AI and the Rise of the Hourglass Organisation

Syllabus: GS3/ Economy, S&T

In Context

• According to McKinsey, AI could add trillions of dollars to the global economy, potentially enhancing productivity by up to 25% in firms that effectively adopt it.
  • As global businesses shift towards AI-integrated models, a new organisational structure, the hourglass model is gaining prominence.

How is the Hourglass Model different from the Conventional Model?

• Pyramid Model: Conventionally, organisations have a top-heavy leadership, a broad middle management, and a large operational base. It represents a structured hierarchy with a well-defined chain of command, multiple layers of supervision and control.
• Hourglass Transformation: In this model, AI automates coordination, monitoring, and decision-making and thinning the middle layer while enhancing top-level strategy and base-level execution.
  • Gartner forecasts that by 2026, 20% of firms in the West will cut over half their middle managers using AI.
  • Microsoft has recently announced the layoff of approximately 6,000 employees, constituting about 3% of its global workforce.
• Collaborative Base: Frontline workers now work alongside AI systems — increasing speed, efficiency, and adaptability.

Case Studies and Sectoral Impacts

• E-commerce & Retail: Companies like Flipkart and Reliance Jio use AI for demand prediction, personalised shopping experience & last-mile logistics.
  • Yet, they retain human managers for language, diversity, and region-specific nuances.
• MSMEs: India’s MSMEs  the economic backbone can benefit from AI in inventory management, predictive maintenance & sales forecasting.
• Yet affordability and awareness remain roadblocks.
• Pharmaceuticals & Healthcare: During COVID-19, AI helped firms navigate supply chain disruptions & telemedicine operations.
• IT & Tech Services: Generative AI accelerates coding, boosting developer productivity by up to 66% (NNG study), allowing firms to shift focus to innovation.
• India’s rank in IMF’s AI Preparedness Index: India houses vibrant AI innovation clusters in Bengaluru, Hyderabad, and Pune, yet it ranks 72nd on the IMF’s AI Preparedness Index (score: 0.49). For comparison, the U.S. scores 0.77 and Singapore 0.80.

Challenges

• Job Displacement: Up to 800 million jobs globally could be affected by AI by 2030 (McKinsey).
  • Middle managers and low-skilled workers face the highest risk. Large sections are non-graduates or older workers with low digital skills.
• Skilling Deficit: While 94% of Indian firms plan to reskill employees (LinkedIn), execution is patchy. Government initiatives like Skill India need expansion and better alignment with AI-driven needs.
• Ethical & Data Risks: Bias in AI algorithms can lead to unfair outcomes in hiring, lending, or policing.
  • The Digital Personal Data Protection Act, 2023 is a start but lacks robust enforcement and awareness.
• Infrastructure Inequality: AI adoption is urban-centric; rural India remains under-equipped.
  • Low-cost AI solutions for SMEs are scarce, and public-private partnerships are still evolving.

Way Forward

• Skilling & Reskilling at Scale: Integrate AI modules in school and university curricula.
  • Expand Skill India Digital to cover AI, data analysis, and prompt engineering.
• Hybrid Organisational Models: Blend AI’s precision with human judgment — keep humans in the loop for ethics, creativity, and leadership.
  • Retain critical middle roles in culturally sensitive sectors (e.g., hospitality, education, public sector).
• Ethical AI Frameworks: Adopt global principles like OECD’s AI Guidelines on transparency, accountability, fairness.
  • Develop a national AI audit mechanism to ensure non-discriminatory outcomes.
• Build India-Centric AI Infrastructure: Incentivise low-cost AI tools through PLI-like schemes for AI hardware/software. Support Rural AI Labs under Digital India 2.0.

Source: TH