Only 12% firms can prove AI marketing ROI, despite 90% raising investments: Report

A majority of organisations have increased investment in AI for marketing over the past two years, but only a small proportion can demonstrate measurable business outcomes from those investments, according to a new report by Comviva.

The report, titled ‘The AI Efficiency Divide: Measuring AI’s Real Value Beyond the Hype,’ examines how marketing leaders are adopting AI while facing growing pressure to demonstrate returns on investment.

According to the report, 90% of organisations increased their AI marketing investment over the past two years, but only 12% said they could prove those investments delivered results.

The findings also point to gaps in measurement practices. Only 16% of marketing leaders said they were confident in defending AI investments with clear business evidence, while many organisations continue to rely on estimates rather than precise metrics.

The report found that 67% of organisations are unable to determine total AI costs, while 79% rely on estimates instead of exact measurement.

Disconnect between AI adoption and value measurement

A significant gap remains between AI deployment and value realisation.

According to the findings, 35% of organisations rely on rough estimates to measure AI performance, 32% track campaign activity without linking it to revenue outcomes, and 21% lack consistent measurement infrastructure.

At the same time, 86% of leadership teams are demanding stronger proof of return on investment, increasing pressure on marketing leaders to justify AI spending.

Cost tracking and attribution challenges

The report identified several barriers to measuring AI’s business impact.

Cost fragmentation emerged as the largest challenge, with 62% of organisations reporting difficulties because AI-related expenses are spread across cloud infrastructure, talent, data and external vendors.

In addition, 58% cited revenue attribution complexity, saying AI influences multiple customer touchpoints, making its contribution difficult to isolate. Another 55% reported a disconnect between customer experience improvements and revenue outcomes, while 50% highlighted governance and integration gaps.

Speaking on the report findings, Rajesh Chandiramani, Chief Executive Officer, Comviva, said, “AI is rapidly moving from experimentation to enterprise-wide adoption, and the industry is entering a phase where accountability and outcomes will define success. Organisations will increasingly focus on connecting AI investments directly to business metrics – whether it is revenue growth, customer lifetime value, or operational efficiency. The real opportunity lies in building the right measurement frameworks and data foundations that enable this shift. Those who can translate AI from a capability into a consistently measurable business driver will be best positioned to lead in the next phase of digital transformation.”

Areas showing better returns

Despite the challenges, the report identified several AI applications that are delivering measurable benefits.

Customer segmentation and targeting was cited by 57% of respondents as a leading use case, followed by campaign automation and optimisation at 43%.

Predictive personalisation and recommendations were highlighted by 41% of respondents, while pricing and offer optimisation was cited by 39%. Demand forecasting was identified by 36% as contributing to improved decision-making and revenue outcomes.

The organisations are beginning to identify areas where AI contributes to revenue growth.

Improvements in customer lifetime value were cited by 43% of respondents, acquisition efficiency by 40%, and conversion rates by 38%.

However, cost visibility remains incomplete. While 62% track software and API costs and 56% account for cloud infrastructure expenses, talent and integration costs are often underreported.

According to the report, this can result in total AI investments being underestimated by as much as 30-50%, potentially overstating return on investment and affecting decision-making.

Operational challenges limit scale

The report also found that many AI initiatives struggle to move beyond the pilot stage because of operational issues.

Around 54% of organisations reported difficulties defining and tracking deployment timelines, while 57% said they were unable to link customer experience improvements to measurable revenue outcomes. Another 58% cited challenges related to explainability and trust.

The findings suggest that organisations face challenges not only in deploying AI technologies but also in measuring and operationalising them effectively across business functions.