CMO
The marketing lens: how Chief Marketing Officers are using AI to transform content creation, personalise campaigns, and confront the ROI question that defines their function.
CMOs operate at the interface between creative production and commercial performance. They need AI to do two fundamentally different things: generate more and better content, and prove that the content drives revenue. Given the smaller sample, we focus on the clearest signals.
// HIGHLIGHT 01
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AI has unlocked content creation at scale, but predictive workflows remain untouched.
We asked CMOs about AI adoption across eleven marketing workflows. The result is a maturity map that divides cleanly into three tiers, and echoes the creative-to-analytical gradient we observed in both the CTOs and CPOs value chain.
Tier 1: Content creation is near-universal.
These are workflows where AI has become the default, not the exception.
Tier 2: Operational marketing is adopted but uneven.
Tier 2: Operational marketing is adopted but uneven.
These are workflows where AI helps, but hasn’t become indispensable.
Tier 3: Strategic and predictive workflows remain untouched.
Tier 3: Strategic and predictive workflows remain untouched.
AI has changed marketing production. Strategy remains human.
What is the current adoption status of AI in marketing workflows?
Blog/Article creation
Social media content
Image/design generation
Video content creation
Campaign analytics
Email personalization
SEO optimization
Ad copy generation
Market research
Lead scoring
Predictive Analytics
-
Occasional Use
-
Not Using
Blog/Article creation
Social media content
Image/design generation
Video content creation
Campaign analytics
Email personalization
SEO optimization
Ad copy generation
Market research
Lead scoring
Predictive Analytics
-
Occasional Use
-
Not Using
// HIGHLIGHT 02
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share[ ]
The CMO’s toolkit is consolidated around tools that weren't built for marketing.
Across three categories of marketing creative tools - text, image, and video - a striking pattern emerges: CMOs have overwhelmingly adopted general-purpose AI tools rather than marketing-specific platforms.
The marketing AI tool market may be crowded, but the winners are the horizontal players.
Text content: ChatGPT at 100%
Every CMO who answered uses ChatGPT for text content creation, and Gemini (86%) is nearly as universal. Marketing-specific tools like Jasper (29%) and Writesonic (14%) have a marginal share of usage. The general-purpose LLMs have eaten the copywriting market.
Image/creative: Nano Banana at 100%
Nano Banana appears in every CMO’s toolkit for image generation. Midjourney (57%) is the quality alternative. Adobe Firefly and other established creative tools barely feature. The image generation market has consolidated around AI-native tools, not creative suite incumbents.
Video: Veo at 86%
Google’s video generation tool dominates the CMO’s video workflow. HeyGen (43%) serves the talking-head and avatar use case. Kling, Freepik, and Midjourney (all 29%) provide alternatives. Notably, 86% of CMOs have not built any custom marketing AI tools; they’re buying off-the-shelf and integrating general-purpose AI.
General-purpose AI tools are winning over those built specifically for content creation.
General-purpose AI tools are winning over those built specifically for content creation.
While Nano Banana is the go-to tool for generating creatives, CMOs are also reaching for the likes of Midjourney, Imagen, and others.
While Nano Banana is the go-to tool for generating creatives, CMOs are also reaching for the likes of Midjourney, Imagen, and others.
Veo is the dominant name in the text-to-video generation space, but the market is brimming with options.
Veo is the dominant name in the text-to-video generation space, but the market is brimming with options.
// HIGHLIGHT 03
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share[ ]
Content volume is up. Quality is up. But the bill is up for debate.
Content volume is up. Quality is up.
But the bill is up for debate.
We asked CMOs how AI has impacted content production across three dimensions
Volume: 100% report an increase
57% say the increase is significant. 43% say moderate. Not a single CMO reports flat or declining content output. AI has turned the content faucet fully on.
Quality: 100% report an increase.
The split is slightly different - 43% significant, 57% moderate - but the direction is unanimous. CMOs are not trading quality for volume. They’re getting both. This is a meaningful finding for the “AI slop” debate: at least from the CMO’s chair, AI-assisted content is meeting or exceeding their quality bar.
Cost: mixed signals.
Here, the consensus breaks. 43% report moderate cost decreases (AI is saving money). But 29% report moderate cost increases (AI tools cost more than the savings they generate). The cost picture depends entirely on scale, tooling choices, and how you account for subscription costs versus creative agency savings.
