AI to act as a catalyst for job creation in India By- Suresh Eswar Bulusu, Director, Pre -Sales, Astrikos.ai

A recent study by ServiceNow, a leading AI platform for business transformation, and Pearson, the world’s foremost learning company, predicts that AI could generate 2.73 million new tech jobs in India by 2028. This presents unparalleled innovation and job creation opportunities, marking a pivotal shift in the conversation around AI and employment.

While concerns about automation replacing human labor, particularly in routine, repetitive tasks, continue to remain, the study highlights AI’s potential to create high-value, strategic roles that emphasize decision-making and problem-solving. Beyond promoting economic growth, this trend stresses the importance of workforce reskilling and upskilling to align with the demands of an AI-driven future.

Critical skillsets and upskilling programs

To seize emerging opportunities, tech professionals must build expertise in software application development, web development, data analytics, software testing, data engineering, AI and machine learning, cloud computing, and the integration and implementation of generative AI, among other in-demand skills.

For professionals aiming to immediately contribute and be productive, they should focus on real-world applications of AI and ML technologies. These skills ensure that individuals can build and deploy models and integrate AI into existing business processes effectively.  Proficiency is essential in core programming languages, machine learning algorithms and model development, deep learning frameworks, data visualization and communication, time series forecasting, deployment and automation of AI/ML models, and more.

AI adoption across different sectors

Retail:

Data scientists use AI/ML technologies to analyze customer feedback, sentiment, and purchasing behavior for targeted marketing campaigns, forecast demand, optimize stock levels, improve distribution, and identify and prevent fraudulent transactions, both online and in-store.

Manufacturing:

AI/ ML technologies are leveraged to analyze large datasets to identify trends, anomalies, and opportunities for improving manufacturing efficiency. In smart cities, Traffic Solutions (ATCS and ITMS), Air Quality Index (AQI) monitoring, Street Lighting, and Building Management Systems operate efficiently to ensure seamless urban infrastructure management. Data scientists use predictive analytics, and actionable recommendations on the performance of the equipment, predict and simulate failure models and proactive maintenance while reducing downtime and impact, and ensuring sustainability.  AI and ML models can be used to enhance inventory management, demand forecasting, and logistics, in addition to optimizing production processes.

Education:

Data scientists utilize AI/ML technologies to analyze student performance data and provide insights into learning patterns, enabling personalized learning experiences. Designing and developing intelligent educational platforms that adapt to personalized and adaptive learning needs is possible. AI-powered chatbots provide students with real-time assistance in learning, administration, and guidance.

Healthcare:

Data scientists work on building machine learning models for medical diagnostics, predictive analytics, and treatment recommendations. They analyze patient data, clinical trials, and healthcare trends to uncover insights that improve treatment outcomes or operational efficiency.  Analyzing clinical data for patient care optimization, predicting patient conditions, and managing chronic diseases is made possible. AI/ML technologies can assist in research-building models to analyze biological data and accelerate drug discovery and development.

Challenges to overcome in AI training

Some of the common AI training challenges are data privacy and data quality issues, the inclusion of biases in AI models knowingly or otherwise, and talent shortage.  These need to be addressed to ensure the effective adoption and growth of AI/ML technologies across industry verticals.  Additionally, the non-availability of skilled trainers and mentors, lack of accessibility to resources, low lab capacities, and internship opportunity shortage for students have to be addressed as well.

The Road Ahead

Going forward, AI/ML training in India is poised for significant growth, driven by the country’s expanding technology ecosystem, a rapidly evolving job market, and strong government support for innovation in AI. Government initiatives like the National AI Mission and AI-specific training centers will be scaled up to offer standardized, industry-driven AI/ML curricula.

Skill development programs by organizations such as NASSCOM, Skill India, and AICTE aim to equip workers in non-technical roles with AI knowledge through upskilling and reskilling programs. Incorporation of project-based learning, where students and professionals work on actual datasets, creating solutions for businesses, communities, or government Hackathons will be key components of AI/ML training, where participants can solve problems related to healthcare, agriculture, finance, and more, promoting hands-on experience.