India’s AI adoption surges with strong focus on data ethics

Managing data responsibly and effectively in the age of AI can offer organisations a significant competitive advantage. However, many are still struggling to harness this potential, despite AI-readiness being a strategic priority. Among global markets, India continues to accelerate AI adoption, reaping business benefits while navigating persistent challenges in AI readiness and information management.

New research conducted by global information management provider Iron Mountain in partnership with FT (Financial Times) Longitude analysed how 500 large organisations are leveraging their information and datasets to become ‘AI-ready’.Indian organisations stood out, reporting average revenue growth of USD 4.1 billion over the past 12 months as a direct result of their information management strategies, more than double the global average of USD 1.9 billion.

The report draws on responses from senior decision-makers, including C-suite, Vice Presidents, and Directors, across key industries in India such as IT and technology services, financial services, and retail.

“With the rise of open-source and specialised AI models, data integrity, transparency, and trust are more critical than ever. At Iron Mountain, we’re investing in solutions like Iron Mountain InSight Digital Experience Platform (DXP) to help our customers prepare their information for generative AI and other AI-powered applications,” said Varun Gupta, Head of Digital Business India, Iron Mountain.“This enables organisations to uncover and manage unstructured data by automating metadata extraction and organisation at speed, scale, and with high accuracy,” Gupta added.

Data integrity as a competitive advantage

The research highlights India’s strong performance in data integrity and responsible data sourcing. Half (50%) of Indian respondents considered this a key competitive advantage, well above the global average of 32% and the UK’s 23%. This underscores India’s growing commitment to secure and ethical data management, which is vital for training reliable AI models.

India also led in transparency, with 50% of respondents stating that transparency around AI models and systems was a significant part of their approach to managing data integrity.

The adoption of Master Data Management (MDM) systems in India was also higher than in other markets. Some 43% of Indian organisations reported using MDM to ensure accuracy and consistency across business-critical data, compared to just 21% in the UK and 18% in Australia.

Furthermore, 83% of Indian organisations are working with solution providers for information management, digital transformation, and secure storage, slightly above the global average of 81%. However, 17% still lacked comprehensive data management and protection solutions.In the past year alone, poor data integrity cost Indian businesses an average of USD 381,375.

Indian organisations are placing increased emphasis on unstructured data strategies, prioritising AI-driven decision-making and business agility over the next two years. In fact, 45% cited enhancing decision-making through AI and predictive models as their primary goal, the highest among all countries surveyed, ahead of Brazil (40%), the US (39%), and Germany (32%).

Additionally, 53% of Indian respondents said that AI-powered analytics for data quality control and assurance had been the most effective method for improving unstructured data to date.

Key challenges: Cybersecurity, compliance, and skills gaps

Despite these advances, significant challenges remain. Cybersecurity threats, regulatory complexities, and workforce readiness are critical concerns.

• 45% of Indian organisations stated that data compliance efforts consistently delivered value and offered a competitive edge.

• 53% identified data encryption and security mechanisms as essential to their operations.

• 43% named compliance risks as their top concern, driven by rapid AI adoption and evolving regulations.

• 40% believed that a lack of skilled personnel in AI data management would hinder their AI readiness over the next three years, a notably high figure.