Volume
Volume
QUALITY
QUALITY
COST
COST
// HIGHLIGHT 04
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AI has an attribution problem for marketing.
No CMO in our survey has cracked the attribution problem.
While over 57% of CMOs have seen some ROI impact, respondents note it's hard to isolate AI's contribution. 14% say it's too early to measure. Another 14% report no measurable change. And 14% describe measurement itself as their biggest challenge.
Marketing teams are producing more content, at a higher quality, faster than ever. But the majority can't connect that production to measurable revenue outcomes.
AI Marketing ROI impact
The scaling barriers explain part of why. Manual context requirements lead at 83%. AI tools that don't understand brand guidelines, campaign history, audience segments, or creative strategy require constant hand-holding. This explains why CMOs overwhelmingly use general-purpose AI rather than marketing-specific tools.
The trade-off is clear: general-purpose tools are more capable, but they know nothing about your brand, your audience, or your past campaigns. Every session starts from scratch.
Content production has been AI-transformed. But distribution, measurement, and the institutional memory that connects campaigns to outcomes remain manual tasks.
83.3%
83.3%
Too much manual context required
Too much manual context required
50.0%
50.0%
Security/compliance concerns
Security/compliance concerns
50.0%
50.0%
Budget
Waiting for tools to mature
Budget
Waiting for tools to mature
33.3%
33.3%
Poor user experience
Poor user experience
33.3%
33.3%
Lack of workflow customisation
Lack of workflow customisation
16.7%
16.7%
Model output quality concerns
Model output quality concerns
16.7%
16.7%
Tools don’t learn from feedback
Tools don’t learn from feedback
37%
37%
16.7%
16.7%
Change management resistance
Change management resistance
83.3%
Too much manual context required
50.0%
Security/compliance concerns
50.0%
Budget
Waiting for tools to mature
33.3%
Poor user experience
33.3%
Lack of workflow customisation
16.7%
Model output quality concerns
16.7%
Tools don’t learn from feedback
37%
16.7%
Change management resistance
// HIGHLIGHT 05
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share[ ]
Output quality is the brake on AI adoption across every function, and marketing is no different.
When asked about the biggest barriers to AI adoption in marketing, one concern trumps all: creative quality.
The outputs aren’t good enough to ship without significant human editing. This places marketing alongside product (CPO: 62.5% cite quality) and engineering (CTO: 68% cite accuracy/hallucinations) in identifying output quality as the universal brake on AI adoption across every function.
The CMO’s quality problem has a different texture from the CTO’s accuracy problem.
Engineers worry about hallucinations and factual errors. Marketers worry about tone, brand voice, creative resonance, and the intangible “is this good enough to represent our brand?” Both end up in the same place - a human-in-the-loop is still required - but for different reasons.
Marketing-Specific AI Adoption Barriers
57.1%
Creative quality concerns
42.9%
Attribution measurement challenges
28.6%
Brand voice consistency issues
28.6%
Cost vs ROI unclear
14.3%
Legal/Compliance review delays
14.3%
Team resistance/training gaps
Output quality is the brake on AI adoption across every function, and marketing is no different.
When asked about the biggest barriers to AI adoption in marketing, one concern trumps all: creative quality.
The outputs aren’t good enough to ship without significant human editing. This places marketing alongside product (CPO: 62.5% cite quality) and engineering (CTO: 68% cite accuracy/hallucinations) in identifying output quality as the universal brake on AI adoption across every function.
The CMO’s quality problem has a different texture from the CTO’s accuracy problem.
Engineers worry about hallucinations and factual errors. Marketers worry about tone, brand voice, creative resonance, and the intangible “is this good enough to represent our brand?” Both end up in the same place - a human-in-the-loop is still required - but for different reasons.
Marketing-Specific AI Adoption Barriers
57.1%
Creative quality concerns
42.9%
Attribution measurement challenges
28.6%
Brand voice consistency issues
28.6%
Cost vs ROI unclear
14.3%
Legal/Compliance review delays
14.3%
Team resistance/training gaps
Explore By Roles
Deep-dive into the data that matters most to your function
AI Adoption Survey Report · Indian Startups 2026
Explore By Roles
Deep-dive into the data that matters most to your function
AI Adoption Survey Report · Indian Startups 2026
Explore By Roles
Deep-dive into the data that matters most to your function
AI Adoption Survey Report ·
Indian Startups 2026
ELEVATION
Explore By Roles
Deep-dive into the data that matters most to your function
AI Adoption Survey Report · Indian Startups 2026
// HIGHLIGHT 01
[ ]
AI has unlocked content creation at scale, but predictive workflows remain untouched.
