“Our research shows a contradiction: that forms of AI with humanlike interactivity and social familiarity seem to encourage the greatest trust, regardless of actual reliability or accuracy,” said Kathy Lange, Research Director of the AI and Automation Practice at IDC. “As AI providers, professionals and personal users, we must ask: GenAI is trusted, but is it always trustworthy? And are leaders applying the necessary guardrails and AI governance practices to this emerging technology?”
“Indian business leaders are demonstrating a sophisticated view of AI,” said Noshin Kagalwalla, Vice President & Managing Director, SAS India. “Unlike many global counterparts who focus on cost savings, Indian organizations are 21% more likely to prioritize decision-making and show elevated interest in product innovation and process efficiency. Their lowest priority is personal productivity, which signals a strategic shift. AI is being seen not just as a tool for operational gains, but as a catalyst for core business transformation. This mindset, combined with a strong emphasis on trustworthy AI, is positioning India as a leader in responsible and impactful AI adoption.”
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The research draws on a global survey of 2,375 respondents conducted across North America, Latin America, Europe, the Middle East and Africa, and Asia Pacific. Participants included a balanced mix of IT professionals and line-of-business leaders, offering perspectives from both technology and business functions.
India’s current AI landscape reflects many of the global trends. The country’s AI maturity is being accelerated by government initiatives like the IndiaAI Mission and Deep Tech Fund, which are driving infrastructure development, domain-specific models and start-up financing. This public sector momentum is complemented by corporate giants and a thriving start-up ecosystem, positioning India as a key player in the global AI race.
Emerging AI technologies evoke most trust
Overall, the study found the most trusted AI deployments were emerging technologies, like GenAI and agentic AI, over more established forms of AI. Almost half of respondents (48%) reported “complete trust” in GenAI, while a third said the same for agentic AI (33%). The least trusted form of AI is traditional AI – less than one in five (18%) indicated complete trust.
Even as they reported high trust in GenAI and agentic AI, survey respondents expressed concerns, including data privacy (62%), transparency and explainability (57%), and ethical use (56%). Despite their high adoption rates, Indian organizations, on average, have 8% less trust in GenAI than the global average. However, this skepticism didn’t lead to hesitation – instead, it prompted them to invest more in trustworthy AI to ensure GenAI works effectively. Other organizations with low trust in generative AI can learn from this approach: Critically assess the technology, identify trust gaps, and invest in solutions to close those gaps. This strategy can lead to faster adoption and greater business impact.
Lagging AI guardrails weaken AI impact ... and ROI
The study showed a rapid rise in AI usage – particularly GenAI, which has quickly eclipsed traditional AI in both visibility and application (81% vs. 66%). This has sparked a new level of risks and ethical concerns.
Across all regions, IDC researchers identified a misalignment in how much organizations trust AI versus how trustworthy the technology truly is. Per the study, while nearly 8 in 10 (78%) organizations claim to fully trust AI, only 40% have invested to make systems demonstrably trustworthy through AI governance, explainability and ethical safeguards.
The research also showed a low priority placed on implementing trustworthy AI measures when operationalizing AI projects. Among respondents’ top three organizational priorities, only 2% selected developing an AI governance framework, and less than 10% reported developing a responsible AI policy. However, deprioritizing trustworthy AI measures may be preventing these organizations from fully realizing their AI investments down the road.
Researchers divided survey respondents into trustworthy AI leaders and trustworthy AI followers. Leaders invested the most in practices, technologies and governance frameworks to make their AI systems trustworthy – and appear to be reaping rewards. Those same trustworthy AI leaders were 1.6 times more likely to report double or greater ROI on their AI projects.
Lack of strong data foundations and governance stall AI
As AI systems become more autonomous and deeply integrated into critical processes, data foundations also become more important. The quality, diversity and governance of data directly influence AI outcomes, making smart data strategies essential to realizing benefits (e.g., ROI, productivity gains) and mitigating risks.
The study identified three major hurdles preventing success with AI implementations: weak data infrastructure, poor governance and a lack of AI skills. Nearly half (49%) of organizations cite data foundations that are not centralized or nonoptimized cloud data environments as a major barrier. This top concern was followed by a lack of sufficient data governance processes (44%) and a shortage of skilled specialists within their organization (41%).
Respondents reported the No. 1 issue with managing the data used in AI implementations to be difficulty in accessing relevant data sources (58%). Other leading concerns included data privacy and compliance issues (49%) and data quality (46%).
In India, 50% of organizations report having advanced data infrastructure, yet data centralization and governance remain among the top three challenges. This paradox reflects a growing awareness: as data maturity increases, so does the complexity of managing it. The rapid adoption of AI is pushing Indian enterprises to pursue data excellence more aggressively, recognizing that strong data foundations are essential for trustworthy and impactful AI.
“For the good of society, businesses and employees – trust in AI is imperative,” said Bryan Harris, Chief Technology Officer at SAS. “In order to achieve this, the AI industry must increase the success rate of implementations, humans must critically review AI results, and leadership must empower the workforce with AI.”
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