NEW DELHI: Despite a sharp rise in artificial intelligence (AI) adoption across marketing functions, most organisations are struggling to demonstrate measurable business outcomes from their investments, according to a new Global CMO Survey Report released by Comviva.

Titled “The AI Efficiency Divide: Measuring AI’s Real Value Beyond the Hype,” the study found that while 90% of organisations increased their AI marketing investments over the past two years, only 12% can prove that these investments delivered tangible business results.

The report highlights a widening accountability gap, with only 16% of marketing leaders expressing confidence in defending AI investments using clear business evidence. Additionally, 67% of organisations are unable to accurately determine their total AI costs, while 79% continue to rely on estimates rather than precise measurement.

According to the findings, 35% of organisations depend on rough estimates to assess AI performance, 32% track campaign activity without linking it to revenue outcomes, and 21% lack a consistent measurement framework altogether. At the same time, 86% of leadership teams are demanding stronger proof of return on investment (ROI), increasing pressure on marketing leaders to justify AI spending.

The report identifies cost fragmentation as the biggest barrier to effective AI measurement, with 62% of organisations struggling to track expenses spread across cloud infrastructure, talent, data, and external vendors. Revenue attribution complexity was cited by 58% of respondents, while 55% pointed to challenges in connecting customer experience improvements with revenue growth. Half of the organisations surveyed also reported governance and integration gaps.

Commenting on the findings, Rajesh Chandiramani, Chief Executive Officer of Comviva, said organisations are moving from AI experimentation to enterprise-wide adoption, making accountability and measurable outcomes critical for future success. He noted that companies capable of linking AI investments directly to revenue growth, customer lifetime value, and operational efficiency will be better positioned to lead the next phase of digital transformation.

Despite the measurement challenges, the report found that AI is delivering strong results in specific areas. Customer segmentation and targeting emerged as the most effective use case, cited by 57% of respondents, followed by campaign automation and optimisation (43%), predictive personalisation (41%), pricing and offer optimisation (39%), and demand forecasting (36%).

The study also revealed that organisations often underestimate the true cost of AI implementation. While software, API, and cloud infrastructure costs are generally tracked, talent acquisition and system integration expenses remain underreported, potentially causing total AI investments to be underestimated by as much as 30–50%.

Furthermore, many AI initiatives continue to face operational hurdles. Around 54% of organisations struggle to define deployment timelines, 57% cannot connect customer experience improvements to measurable revenue outcomes, and 58% cite explainability and trust concerns as barriers to scaling AI successfully.