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India's AI landscape is undergoing a transformative shift. According to the 2024 AI Adoption Index 2.0 by NASSCOM, the Indian AI market is projected to mirror the global growth rate of 25–35% over the next 3–4 years, driven by the rapid evolution of AI technologies, including Generative AI. This surge in AI adoption is creating an unprecedented demand for skilled engineering talent across the nation.
Traditionally, India's Tier-1 cities - Bengaluru, Hyderabad, and Delhi - have been the epicenters of tech innovation. However, the escalating demand for AI talent necessitates a broader talent acquisition strategy. Tier-2 cities like Kochi, Coimbatore, Indore, and Jaipur are emerging as viable alternatives, offering cost advantages and access to untapped talent pools.
The Zinnov-NASSCOM India GCC Landscape Report highlights a significant trend: the share of Global Capability Centers (GCCs) in Tier-2 and Tier-3 cities has grown from 5% in FY2019 to 7% in FY2024, with a projected rise to 15–20% by 2025. This shift underscores the strategic importance of diversifying engineering teams beyond traditional hubs.
Despite the growing promise of Tier-2 cities, building diverse engineering teams in these regions comes with distinct challenges:
Infrastructure Gaps: Tier-2 cities often lack the advanced infrastructure found in Tier-1 cities, affecting the ease of setting up operations.
Skill Disparities: There is a shortage of workforce with specialized AI skills in these regions.
Cultural Differences: Integrating teams from diverse cultural backgrounds requires nuanced management strategies.
However, leading organizations are actively addressing these issues through targeted investments in digital infrastructure, strategic upskilling programs, and partnerships with regional institutions to foster a more inclusive and future-ready talent ecosystem.
While Tier-2 cities in India are rapidly emerging as talent hubs, building diverse engineering teams in these regions requires more than just tapping into the existing workforce. It demands a strategic shift towards ecosystem-building rather than simple talent acquisition - investing in upgrading local infrastructure, establishing partnerships with regional universities and coding bootcamps, and creating programs that upskill graduates in advanced technologies like AI and cloud engineering.
Equally important is fostering cultural readiness and leadership development within these teams. Companies are cultivating local leaders instead of relocating managers from Tier-1 cities, ensuring that regional teams are empowered and culturally aligned.
Diversity and inclusion policies are being localized to reflect the unique socio-cultural dynamics of each area. Moreover, AI itself is being leveraged to bridge skills gaps—adaptive learning platforms personalize upskilling journeys, and AI-driven recruitment tools help mitigate unconscious bias.
For forward-thinking IT leaders, the goal is no longer just to build teams but to nurture thriving innovation ecosystems in Tier-2 cities that drive not only productivity but also responsible and unbiased AI development.
Diversity in engineering teams is not just a metric of inclusion but a cornerstone for responsible AI development. A study in the Global Journal of Human Resource Management indicates that organizations with diverse workforces tend to have better performance metrics, including increased productivity and innovation.
Moreover, diverse teams bring varied perspectives, which are crucial in identifying and mitigating biases in AI algorithms. This is particularly important in the Indian context, where socio-cultural diversity is vast.
1. Collaborate with Educational Institutions to Build an Industry-Ready Workforce: Organizations can play a transformative role by partnering with local universities and technical institutes to design and deliver training modules tailored to the skill gaps prevalent in Tier-2 regions. These collaborations can focus on niche and emerging areas such as AI, data engineering, and cloud-native development to align academic learning with industry needs. Additionally, companies should offer intensive internship programs lasting at least six months, providing hands-on, on-the-job training that prepares graduates for real-world challenges. This not only ensures a steady pipeline of employable talent but also accelerates their readiness to contribute meaningfully to cutting-edge projects in fast-evolving domains.
2. Upskilling Within Organizations: To fully leverage the potential of talent from Tier-2 cities, companies must invest in continuous learning frameworks within their own walls. This includes offering structured learning paths, AI-focused bootcamps, and cross-functional project exposure that help employees transition into high-demand roles. Encouraging internal mobility and mentorship programs can also bridge skill gaps, while fostering a culture of growth and innovation. Such initiatives not only future-proof the workforce but also improve retention by showing employees a clear path for career advancement.
3. Foster Inclusive Work Cultures: Implement policies that promote inclusivity, ensuring that diverse teams can collaborate effectively.
4. Monitor and Evaluate Progress: Establish metrics to assess the effectiveness of diversity initiatives and make data-driven adjustments as necessary.
As AI continues to reshape the technological landscape, the imperative for diverse and regionally distributed engineering teams becomes increasingly clear. By proactively addressing the challenges and leveraging the opportunities in Tier-2 cities, IT leaders can not only meet the growing demand for AI talent but also drive innovation through inclusive and responsible AI development.
Authored by Jaimy Thomas, Co-founder and Chief Delivery Officer, Experion Technologies
[Disclaimer: The views expressed are solely of the author and Analytics Insight does not necessarily subscribe to it. Analytics Insight shall not be responsible for any damage caused to any person/organization directly or indirectly.]
