Databricks, the Data and AI company, unveiled a new Economist Impact report, “Unlocking Enterprise AI: Opportunities and Strategies,” which examines the challenges businesses face in adopting and scaling AI, and the techniques they are using to drive greater value from these investments. The report found the vast majority of Indian enterprises (94%) are using generative AI (GenAI) in at least one function, the highest percentage globally across 19 countries surveyed. But only 24% of Indian respondents believe their GenAI applications are production-ready as respondents across Asia-Pacific cite key hurdles including cost (40%), skills (38%), governance (38%) and quality (33%).
As demand for data intelligence grows worldwide, AI continues to be a major focus area for companies. According to Goldman Sachs, global AI spend is expected to reach US$1 trillion in the next few years. The AI market of India is growing at a CAGR of 25-35% and is expected to reach US$17 billion by 2027, according to a Nasscom-BCG report. While more companies are investing in AI than ever before, struggles related to delivering business-specific, highly accurate, and well-governed results at a reasonable cost are preventing organizations from scaling their AI efforts and achieving more transformational results.
“As businesses in India rapidly adopt AI, they are setting a new benchmark for integrating data-driven solutions tailored to the customers’ unique needs,” said Anil Bhasin, Vice President and Country Manager for Databricks India. “At Databricks, we are dedicated to supporting this journey by providing platforms that prioritize governance, scalability, and efficiency. This new Economist Impact report reinforces the importance of data intelligence and highlights that industry leaders will be those who take a holistic approach, focusing on robust data management, governance, and specialized expertise. We’re here to empower India’s AI ambitions, contributing to a future where the country leads in building transformative technology for the world.”
Whether streamlining clinical trials or identifying potential vehicle issues before they occur, many enterprises are already using AI to improve efficiency and productivity. With the growth of ‘Agentic AI’ — artificial agents with a natural language interface that can plan and execute tasks on behalf of a user — companies can spread these benefits to more of the workforce. In fact, 86% of Indian respondents expect that, within the next three years, natural language will be the primary or only way non-technical staff will interact with complex datasets. Increasingly, organizations are also using AI to improve customer service, fraud detection, and patient care, among the many other use cases, highlighting the long-term potential of the technology to accelerate overall business success.
The value of AI for uncovering hidden insights is clear for many large enterprises, such as Mahindra Group, an Indian multinational conglomerate. “We have many listed companies,” said Mohit Kapoor, Group Chief Technology Officer, Mahindra Group. “We ensure data is always identifiable so that it can be segregated, but we can also anonymise that data to drive insights from across our companies.”
“Companies and organisations have access to the best fuel for AI models: the right and most relevant data, not merely data sourced from the open internet. Dream Sports, for instance, found third-party LLMs ineffective, even if [we] were using the latest application because the company’s language is too specific. Models have to be trained on the company’s data,” said Amit Sharma, Chief Technology Officer, Dream Sports.
The Economist Impact report surveyed 1,100 technical executives and technologists from 19 countries across Asia, Europe and the Americas, including insights from 100 respondents from India. Among the organizations represented are Accenture, CJ CheilJedang, Condé Nast, Dream Sports, Fanatics Betting & Gaming, Flo Health, Frontier, General Motors, HP, JetBlue, Mahindra Group, Mastercard, Molson Coors, Novartis, NTT Docomo, Opendoor, Providence, Rakuten Group, Repsol, Rivian, Seven West Media, Shell, Siam Commercial Bank, TD Bank Group, Thermo Fisher Scientific, Unilever, UPS and the United States Army.
Additional key findings include:
- More than seven in 10 Indian respondents (71%) see AI as crucial to their long-term goals. Despite the momentum, only 29% believe investment across technical and non-technical domains is sufficient.
- By 2027, 100% of all Indian respondents expect GenAI adoption across both internal and external use cases.
- Nearly half of data scientists (49%) across Asia-Pacific are still using a general-purpose large language model (LLM) without contextual enterprise data. These general-purpose models often struggle to provide the necessary quality, governance and the ability to evaluate outputs. More than half (58%) of data scientists across the region have begun to augment their LLMs with proprietary data through retrieval augmented generation (RAG), and 63% of organizations in India see significant potential in combining LLMs with enterprise data to build data intelligence.
- Indian organizations expect to mix and match different models and tools in their Agent Systems, spanning open source and proprietary technologies, to drive better performance. By 2027, 99% plan to deploy open source AI models.
- Less than three in 10 (29%) Indian respondents are confident their organization can secure enough AI talent.
- 39% of Indian respondents acknowledge their organization’s data and AI governance is insufficient. Enterprises face challenges with fragmented data estates, complicating discovery, access permissions, data usage, audits and sharing. Governing AI models and tools is also vital to meet evolving AI regulations. To succeed, enterprises need a unified and open governance approach.
“From classic machine learning to generative AI, the business world’s obsession with AI isn’t letting up. But our findings show that, for many organizations, the real value comes when the technology is unleashed on their own proprietary data to develop data intelligence,” said Tamzin Booth, Editorial Director of Economist Impact. “That data intelligence is even more valuable in an increasingly unpredictable world. To drive the algorithm advantage they’re seeking, it’s clear enterprises must address significant challenges with producing high-quality outputs, identify ways to evaluate performance and governance with large AI models, and work out how to effectively connect AI to the workforce.”
