New Delhi: Despite the rapid adoption of artificial intelligence (AI) across marketing functions, most organisations are struggling to demonstrate its tangible business value, according to a new global study released by Comviva.
The report, titled “The AI Efficiency Divide: Measuring AI’s Real Value Beyond the Hype,” reveals that while 90% of organisations have increased their AI marketing investments over the past two years, only 12% can clearly prove that those investments have delivered measurable results. The findings highlight a growing gap between AI adoption and business impact, which Comviva describes as one of the most pressing challenges facing marketing leaders today.
Based on insights from global marketing executives, the study found that measurement maturity remains a significant concern. Only 16% of marketing leaders said they are confident in defending AI investments with clear business evidence, while a majority continue to rely on estimates and incomplete data.
The report also points to limited visibility into the true cost of AI adoption. Around 67% of organisations said they are unable to determine their total AI expenditure, while 79% rely on estimates rather than precise measurement, creating a disconnect between investment levels and measurable returns.
Growing Pressure to Prove ROI
According to the study, organisations are facing increasing scrutiny from leadership teams regarding the effectiveness of AI investments.
While 35% of respondents rely on rough estimates to assess AI performance, 32% track campaign activity without connecting it to revenue outcomes, and 21% lack a consistent measurement framework altogether.
At the same time, 86% of leadership teams are demanding stronger evidence of return on investment (ROI), putting additional pressure on chief marketing officers and business leaders to demonstrate business outcomes from AI initiatives.
Structural Challenges Hinder Measurement
The report identifies several key barriers preventing organisations from effectively measuring AI impact.
Cost fragmentation emerged as the biggest challenge, with 62% of respondents stating that AI-related expenses are spread across cloud infrastructure, talent, data management, and third-party vendors. Another 58% cited revenue attribution complexity, noting that AI influences multiple customer touchpoints, making its impact difficult to isolate.
Additionally, 55% reported a disconnect between customer experience improvements and revenue outcomes, while 50% pointed to governance and integration challenges that limit measurement consistency.
Commenting on the findings, Rajesh Chandiramani, Chief Executive Officer at 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.”
AI Delivers Strongest Returns in Revenue-Linked Use Cases
Despite measurement challenges, the report highlights several AI applications that are generating measurable business benefits.
Customer segmentation and targeting emerged as the most effective use case, cited by 57% of respondents. Campaign automation and optimisation followed at 43%, while predictive personalisation and recommendation engines were identified by 41% as key drivers of customer engagement.
Pricing and offer optimisation (39%) and demand forecasting (36%) also ranked among the top-performing AI use cases contributing to business growth and improved decision-making.
Hidden Costs Continue to Distort ROI Calculations
The study also warns that many organisations underestimate the full cost of AI deployment.
While 62% track software and API expenses and 56% monitor cloud infrastructure costs, critical expenditures related to talent acquisition, training, and systems integration are often overlooked. As a result, total AI investments may be underestimated by as much as 30–50%, potentially leading to inflated ROI assessments and misguided investment decisions.
Scaling AI Remains a Challenge
Beyond financial considerations, organisations continue to face operational hurdles in scaling AI initiatives.
The report found that 54% of organisations struggle to define and monitor deployment timelines, slowing time-to-value. Meanwhile, 57% are unable to directly link customer experience improvements to revenue outcomes, and 58% cite explainability and trust concerns as barriers to broader adoption.
The findings suggest that long-term AI success will depend not only on deploying advanced technologies but also on building robust measurement frameworks, governance structures, and operational processes that can translate AI capabilities into measurable business outcomes.
With AI investments continuing to accelerate globally, the report concludes that organisations that can effectively bridge the gap between implementation and accountability will be best positioned to unlock sustainable value from their AI initiatives.
