report landed on the desk of Meera Sanyal, an independent organizational consultant and workplace culture investigator. The report, commissioned by the HR Director of BrightWave Advertising Pvt. Ltd., outlined a troubling trend: in the last 14 months, eight mid-level female employees had resigned, citing “personal reasons” in their exit interviews. However, in informal HR debriefs, phrases like “toxic culture,” “male bias,” and “inappropriate behavior” kept surfacing. BrightWave, a Mumbai-based creative agency with 220 employees, had built its reputation over a decade as one of India’s most daring boutique advertising firms. It won national awards for campaigns that celebrated female empowerment and diversity — ironic, given what was unfolding inside its own walls.
Continue reading “Case Study – Gendered Culture at BrightWave Advertising”6239 Indians in Top 2% List in 2024
Across areas, this year, 6239 Indian Researchers (working in Indian Institutions / universities) are listed as top researchers in their field. The entire data set is published here:
Ioannidis, John P.A. (2025), “August 2025 data-update for “Updated science-wide author databases of standardized citation indicators””, Elsevier Data Repository, V8, doi: 10.17632/btchxktzyw.8
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Case: An Organisational Capability Framework for the Integration of Generative AI
Introduction: Beyond Technological Novelty to Strategic Necessity
The advent of advanced Generative Artificial Intelligence (GenAI), marked by the public release of sophisticated models like ChatGPT, represents a pivotal moment in the history of automation and organisational management. For the first time, technology can interact in natural human language, process unstructured data at scale, and generate novel content, positioning GenAI not merely as an incremental improvement but as a general-purpose technology poised to create the next frontier of productivity. The potential economic impact is substantial, with estimates suggesting GenAI could add trillions of dollars annually to the global economy by automating a significant portion of employee activities and creating value across functions like marketing, sales, customer operations, and software engineering.
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Case Study – Use of Agentic AI in Search Engine Marketing
Breaking News! Google Specialist releases guide to Agentic AI systems
Antonio Gulli’s comprehensive book covers 21 design patterns for building autonomous AI agents, featuring practical frameworks and technical implementation guides for developers. Antonio Gulli, a Google distinguished engineer, has published a comprehensive 400-page technical guide to building autonomous AI systems, offering detailed blueprints for creating sophisticated artificial intelligence agents. Antonio Gulli, Senior Director and Distinguished Engineer in Google’s CTO Office, announced Agentic Design Patterns: A Hands-On Guide to Building Intelligent Systems with a scheduled release date of December 3, 2025. The publication addresses a critical gap in AI development methodology. According to Gulli, building effective agentic systems requires more than just a powerful language model—it demands structured architectural blueprints. “It’s about moving from raw capability to robust, real-world applications,” Gulli stated in the book’s introduction. The guide presents 21 distinct agentic patterns that serve as fundamental building blocks for autonomous AI systems. These patterns range from foundational concepts such as Prompt Chaining and Tool Use to advanced implementations including Multi-Agent Collaboration and Self-Correction frameworks. Each pattern represents a reusable solution to common challenges encountered when building intelligent, goal-oriented systems
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