In India’s corporate landscape, artificial intelligence (AI) has transitioned from a futuristic concept to a significant focus for boardrooms. Every major strategic offsite includes an “AI slide,” and almost all CXOs are discussing topics such as copilots, Generative AI, and intelligent automation. However, underlying the impressive presentations and ambitious roadmaps is a challenging reality: while India’s AI adoption is eager, it remains inconsistent.
According to the ETCIO AI Playbook 2025, which surveyed 500 senior technology leaders, only 8% of Indian enterprises have fully integrated AI into their core operations. A significant 67% of companies are limited to pilots, proof-of-concepts, and isolated deployments, while 25% are still in exploratory phases, trying to transcend experimentation. The gap between aspirations and implementation has become a hallmark of India’s AI narrative.
Sector-specific trends reveal stark contrasts. In the banking, financial services, and insurance (BFSI) sectors, AI has progressed past experimentation. Financial institutions utilize AI for fraud detection, underwriting, risk assessment, collections, and customized customer engagement. IT-enabled services and shared service organizations implement AI in customer support, service delivery, and analytics, streamlining operations by addressing repetitive tasks, case summaries, and reporting acceleration.
Conversely, the manufacturing sector has experienced more limited applications of AI, focusing primarily on predictive maintenance, basic inventory optimization, and early warning systems without achieving widespread integration. Healthcare has made notable advancements in diagnostics and imaging, assisting health professionals with image analysis, yet progress is hindered by privacy challenges, fragmented records, and an absence of unified data standards. Retailers are increasingly proficient with front-end AI applications like chatbots and recommendation systems but struggle with deeper applications involving supply chain visibility and demand forecasting.
A common misunderstanding persists across industries: many organizations equate basic automation with true AI. Chatbots, robotic process automation (RPA) scripts, and standard analytics dashboards are frequently misclassified as AI initiatives, lacking the intelligent decision-making features typical of advanced AI systems. This confusion has led to inflated expectations and disappointing outcomes, contributing to a growing fatigue with the term “AI.”
Amit Jadhav, an entrepreneur and author, observes a notable shift among Indian enterprises, even as implementation remains patchy. “Organizations are redefining their AI agendas, transitioning from whether to adopt AI to how quickly it can be scaled,” he remarks. He notes a bimodal trend; large enterprises are simultaneously exploring multiple generative AI use cases, such as internal knowledge assistants and customer engagement bots. Meanwhile, small and medium enterprises (SMEs) are cautiously employing AI to address specific pain points, including automated invoice processing and inventory management.
Jadhav outlines three key priorities for Indian enterprises over the next two to three years: operational automation, which allows for measurable cost savings and productivity improvements; customer service transformation, where rapid response times are crucial for competitive advantage; and marketing personalization, where accessible AI tools seamlessly integrate into existing technology stacks.
However, a paradox exists within this transition. Organizations express confidence in AI’s potential while hesitating to allocate sufficient resources. The successful approach, Jadhav asserts, is not about grand transformation but about starting small, fostering internal capabilities, and designing seamless human-AI collaboration.
Serious investments in AI often stem from pressing operational issues rather than excitement. The impetus for AI integration typically arises when specific metrics are underperforming, such as high average handling times, persistent invoice processing delays, or high churn rates. Once the cost of inaction becomes evident, organizations begin to allocate budgets for AI initiatives.
He cites instances where organizations shifted from manual processes in customer onboarding and compliance to AI-assisted workflows within six to twelve months, resulting in speed and accuracy gains that justify further investment. Successful implementations focus on solving the “first honest problem” that genuinely concerns leadership while demonstrating clear returns on investment.
Despite the advancements, most enterprises find themselves stuck between pilot programs and broader scale-ups. Jadhav emphasizes that this limbo is where “most AI promises go to die.” Pilot projects often operate on clean, curated datasets in a controlled environment and are tested with familiar user groups. In contrast, the real world presents challenges, with data arriving in diverse languages and formats, frequently containing gaps that models have not encountered. For example, a large manufacturing firm in Pune faced challenges when a conversational AI system, initially achieving 85% intent recognition during testing, struggled with real-world interactions in various dialects due to insufficient training.
