Introduction

Not too long ago, generative AI was a term you only heard in tech labs or at niche conferences. Fast-forward to today, and it’s impossible to ignore. Whether you’re scrolling Instagram reels, binge-watching YouTube Shorts, or firing off a customer service query, generative AI is quietly, but powerfully, shaping what you see, hear, and read.

That leads us to the burning question: What type of data is most suitable for generative AI? Text, image, or video?

This isn’t just a technical debate, it’s cultural, creative, and very much about business survival. Indian brands, influencers, and marketers are wrestling with this daily. Is AI better at writing crisp product reviews? Should it design catalog-ready visuals? Or is the real magic in UGC videos that carry emotional weight? With platforms like Hobo.Video mixing authentic creator content with AI capabilities, the conversation is no longer theoretical, it’s shaping budgets and strategies right now.

In this article, I’ll take you through the real-world suitability of generative AI across text, images, and video, explore success stories, lay down best practices, and show how Indian influencer marketing is already using these tools.

1. The Foundation of Generative AI

1.1 What Is Generative AI?

At its simplest, generative AI refers to machine learning systems designed to create something new instead of just analyzing what already exists. Traditional AI stops at classification, “this is spam,” “this is a dog.” Generative AI goes further. It writes a product review, draws a dog that never existed, or creates a 15-second promo video featuring a synthetic actor.

• Generative AI and text generation: Think of GPT-style models writing blogs, ad copy, or even witty tweets.
• Image data in generative AI: Tools like DALL·E, Stable Diffusion, and MidJourney churn out artwork that looks like it belongs in a professional studio.
• Video data for generative AI: Platforms such as Runway or Synthesia make it possible to build explainer videos without cameras or actors.

It’s not magic, it’s patterns. AI chews through vast amounts of data and learns how to mimic what humans do, but at an industrial scale.

1.2 Structured vs. Unstructured Data for AI

For generative AI to work well, it needs data and lots of it. But not all data is created equal.

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• Structured data is neat and tidy: numbers in a spreadsheet, a labeled dataset of “cats” vs. “dogs.”
• Unstructured data is the wild jungle of the internet: untagged selfies, casual UGC videos, and random customer feedback comments.

Interestingly, the biggest leaps in generative AI have come from unstructured data because it mirrors how humans communicate in the messy real world. Think about WhatsApp chats or influencer captions, these are rarely “structured,” but they teach AI how people actually talk.

2. Generative AI Data Suitability Across Modalities

2.1 Text Data for Generative AI

(a) Why Text Works Best

Text has always been AI’s first love. Why? Because text is everywhere, reviews on Amazon, posts on X, blogs, press releases, even government documents. And digitizing it is straightforward.

That’s why text-based generative AI is so powerful. From SEO content optimization to auto-generating personalized emails, it’s one of the best practices for selecting data in generative AI training.

• Examples of generative AI using text: Chatbots that resolve queries, blog generators that feed digital marketing, and personalized ads that feel almost human.
• In India, text AI is doing something even bigger: enabling regional language marketing. Suddenly, a Tamil-speaking customer sees the same product description that was originally in Hindi, automatically generated by AI.

(b) Generative AI Data Suitability for Text Generation

Why does text data feel so “suitable” for generative AI? Because text follows rules. Language models can pick up grammar, syntax, and intent, making predictions accurate.

Consider this: IBM reported in 2024 that AI-driven customer support bots now resolve nearly 70% of first-level queries without human input. That’s not just efficiency, that’s cost savings and customer satisfaction bundled into one.

Closer home, Indian e-commerce platforms use AI to spin out multilingual product listings. Imagine uploading one English description and instantly getting polished versions in Marathi or Bengali. That’s generative AI data suitability for text generation in action.

2.2 Image Data for Generative AI

(a) Why Images Drive Creativity

When it comes to creativity, text pales in comparison to visuals. Image data in generative AI fuels product mockups, campaign graphics, and even quirky memes. The appeal? It’s cheaper and faster than hiring photographers or designers.

Here’s how it works: AI tools crunch millions of tagged photos, learning everything from color palettes to fabric textures. The next time a fashion startup wants a catalog image, they don’t always need a studio shoot, AI can generate it.

Indian fashion brands have started experimenting with this approach, and it’s changing how smaller labels compete with giants like Zara or H&M.

(b) Examples of Generative AI Using Images

• Flipkart has dabbled with AI-driven banner ads.
• Influencer marketing India campaigns are layering AI UGC images with real creator content, making feeds feel both authentic and fresh.
• Famous Instagram influencers are using AI filters and edits not just for vanity but to create unique aesthetics that set them apart.

2.3 Video Data for Generative AI

(a) The Power of Moving Content

Ask yourself this, when was the last time you remembered a static ad compared to a short video? Probably never. Video dominates because it captures emotion. In fact, Statista reports that video made up 82% of all global internet traffic in 2024. That’s why video data for generative AI is such a hot topic.

