90% of organisations increase AI marketing investments, but only 12% can measure real impact: Comviva Global CMO Survey Report

Comviva study titled ‘The AI Efficiency Divide: Measuring AI’s Real Value Beyond the Hype’ highlights the widening gap between AI investment and measurable business outcomes

Even as AI adoption accelerates across marketing functions, most organisations are struggling to prove its business value. Ninety percent of organisations increased their AI marketing investment over the past two years. Only twelve percent can prove it worked. This gap between expectation and actual delivery is the defining challenge of the next eighteen months of marketing leadership. Against this backdrop, Comviva has released its Global CMO Survey Report titled “The AI Efficiency Divide: Measuring AI’s Real Value Beyond the Hype,” examining how marketing leaders are scaling AI while facing pressure to demonstrate tangible outcomes.

The report further underscores gaps in measurement maturity, with only 16% of marketing leaders confident in defending AI investments with clear business evidence, while many continue to rely on approximations. It also reveals limited cost visibility, as 67% of organizations are unable to determine total AI costs and 79% rely on estimates rather than precise measurement, reinforcing the disconnect between investment and measurable impact.

The Accountability Gap No One Planned For

According to the report, a significant disconnect exists between AI deployment and value realization, with most organizations lacking robust measurement frameworks.

• While 35% rely on rough estimates, 32% track campaign activity without linking it to revenue outcomes, and 21% lack consistent measurement infrastructure altogether.
• At the same time, 86% of leadership teams are demanding stronger proof of ROI, increasing pressure on CMOs to justify AI investments with tangible business outcomes.

What’s Blocking AI Measurement?

The report identifies structural barriers that prevent organizations from effectively measuring AI impact.

• Cost fragmentation is the biggest challenge, with 62% of organizations struggling as AI expenses are spread across cloud, talent, data, and vendors.
• In addition, 58% cite revenue attribution complexity, as AI influences multiple touchpoints, making its contribution difficult to isolate.
• A further 55% report a disconnect between customer experience and revenue, while 50% highlight governance and integration gaps that limit consistent measurement.

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.”

These findings indicate that AI delivers the strongest impact when applied to use cases linked to revenue generation and real-time decision-making.

Where AI Investment Actually Pays Off

Despite these challenges, certain AI use cases are delivering clear returns.

• Customer segmentation and targeting lead, cited by 57% of respondents, followed by campaign automation and optimization at 43%.
• Predictive personalization and recommendations, highlighted by 41%, are also driving stronger customer engagement.
• Pricing and offer optimization (39%) and demand forecasting (36%) further contribute to improved decision-making and revenue outcomes.

The Real Cost Equation: Revenue Drivers and Hidden Costs

While organizations are beginning to identify where AI drives revenue, they often underestimate its true cost.

• Key revenue drivers include improvements in customer lifetime value (43%), acquisition efficiency (40%), and conversion rates (38%).
• However, cost visibility remains fragmented, with 62% tracking software and API costs and 56% accounting for cloud infrastructure.
• Critically, talent and integration costs are often underreported, leaving total AI investments underestimated by as much as 30–50%.

This incomplete view risks overstating ROI and misguiding investment decisions.

Why Promising AI Initiatives Still Fail

The report highlights that many AI initiatives fail to scale due to operational gaps.

• Around 54% of organizations struggle to define and track deployment timelines, delaying time-to-value.
• Meanwhile, 57% are unable to link customer experience improvements to measurable revenue outcomes, and 58% cite challenges around explainability and trust.

These gaps suggest that success depends not just on deploying AI, but on operationalizing it effectively across speed, experience, and governance.