Moreover, challenges extend beyond data issues. Many AI projects lack clear ownership; when difficulties arise, neither the data science team nor operations take responsibility, leading to prolonged issues. There is also resistance within organizations, where staff question the accuracy of AI models, fearing that success could threaten their job security. Jadhav asserts that pilot successes often falter when they encounter the complexity of real-world data, unclear roles, and apprehensions regarding transformations. To achieve scale, organizations must embrace transparent change management, establish defined governance, and ensure retraining budgets are integrated into the operating model.
Ratan Kesh, Executive Director and COO at Bandhan Bank, presents a different perspective on banking transformations. He suggests that AI should form the foundation of a new operating model rather than serve as an add-on to existing systems. “Our AI vision is to create an internal operating system for the bank where every core platform—customer onboarding, CRM, service channels, operations, governance, risk, and compliance—is designed to be AI-native from the beginning,” Kesh explains.
The roadmap for this initiative encompasses distinct phases. Initially, the focus is on embedding AI into routine workflows, such as automated document processing for onboarding and credit scoring woven into decision paths. The goal is to integrate AI seamlessly into existing processes so employees perceive it as a supportive resource rather than a disruptive force.
The subsequent phase involves enhancing predictive capabilities—anticipating incidents and customer needs rather than merely responding. These advanced systems require tighter integration between monitoring frameworks, data repositories, and AI engines, with the potential to revolutionize risk management and operational reliability.
The third phase envisions increasingly autonomous operations, where AI can execute workflows with minimal human intervention, employing knowledge and generative AI layers to assist both employees and customers in real-time decision-making. Kesh emphasizes that the ultimate vision is for AI to serve not as a mere tool but as the driving intelligence behind efficiency and enhanced customer experiences.
Nonetheless, the journey did not originate from an extravagant AI vision. It began with urgent operational challenges as the bank expanded, highlighting inefficiencies in onboarding and credit decision processes. The need for AI arose when it became clear that existing workflows could no longer support the bank’s growth. Early-warning mechanisms for fraud and operational issues were lacking, leading teams into reactive instead of proactive engagements.
Kesh notes, “AI adoption often reveals organizational frailties before it yields benefits. If processes are inconsistent or data quality is poor, AI will highlight these shortcomings.” Consequently, AI implementations may magnify existing issues. The evolving regulatory landscape surrounding data privacy, including India’s emerging data protection framework, has heightened the necessity for responsible data management—requiring measures such as purpose limitation and explicit user consent.
Looking forward to 2026, the landscape of AI-enhanced enterprises in India is already taking shape. In numerous organizations, AI copilots are expected to operate quietly behind the scenes, automating tasks like drafting responses, summarizing documents, and identifying risks in real time. Customer interactions will increasingly be facilitated by systems that understand intent and context across various channels rather than isolated applications. In banking, AI-native operating systems will simplify processes like onboarding and credit reviews, relegating human engagement to exceptional, judgement-intensive situations. In manufacturing, predictive AI applications will span entire plants, linking maintenance and supply chain decisions seamlessly.
For SMEs and MSMEs, cloud-based AI tools will automate tasks such as invoicing and inventory management, empowering smaller teams with capabilities akin to larger enterprises. By 2026, discussions in boardrooms are likely to shift from “executing AI projects” to “evaluating AI’s impact” on productivity and growth.
As Rakesh Bhardwaj of Lupin aptly states, “AI has progressed beyond concepts—it’s now about measurable value.” The focus will no longer be whether AI functionalities operate effectively, but whether they enhance employee performance and decision-making processes meaningfully. India’s AI future appears bright, contingent upon the implementation of comprehensive, value-driven strategies grounded in effective governance, high-quality data, and a strong collaboration between human expertise and AI capabilities. The next two years will be critical in determining which organizations can transform high aspirations into the framework of intelligent, agile, and truly AI-native enterprises.
