The Artificial Intelligence (AI) revolution has taken the world by storm. IDC forecasts that worldwide AI spending will exceed $512 billion by 2027, more than double its 2024 market size. While AI was picking up pace, the introduction of Generative AI (GenAI) changed the way enterprises leveraged AI.
In India, AI and GenAI adoption is significantly increasing across software, services, and hardware for AI-centric systems, with AI and GenAI spending projected to reach $6 billion by 2027 with a compound annual growth rate (CAGR) of 33.7% for the period 2022-2027. The AI revolution has also accelerated digital adoption in India with 62% of Indian enterprises expecting more than 50% of revenue to come from digital models by 2026.
Multiple AI Use Cases Are Emerging Across Indian Industries
Industries that have been slow in terms of digital adoption have accelerated their journey to get ready for their AI journey while industries that have been digital leaders have already started evaluating and deploying relevant use cases.
Governments in the Asia Pacific are the third-largest adopters of AI/GenAI, with spending expected to increase with a 5-year CAGR of 96.2% by 2027. This growth offers a chance to enhance efficiency, transparency, and citizen engagement in public services. In India, almost 50% of government organizations are planning to invest significantly in data management-related services, such as discovery, quality, data engineering, and governance in 2024. Hence, the approach is clearly shifting towards data-centric rather than model-centric. Additionally, a sizable chunk of these organizations is planning to significantly increase investments in AI and Machine Learning (ML), including GenAI. For example, citizen services in states like Haryana are leveraging Jugalbandi, a new GenAI-powered chatbot on WhatsApp, to facilitate a wide range of tasks, including pension payments and college scholarship applications. While adopting AI/GenAI, the government also has a responsibility to govern the usage of this technology while giving the necessary impetus for innovation.
Digital in healthcare has been a focal point in recent years, particularly in the post-pandemic period. The Indian healthcare sector is witnessing a surge in clinical data driven by patient-centric care management that is evolving into real-time patient data capture and analysis. Such a surge in the clinical data, along with immunity to AI investments by healthcare organizations aligns the healthcare sector increasingly towards an “AI everywhere” approach. There is already an increased focus on early detection of diseases, both communicable and non-communicable diseases.
During our discussions with CIOs of multi-specialty hospitals, AI-based use cases, mainly focusing on diagnostic accuracy, speed, and workflow efficiency are popular. For example, Apollo Hospital is set to leverage the use of AI to detect TB from chest X-rays, as a means of triaging. They even scan the villages to screen TB cases. Another case is the launch of “iOncology.ai”, by AIIMS Delhi for early detection of breast and ovarian cancers, the two most prominent types of cancer in the country and percolate the solution to district hospitals.
AI is also transforming every corner of the Banking, Financial Services, and Insurance (BFSI) sector, with the most significant impact on customer interactions, risk management, and operational efficiency in India’s financial landscape. JP Morgan’s AI-focused strategy, implemented six years ago, exemplifies the long-term commitment of leading financial institutions to AI integration. In a highly regulated industry like BFSI, institutions face challenges such as big data management, outdated IT infrastructure, market responsiveness, and cyber fraud risks, which function as a speed breaker for AI adoption. Despite that, we are witnessing growing importance for GenAI pilots among many BFSI institutions in India across various business functions primarily to enhance existing services.
Telecom operators are aspiring to be more than just connectivity providers – they want to be recognized as digital leaders. India is the second largest market by subscribers globally, yet it has one of the lowest average revenues per user. IDC predicts total connections in India will reach 1.5 billion in 2028 (both mobile and fixed) with a total data traffic of 468 exabytes. Though the demand is high, balancing churn and profit margins have continued to challenge telcos in India.
Two areas which tie into churn and profit margins are customer experience (CX) and network operations. For CX, the augmentation of AI is at the forefront of addressing customers across various touchpoints. For example, any ambiguity in the bill will lead to customer churn, both in consumers and enterprises. Use of AI to explain and analyze bills consisting of varying bill cycles, bill splits, multiple payment modes, loyalty, and promotional offers reduces the number and duration of calls for contact center agents. On the network operations side, AI is infused to shift from reactive to proactive network management eventually to predictive network management. As networks become more disaggregated with increased virtualization and edge deployments, the urgent need to look past manual troubleshooting has led to network automation. The closed-loop network management should span across network operation workflows and BSS systems.
India is at a pivotal moment, ready to become a global manufacturing hub as the world looks for alternatives to China. Today, China makes up about 28% of global manufacturing, while India accounts for only 3.3%, competing with countries like Vietnam and South Korea in Southeast Asia. For India to become a manufacturing hub, India must leverage technologies like AI, robotics, automation, IoT, and 3D printing. Furthermore, the manufacturing industry also faces challenges with regular supply chain disruptions. According to IDC’s April 2024 Global Supply Chain Survey, more than 30% of India’s manufacturers, retailers, and logistics companies are expecting supply chain disruptions due to rising costs, talent shortages, and regulatory compliance issues.
These challenges are also accelerating the adoption of data-driven technologies and AI in the manufacturing sector. AI can help India with three very important competitive factors in the manufacturing industry – enable scale, reduce cost, and increase efficiency. AI can also enhance supply chain security by detecting any risk or fraud in the supply chain system. Furthermore, navigating regulatory compliance is becoming more manageable with AI solutions that automate compliance monitoring and reporting, ensuring adherence to complex regulations.
Given all these, the expectations of Indian enterprises from AI vendors are around multiple dimensions and IDC recommends tech vendors to take the following approach to be successful:
- Develop robust data platform (necessary governance, security, data privacy, ethics and so on) and sound, workable & effective data infrastructure.
- Offer data integration and data management capabilities which align with existing legacy government IT infrastructure.
- Identify specific use cases for national priorities & areas such as agriculture, healthcare, traffic, insurance, etc., and build relevant solutions around those.
- Controlling Costs is critical. Explore the option of a “pay-as-you-go” model, explore Small Language Models (SLM) and Medium Language Models (MLM) instead of full-fledged Large Language Models (LLM) which may not be needed for all enterprises.
- Ensure Trust –most importantly, ensure trust by maintaining transparency.
- Communicating the functionalities and risks of AI systems to stakeholders and offering continuous education and training are essential for effective AI utilization and to bridge knowledge gaps.
- Expedite the implementation process to ensure faster time to value, helping organizations quickly realize the benefits of AI
