A new EY India study finds that only 23% of companies are generating significant ROI from AI, while 44% report negligible gains. This gap between hype and results suggests that for investors, success depends on data quality and operational integration rather than simple tool adoption.
A recent EY India study, released on November 4, 2026, has highlighted a critical disconnect between AI adoption and actual business value. The report, titled 'Demystifying AI in customer service: Navigating AI-led transformation to value realization,' reveals that despite widespread AI implementation, only 23% of organizations are seeing significant returns on their investments. These top-performing firms, classified as 'Achievers,' have successfully integrated AI into their core operations to deliver improvements of over 50% in key areas like revenue, customer experience, and agent productivity.
The findings act as a reality check for the broader market, which has seen an explosion in AI-related spending. The report identifies a clear divide in the industry: while 'Achievers' (23%) prioritize long-term integration, a significant 44% of firms, termed 'Aspirants,' are reporting low or negligible value from their AI initiatives. Another 33% of companies fall into the 'Challengers' category, seeing only moderate gains. This suggests that simply deploying AI tools—such as basic chatbots or isolated sentiment analysis apps—is rarely sufficient to drive bottom-line growth.
For investors, the report underscores the importance of looking beyond corporate announcements regarding AI. The study notes that successful firms spent time building a foundation of data quality and governance before scaling. In contrast, firms failing to extract value are often struggling with fragmented information, siloed data, and poor internal processes. The investment in AI is essentially a test of operational maturity, not just technology procurement. Firms that rush into AI without cleaning up their underlying data are at risk of wasting capital, as these projects often fail to deliver the expected efficiency or cost savings.
There are also material risks to consider when evaluating a company's AI strategy. The report warns that rapid adoption without a structured framework can lead to significant governance gaps, including compliance failures and security vulnerabilities. These hidden costs can weigh on profitability and create regulatory headwinds. Successful AI implementation is also a longer-term endeavor; the data shows that the top-performing companies typically began their AI journeys more than two years ago, rejecting the narrative of rapid, short-term payoffs.
Going forward, investors may monitor how companies communicate their AI progress. A red flag may be a focus on the number of tools deployed or the hype around specific technologies, rather than the integration of AI across the customer journey and measurable improvements in efficiency. The key differentiator for future competitive advantage will likely be a company’s ability to move from a pilot-focused, experimental mindset to a deep, data-driven operational strategy. Assessing whether a company is building the necessary data infrastructure or merely buying software is essential for understanding the long-term potential of their AI investment.