We asked CMOs about AI adoption across eleven marketing workflows. The result is a maturity map that divides cleanly into three tiers, and echoes the creative-to-analytical gradient we observed in both the CTOs and CPOs value chain.
Tier 1: Content creation is near-universal.
These are workflows where AI has become the default, not the exception.
Tier 2: Operational marketing is adopted but uneven.
These are workflows where AI helps, but hasn’t become indispensable.
Tier 3: Strategic and predictive workflows remain untouched.
AI has changed marketing production. Strategy remains human.
We asked CMOs about AI adoption across eleven marketing workflows. The result is a maturity map that divides cleanly into three tiers, and echoes the creative-to-analytical gradient we observed in both the CTOs and CPOs value chain.
Tier 1: Content creation is near-universal.
These are workflows where AI has become the default, not the exception.
Tier 2: Operational marketing is adopted but uneven.
These are workflows where AI helps, but hasn’t become indispensable.
Tier 3: Strategic and predictive workflows remain untouched.
AI has changed marketing production. Strategy remains human.
Blog/Article creation
Social media content
Image/design generation
Video content creation
Campaign analytics
Email personalization
SEO optimization
Ad copy generation
Market research
Lead scoring
Predictive Analytics
-
Occasional Use
-
Not Using
What is the current adoption status of AI in marketing workflows?
// HIGHLIGHT 02
[ ]
The CMO’s toolkit is consolidated around tools that weren't built for marketing.
Across three categories of marketing creative tools - text, image, and video - a striking pattern emerges: CMOs have overwhelmingly adopted general-purpose AI tools rather than marketing-specific platforms.
The marketing AI tool market may be crowded, but the winners are the horizontal players.
Text content: ChatGPT at 100%
Every CMO who answered uses ChatGPT for text content creation, and Gemini (86%) is nearly as universal. Marketing-specific tools like Jasper (29%) and Writesonic (14%) have a marginal share of usage. The general-purpose LLMs have eaten the copywriting market.
Text content: ChatGPT at 100%
Every CMO who answered uses ChatGPT for text content creation, and Gemini (86%) is nearly as universal. Marketing-specific tools like Jasper (29%) and Writesonic (14%) have a marginal share of usage. The general-purpose LLMs have eaten the copywriting market.
Image/creative: Nano Banana at 100%
This is the one finding specific to the Indian market. Nano Banana appears in every CMO’s toolkit for image generation. Midjourney (57%) is the quality alternative. Adobe Firefly and other established creative tools barely feature. The image generation market has consolidated around AI-native tools, not creative suite incumbents.
Image/creative: Nano Banana at 100%
This is the one finding specific to the Indian market. Nano Banana appears in every CMO’s toolkit for image generation. Midjourney (57%) is the quality alternative. Adobe Firefly and other established creative tools barely feature. The image generation market has consolidated around AI-native tools, not creative suite incumbents.
Video: Veo at 86%
Google’s video generation tool dominates the CMO’s video workflow. HeyGen (43%) serves the talking-head and avatar use case. Kling, Freepik, and Midjourney (all 29%) provide alternatives. Notably, 86% of CMOs have not built any custom marketing AI tools; they’re buying off-the-shelf and integrating general-purpose AI.
Video: Veo at 86%
Google’s video generation tool dominates the CMO’s video workflow. HeyGen (43%) serves the talking-head and avatar use case. Kling, Freepik, and Midjourney (all 29%) provide alternatives. Notably, 86% of CMOs have not built any custom marketing AI tools; they’re buying off-the-shelf and integrating general-purpose AI.
General-purpose AI tools are winning over those built specifically for content creation.
While Nano Banana is the go-to tool for generating creatives, CMOs are also reaching for the likes of Midjourney, Imagen, and others.
Veo is the dominant name in the text-to-video generation space, but the market is brimming with options.
// HIGHLIGHT 03
[ ]
Content volume is up. Quality is up. But the bill is up for debate.
We asked CMOs how AI has impacted content production across three dimensions
Volume: 100% report an increase
57% say the increase is significant. 43% say moderate. Not a single CMO reports flat or declining content output. AI has turned the content faucet fully on.
