In India, AI is reaching a tipping point. Over the last two years, AI has begun gaining traction among common users, and tools used for conversations through applications now have a presence on nearly every device, whether it’s a smartphone or tablet. Businesses are discovering that incorporating AI into business processes through the use of AI assistants helps increase efficiency and ultimately assist organizations in completing tasks faster, more accurately, and at a lower cost. Additionally, many of India’s leading companies, including those in the financial services sector, telecommunications, and e-commerce industries, are already investing in raising awareness about the potential of AI technology to improve and streamline operations and user experience.
However, the global view of AI currently is one of uncertainty. Although there is tremendous excitement, businesses and governments are now asking: What type of measurable financial benefits will AI bring? The majority of businesses attempting to implement AI technology are not yet seeing any return on investment; approximately 95% of AI projects developed worldwide have yet to produce any financial benefit. For Indian businesses, this aspect has taken on greater significance due to India’s size, linguistic diversity, and the current regulatory landscape around AI.Prediction 1: The AI reality check — From experiments to scalable value
One of the most significant transitions we can expect in 2026 is the shift from an experimental phase of technology to a living product that is scaled in terms of business application. This has been recognized as a “GenAI paradox”. Building generative AI copilots is fast, but long-term ROI requires deep integration with enterprise tech stacks, rules, and workflows. In India’s legacy-heavy sectors such as banking, healthcare, and manufacturing, scaling AI hinges on successful integration first; automation should follow only after systems and processes are aligned. Gartner predicts 40% of AI agent initiatives will fail by 2027 due to unclear ROI, weak governance, and poor alignment with real-world operations. In India, AI adoption will depend less on rapid model development and more on effective integration with enterprise systems and governance frameworks for lasting value.
Across sectors, businesses are recognizing that AI agents can complete tasks traditionally performed by humans and operate as co-workers. However, they still require human oversight, especially in India’s regulated environment where accountability and trust are critical. While AI is used for Know Your Customer (KYC), claims analysis, coding assistance, customer service, and legal research, only limited deployments have moved beyond controlled pilots. Cultural and regulatory expectations, driven by bodies like Reserved Bank of India (RBI), Insurance Regulatory and Development Authority of India (IRDAI), National Highway Authority of India (NHAI), and the Digital Personal Data Protection Act, reinforce the need for caution. By 2026, AI agents will complement rather than replace human workers. To unlock value, organizations must prioritize onboarding, explainability, retraining, and governance.
Prediction 3: Context engineering will become India’s most valuable AI skill
As AI adoption accelerates in India, businesses are learning that model choice matters, but the structure and precision of input matter more. Broad context windows often lead to “context rot,” which reduces output accuracy. With India’s multilingual landscape and industry-specific terminology, fine-tuned context becomes essential. The next stage of AI implementation will prioritize controlled information flow, ensuring organizations define what data models can access and how decisions are influenced. This includes establishing governance frameworks, domain-specific knowledge boundaries, and clearly structured rules for processing language, as well as compliance requirements and operational nuances. Success will depend on designing AI systems that operate with clarity, consistency, and precision.
Prediction 4: AI will shift from data-push to smart, pull-based context retrieval
Earlier AI systems relied on sending large volumes of indexed data to models. New agentic AI works differently: it retrieves only the data needed when a request is made, a method known as “minimum viable context.” This pull-based approach is especially valuable in India’s hybrid infrastructure, where data spans private cloud, public cloud, and on-premises systems. By accessing only relevant information, AI agents can work faster, generate more accurate outputs, and better support regulatory compliance. This shift marks a step toward more efficient, secure, and operationally aligned AI deployment across Indian enterprises.
Prediction 5: Graph databases will become the navigation layer for AI Agents in India
As AI evolves beyond basic reasoning, traditional relational data formats will no longer be sufficient. In India, where operations and regulation depend heavily on relationships, whether detecting UPI fraud, tracing telecom identities, integrating supply chains, or mapping healthcare journeys, graph databases offer a more effective foundation. They enable AI systems to understand connections, context, and causality rather than isolated data points. As a result, analysts can build AI agents that reason, trace logic paths, and explain outcomes. By 2026, graph technology will serve as a core layer for trusted, transparent AI systems, helping businesses create connected knowledge frameworks that support accuracy and accountability.
Prediction 6: Databases will become adaptive rather than static
As AI workloads expand, the data layer beneath them must evolve. Next-generation databases will be adaptive, continuously optimizing their performance based on usage patterns, model behavior, and feedback. Many enterprise databases still reflect architectures from the late 20th century, making them inefficient for continuous, reasoning-based AI. In India, where AI is increasingly embedded in banking, governance, mobility, commerce, and national digital infrastructure, adaptive systems will shift from optional to essential.
India is now transitioning from proving AI works to ensuring it works responsibly, consistently, and at scale. The organizations that lead will integrate AI with structured knowledge, strong governance, and connected data systems, enabling sustainable transformation rather than experimentation.
The author is Ish Thukral, General Manager for India and SAARC, Neo4j.
Disclaimer: The views expressed are solely of the author and ETCIO does not necessarily subscribe to it. ETCIO shall not be responsible for any damage caused to any person/organization directly or indirectly.
