Shift from Pyramid to Hourglass Model
- Traditional pyramid structure: Top-heavy with bosses, middle managers, and a broad base of workers.
- Hourglass structure: AI flattens the middle tier by automating coordination and decision-making tasks.
- Leaders focus on strategy, while the base comprises frontline workers and AI systems working collaboratively.
Relevance : GS 2(Governance) ,GS 3(Technology)
AI’s Economic Promise
- McKinsey projects AI could add trillions to the global economy.
- Potential to increase productivity by up to 25% for firms embracing AI.
- SMEs in India could significantly benefit due to the potential for efficiency and flexibility gains.
Global Trends and India’s Context
- Western firms are already adopting hourglass models (e.g., 20% of firms may reduce middle managers by 2026).
- India’s scenario is unique:
- Ranks 72nd in IMF’s AI Preparedness Index.
- Urban-rural divide limits infrastructure and connectivity.
- Cultural hierarchy and respect for authority slow organizational flattening.
India’s Hybrid Approach
- Indian firms are adapting selectively:
- Flipkart, Jio use AI for supply chain and customer behavior prediction but retain human layers for local adaptability.
- Hybrid model: AI + human oversight accommodates India’s multilingual, diverse market needs and low labor costs.
Advantages of AI in Indian Workplaces
- Efficiency: AI-driven demand forecasting and supply chain optimization.
- Innovation: Generative AI improves task performance by 66% (NNG Group).
- Flexibility: AI helped pharma firms during pandemic disruptions.
- Customer/employee experience: 24/7 chatbots, automated payroll systems.
- New job roles: Rise in demand for AI experts, data ethicists — projected 1.25 million jobs by 2027 (Deloitte & Nasscom).
Key Challenges
- Job Displacement:
- Risk to middle managers and less-skilled workers.
- Up to 800 million jobs may shift globally by 2030.
- Indian non-graduates and older workers most vulnerable.
- Reskilling Needs:
- While 94% of Indian firms plan to reskill (LinkedIn), execution remains challenging.
- Ethical Concerns:
- Biased datasets can affect fairness in decisions (loans, hiring).
- Data privacy: 79% of Indians dislike data being sold (ISACA).
- Digital Personal Data Protection Act (2023) still in early implementation.
- Infrastructure Gaps:
- 65% of India lives in rural areas, many without internet access.
- High costs of AI tools and platforms make it hard for smaller firms.
- Cultural Barriers:
- Preference for hierarchical structures in family-owned businesses and traditional companies.
Recommendations
- Reskilling: Expand digital literacy and problem-solving training (e.g., through Skill India).
- Ethical Frameworks: Adopt clear AI ethics guidelines (OECD model), address bias and build public trust.
- Hybrid Strategy: Combine AI’s efficiency with human adaptability for decision-making.
- Collaborations: Partner with Western firms to develop customised AI for Indian needs.
- Long-term Monitoring: Treat AI as an ongoing transformation, not a quick fix — adapt to cyber threats and regulation changes.
