Create a two minute video on Change Management Lesson 6: Types of organizational change. Organisational change frameworks generally categorize structural transitions into four core types: Incremental, Transformational, Disruptive, and Crisis-driven change. When examining these four types through the lens of AI adoption within the Indian corporate and enterprise ecosystem, each manifests with unique operational challenges, cultural dynamics, and execution realities.1. Incremental Change (Continuous Improvement)The Concept: Small, continuous, low-risk adjustments made over time to fine-tune existing procedures and processes without altering core structures. AI Adoption in the Indian Context:What it looks like: Introducing AI spelling, grammar, and basic document summarization tools for back-office operations, or using routine automated spreadsheet add-ons in finance and compliance teams.The Real-World Reality: This is the easiest type of AI adoption for Indian firms because it faces minimal resistance. Employees view it as a helpful utility rather than a threat. However, the risk here is staying stuck in perpetual incrementalism—optimizing low-level tasks while missing out on deeper, strategic business model transformations.2. Transformational Change (Fundamental Overhaul)The Concept: Radical, second-order shifts that completely redefine an organization’s strategy, operating model, hierarchy, and culture. AI Adoption in the Indian Context:What it looks like: Redesigning traditional IT service delivery models or Global Capability Centers (GCCs) from a headcount-heavy billing model ("linear scaling of human effort") to an AI-augmented, outcome-based product engineering model. It involves rewriting workflows where junior staff no longer learn solely through manual data entry, shifting the career ladder entirely.The Real-World Reality: Highly complex in India's hierarchical corporate structures. It requires intense change leadership to manage deep-seated anxieties around job security, bridge the middle-manager bottleneck, and restructure performance metrics to reward automation over raw execution hours.3. Disruptive Change (Market-Driven Shifts)The Concept: Industry-reshaping innovations driven by new market entrants, shifting customer expectations, or advanced technologies that force a company to radically alter its playbook to stay relevant.AI Adoption in the Indian Context:What it looks like: Agile Indian fintech startups leveraging generative AI to instantly process loan applications, underwrite credit, and offer hyper-localized vernacular financial advice, forcing legacy Indian private and public sector banks to rapidly rebuild their consumer apps around conversational AI agents.The Real-World Reality: Legacy firms often struggle with fragmented data architectures and rigid bureaucracy. To respond to disruptive AI forces, they are compelled to establish sandboxed innovation hubs and bypass traditional, slow-moving approval chains to prevent losing market share to nimble tech-first competitors.4. Crisis-Driven Change (Reactive Responses to Shocks)The Concept: Immediate, high-urgency reactions triggered by sudden external shocks, severe regulatory changes, or existential operational threats.AI Adoption in the Indian Context:What it looks like: Traditional BPOs and customer-support heavyweights suddenly facing massive pricing pressure or loss of overseas client contracts unless they transition overnight to multilingual, real-time generative AI voice and chat resolution systems.The Real-World Reality: Panic-driven adoption. While urgency eliminates procrastination, it often leads to "Shadow AI" or rushed deployments that bypass internal compliance, data residency laws, and security standards—ultimately requiring intensive firefighting to fix governance gaps post-implementation.
