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Artificial intelligence has become nearly universal in marketing content creation, but its influence drops sharply when it comes to strategy, attribution, and predictive decision-making, according to new data from CMOs across startups stated in “The State of AI Adoption in Indian Startups” report.
The findings map AI adoption across 11 marketing workflows and reveal a clear three-tier structure. At the top, content creation has effectively become AI-first.
Across use cases such as blog writing, social media content, image generation, and video creation, 85.7% of CMOs report active and extensive usage, with the remaining 14.3% still exploring.
Notably, no respondents reported zero usage in these areas, signalling that AI is now embedded in day-to-day content production.
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Adoption becomes less consistent as workflows move beyond content. Functions such as campaign analytics, email personalisation, SEO optimisation, and ad copy generation are seeing growing usage, but have not yet reached the same level of standardisation. AI is supporting execution in these areas, but has not become indispensable.
At the lowest level of adoption, strategic and predictive workflows remain largely untouched. Use cases such as market research, lead scoring, and predictive analytics continue to be primarily human-led, suggesting that while AI has transformed how marketing output is created, it has yet to meaningfully shape decision-making.
The tools powering this shift are also notable. CMOs are overwhelmingly relying on general-purpose AI platforms rather than marketing-specific software. ChatGPT is used by 100% of respondents for text content, with Gemini close behind at 86%. In comparison, specialised tools like Jasper and Writesonic see limited adoption.
A similar trend is visible in creative workflows, where tools like Nano Banana and Midjourney dominate, while traditional platforms such as Adobe Firefly have a smaller footprint. In video, Veo leads usage, followed by HeyGen. Most CMOs are not building their own tools either, 86% rely on off-the-shelf solutions.
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This widespread adoption is translating directly into output. Every CMO surveyed reported an increase in content volume, with 57% describing it as significant and 43% as moderate.
Quality has also improved across the board, with 100% reporting better output, split between 43% significant improvement and 57% moderate gains. The data suggests that, at least from a marketing perspective, AI is not forcing a trade-off between speed and quality.
However, the cost picture is less clear. While 43% of CMOs report moderate cost reductions, 29% say costs have increased, indicating that the financial impact of AI depends heavily on scale, tool selection, and how companies balance software subscriptions against agency or manpower savings.
More importantly, measuring the return on these investments remains a challenge. Although over 57% of CMOs report some level of ROI impact, most say they are unable to isolate AI’s direct contribution.
14% say it is too early to measure, another 14% report no measurable change, and a further 14% identify measurement itself as their biggest challenge. The result is a growing disconnect between higher output and clear business attribution.
Operational constraints are also slowing deeper adoption. The biggest issue cited is the need for manual context, with 83.3% of CMOs saying AI tools require constant input because they lack awareness of brand guidelines, audience segments, and past campaigns.
Other concerns include security and compliance (50%), budget limitations (50%), poor user experience (33.3%), and lack of workflow customisation (33.3%).
Quality, too, remains a limiting factor, though in a different way than in engineering or product teams. 57.1% of CMOs cite creative quality as a key concern, particularly around tone, brand voice, and whether outputs are good enough to publish without heavy editing. Attribution challenges (42.9%), brand consistency (28.6%), and unclear cost versus ROI (28.6%) further complicate adoption.
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