Volume: 100% report an increase
57% say the increase is significant. 43% say moderate. Not a single CMO reports flat or declining content output. AI has turned the content faucet fully on.
Quality: 100% report an increase.
The split is slightly different - 43% significant, 57% moderate - but the direction is unanimous. CMOs are not trading quality for volume. They’re getting both. This is a meaningful finding for the “AI slop” debate: at least from the CMO’s chair, AI-assisted content is meeting or exceeding their quality bar.
Quality: 100% report an increase.
The split is slightly different - 43% significant, 57% moderate - but the direction is unanimous. CMOs are not trading quality for volume. They’re getting both. This is a meaningful finding for the “AI slop” debate: at least from the CMO’s chair, AI-assisted content is meeting or exceeding their quality bar.
Cost: mixed signals.
Here, the consensus breaks. 43% report moderate cost decreases (AI is saving money). But 29% report moderate cost increases (AI tools cost more than the savings they generate). The cost picture depends entirely on scale, tooling choices, and how you account for subscription costs versus creative agency savings.
Cost: mixed signals.
Here, the consensus breaks. 43% report moderate cost decreases (AI is saving money). But 29% report moderate cost increases (AI tools cost more than the savings they generate). The cost picture depends entirely on scale, tooling choices, and how you account for subscription costs versus creative agency savings.
Volume
QUALITY
COST
// HIGHLIGHT 04
[ ]
AI has an attribution problem for marketing.
No CMO in our survey has cracked the attribution problem.
The most commonly reported outcome is a 15–30% improvement. But even then, respondents note it's hard to isolate AI's contribution. 14% say it's too early to measure. Another 14% report no measurable change. And 14% describe measurement itself as their biggest challenge.
Marketing teams are producing more content, at a higher quality, faster than ever. But the majority can't connect that production to measurable revenue outcomes.
The scaling barriers explain part of why. Manual context requirements lead at 83%. AI tools that don't understand brand guidelines, campaign history, audience segments, or creative strategy require constant hand-holding. This explains why CMOs overwhelmingly use general-purpose AI rather than marketing-specific tools.
The trade-off is clear: general-purpose tools are more capable, but they know nothing about your brand, your audience, or your past campaigns. Every session starts from scratch.
Content production has been AI-transformed. But distribution, measurement, and the institutional memory that connects campaigns to outcomes remain manual tasks.
83.3%
Too much manual context required
50.0%
Security/compliance concerns
50.0%
Budget
Waiting for tools to mature
33.3%
Poor user experience
33.3%
Lack of workflow customisation
16.7%
Model output quality concerns
16.7%
Tools don’t learn from feedback
37%
16.7%
Change management resistance
// HIGHLIGHT 05
[ ]
Marketing-Specific AI Adoption Barriers
57.1%
Creative quality concerns
42.9%
Attribution measurement challenges
28.6%
Brand voice consistency issues
28.6%
Cost vs ROI unclear
14.3%
Legal/Compliance review delays
Output quality is the brake on AI adoption across every function, and marketing is no different.
When asked about the biggest barriers to AI adoption in marketing, one concern trumps all: creative quality.
Hearing directly from these leaders confirms and deepens the picture. 47.6% are still in the early exploration stage. Another 28.6% are running pilots. Only 23.8% have reached any form of production use (14.3% are broadly deployed, 9.5% are in partial production). These functional leaders are at an earlier stage of the journey.
The maturity gap is not about reluctance. 73.7% of these leaders report being more excited about AI than a year ago. The gap is about readiness.
The right tools don’t exist yet for their specific workflows, the ROI hasn’t been proven in their functions, and most haven’t yet committed to either building or buying AI solutions. They’re waiting because the ecosystem hasn’t met them where they are yet.
CMO
The marketing lens: how Chief Marketing Officers are using AI to transform content creation, personalise campaigns, and confront the ROI question that defines their function.
The marketing lens: how Chief Marketing Officers are using AI to transform content creation, personalise campaigns, and confront the ROI question that defines their function.
CMOs operate at the interface between creative production and commercial performance. They need AI to do two fundamentally different things: generate more and better content, and prove that the content drives revenue. Given the smaller sample, we focus on the clearest signals.
CMOs operate at the interface between creative production and commercial performance. They need AI to do two fundamentally different things: generate more and better content, and prove that the content drives revenue. Given the smaller sample, we focus on the clearest signals.