Software
Despite a surge in AI spending across marketing organizations, only 12% of companies can demonstrate that their AI investments are delivering measurable business results, according to a new survey from Comviva.
The findings highlight a growing accountability challenge facing chief marketing officers as enterprises move from AI experimentation to large-scale deployment while struggling to measure returns.
Comviva's Global CMO Survey Report, titled The AI Efficiency Divide: Measuring AI's Real Value Beyond the Hype, found that 90% of organizations increased AI investments in marketing over the past two years. Yet only a small minority can clearly prove those investments generated business value.
The report suggests that the next phase of enterprise AI adoption will be defined less by implementation and more by measurement, governance and business outcomes.
A key finding is the widening gap between executive expectations and marketing's ability to demonstrate results. While 86% of leadership teams are demanding stronger proof of AI return on investment, only 16% of marketing leaders said they are confident in defending AI spending with clear business evidence.
Many organizations continue to rely on incomplete measurement approaches. The survey found that 35% use rough estimates to assess AI performance, while 32% track campaign activity without connecting it to revenue outcomes. Another 21% lack consistent measurement frameworks altogether.
The challenge extends beyond performance measurement to cost visibility.
According to the report, 67% of organizations cannot accurately determine their total AI spending, while 79% rely on estimates rather than precise financial tracking. Cost fragmentation remains a major obstacle, with AI expenses spread across cloud infrastructure, software, data, talent and external vendors.
As a result, many organizations risk overstating AI returns because they fail to account for significant implementation and integration costs.
The study found that talent and integration expenses are frequently overlooked, potentially causing enterprises to underestimate total AI investment costs by 30% to 50%.
Revenue attribution presents another hurdle. More than half of respondents said they struggle to isolate AI's contribution across increasingly complex customer journeys and multiple digital touchpoints.
According to the report, 58% cited attribution complexity as a key challenge, while 55% reported difficulty connecting customer experience improvements to measurable financial outcomes.
Despite these challenges, the survey identified several areas where AI is generating tangible business value.
Customer segmentation and targeting emerged as the most successful use case, cited by 57% of respondents. Campaign automation and optimization followed at 43%, while predictive personalization and recommendation engines were highlighted by 41%.
Organizations also reported positive outcomes from AI-driven pricing optimization and demand forecasting initiatives.
The strongest business benefits were linked to improvements in customer lifetime value, customer acquisition efficiency and conversion rates, indicating that AI delivers the greatest impact when deployed against revenue-generating processes rather than standalone productivity initiatives.
Rajesh Chandiramani said the industry is moving into a phase where accountability will become as important as AI adoption itself.
"Organizations will increasingly focus on connecting AI investments directly to business metrics—whether it is revenue growth, customer lifetime value, or operational efficiency," Chandiramani said.
The report also points to operational barriers that continue to limit AI scaling efforts. More than half of respondents said they struggle to define deployment timelines, while others cited challenges around explainability, trust and governance.
Taken together, the findings suggest that while AI adoption in marketing is becoming mainstream, many organizations remain in the early stages of establishing the financial, operational and governance frameworks needed to translate AI investments into measurable business outcomes. For CMOs, proving value may now be a bigger challenge than deploying the technology itself.
The findings highlight a growing accountability challenge facing chief marketing officers as enterprises move from AI experimentation to large-scale deployment while struggling to measure returns.
Comviva's Global CMO Survey Report, titled The AI Efficiency Divide: Measuring AI's Real Value Beyond the Hype, found that 90% of organizations increased AI investments in marketing over the past two years. Yet only a small minority can clearly prove those investments generated business value.
The report suggests that the next phase of enterprise AI adoption will be defined less by implementation and more by measurement, governance and business outcomes.
A key finding is the widening gap between executive expectations and marketing's ability to demonstrate results. While 86% of leadership teams are demanding stronger proof of AI return on investment, only 16% of marketing leaders said they are confident in defending AI spending with clear business evidence.
Many organizations continue to rely on incomplete measurement approaches. The survey found that 35% use rough estimates to assess AI performance, while 32% track campaign activity without connecting it to revenue outcomes. Another 21% lack consistent measurement frameworks altogether.
The challenge extends beyond performance measurement to cost visibility.
According to the report, 67% of organizations cannot accurately determine their total AI spending, while 79% rely on estimates rather than precise financial tracking. Cost fragmentation remains a major obstacle, with AI expenses spread across cloud infrastructure, software, data, talent and external vendors.
As a result, many organizations risk overstating AI returns because they fail to account for significant implementation and integration costs.
The study found that talent and integration expenses are frequently overlooked, potentially causing enterprises to underestimate total AI investment costs by 30% to 50%.
Revenue attribution presents another hurdle. More than half of respondents said they struggle to isolate AI's contribution across increasingly complex customer journeys and multiple digital touchpoints.
According to the report, 58% cited attribution complexity as a key challenge, while 55% reported difficulty connecting customer experience improvements to measurable financial outcomes.
Despite these challenges, the survey identified several areas where AI is generating tangible business value.
Customer segmentation and targeting emerged as the most successful use case, cited by 57% of respondents. Campaign automation and optimization followed at 43%, while predictive personalization and recommendation engines were highlighted by 41%.
Organizations also reported positive outcomes from AI-driven pricing optimization and demand forecasting initiatives.
The strongest business benefits were linked to improvements in customer lifetime value, customer acquisition efficiency and conversion rates, indicating that AI delivers the greatest impact when deployed against revenue-generating processes rather than standalone productivity initiatives.
Rajesh Chandiramani said the industry is moving into a phase where accountability will become as important as AI adoption itself.
"Organizations will increasingly focus on connecting AI investments directly to business metrics—whether it is revenue growth, customer lifetime value, or operational efficiency," Chandiramani said.
The report also points to operational barriers that continue to limit AI scaling efforts. More than half of respondents said they struggle to define deployment timelines, while others cited challenges around explainability, trust and governance.
Taken together, the findings suggest that while AI adoption in marketing is becoming mainstream, many organizations remain in the early stages of establishing the financial, operational and governance frameworks needed to translate AI investments into measurable business outcomes. For CMOs, proving value may now be a bigger challenge than deploying the technology itself.
See What’s Next in Tech With the Fast Forward Newsletter
Tweets From @varindiamag
Nothing to see here - yet
When they Tweet, their Tweets will show up here.