But here’s the challenge: generative AI data suitability for video is trickier. Video blends audio, motion, and visuals. Training requires hours, sometimes terabytes of footage. It’s costly and technically complex. Yet, the potential payoff in engagement is massive.

(b) Examples of Generative AI Using Video

• Startups like Synthesia now let companies build explainer videos without hiring actors.
• In India, FMCG brands are testing AI-enhanced UGC videos for product demos. Imagine a creator making a quick clip, and AI polishing it with subtitles and animations.
• EdTech giants like BYJU’S and Vedantu are exploring AI-generated teachers, virtual instructors who can deliver lessons in any language, any time.

3. Comparing Generative AI Data Suitability

3.1 Text vs. Image vs. Video

3.2 Structured vs. Unstructured Data

Here’s where the trade-off becomes interesting.

• Structured data: Think labeled datasets or organized spreadsheets. Great for accuracy.
• Unstructured data: Raw UGC videos, casual comments, messy feedback threads. Great for realism.

The sweet spot is a mix, structured data ensures stability, while unstructured data ensures relatability. And if you’re in influencer marketing, realism always wins.

4. Best Practices for Selecting Data in Generative AI

1. Stay Relevant – Don’t throw in random datasets; align them with your industry.
2. Respect Culture – For India, that means training on regional languages and cultural cues.
3. Mix Modalities – A campaign that blends text, image, and video outperforms single-medium approaches.
4. Filter Rigorously – Weed out biased, toxic, or misleading data.
5. Leverage UGC – Use real creator input. Blending AI with genuine human content builds trust.

Campaigns combining multiple data types (text + image + video) achieved 25% higher engagement than campaigns relying on just one format. Numbers like that are hard to ignore.

5. Generative AI in Indian Influencer Marketing

5.1 UGC Meets AI

If you’re in influencer marketing, you already know the value of UGC videos. Add AI into the mix, and the possibilities explode. Platforms like Hobo.Video are leading the charge with AI influencer marketing.

Take a skincare brand. Instead of asking creators to manually add subtitles, AI can auto-generate them in multiple languages. Or imagine influencers reusing the same clip with different AI-driven backgrounds or effects. That’s speed, scale, and creativity in one package.

5.2 How Brands Use Generative AI in Influencer Campaigns

• E-commerce: Multilingual AI-generated product unboxing clips.
• Fashion: Try-on demos powered by image AI.
• Food: AI-enhanced recipe videos with both creator and AI-generated elements.

5.3 Why Hobo.Video Leads the Way

Hobo.Video isn’t just another marketplace. It’s positioned as a top influencer marketing company and best influencer platform for Indian brands that want both authenticity and innovation. Its AI UGC tools give creators and brands a competitive edge, something generic platforms can’t match.

6. Case Studies of Generative AI Data Suitability

6.1 Text Case: E-commerce Product Descriptions

Amazon India leveraged AI to auto-create product summaries. The payoff? A 15% bump in click-through rates, proving text AI delivers immediate value. Beyond just numbers, the AI-generated descriptions also allowed the team to localize content for regional languages, reaching Hindi, Tamil, and Bengali-speaking customers faster than ever. This not only enhanced user experience but also reduced manual workload for content teams, letting them focus on more creative tasks.

6.2 Image Case: Fashion Catalogs

Myntra tested AI for catalog photos and shaved off 40% of photoshoot expenses while keeping engagement metrics healthy. AI-generated images also enabled rapid experimentation with outfit combinations, backgrounds, and lighting, which would have been costly in a traditional studio. The brand could A/B test visuals across multiple campaigns simultaneously, allowing marketing teams to respond quickly to trends and consumer preferences.

6.3 Video Case: EdTech Tutorials

Vedantu’s AI-generated instructors cut content creation timelines by 30%, helping the platform scale faster without losing quality. In addition, the AI tools allowed personalized lessons for students in different regions, adapting scripts and examples to local contexts. This meant learners could receive more relatable content, improving comprehension and engagement while giving instructors time to focus on interactive teaching methods.

7. The Whole Truth of Generative AI for Marketers

Here’s the thing: AI isn’t here to replace people, it’s here to scale them. Influencers bring heart, emotion, and trust. Generative AI brings efficiency, reach, and personalization. The brands that thrive will be the ones who blend both worlds. Those that don’t risk looking robotic, losing their audience’s faith.

Summary : Key Learnings

• Generative AI data suitability is modality-specific: text is efficient, images creative, videos engaging.
• Structured vs. unstructured data for AI impacts accuracy vs. realism.
• Best practices for selecting data in generative AI training involve relevance, diversity, and cultural awareness.
• Indian brands are already testing AI in UGC videos and influencer marketing.
• Hobo.Video is bridging human creativity with AI-driven scale.

About Hobo.Video

Hobo.Video is India’s leading AI-powered influencer marketing and UGC company. With 2.25 million creators, it offers end-to-end campaign solutions crafted for growth. Combining AI precision with human creativity, it ensures maximum ROI.

Services offered:

• Influencer marketing
• UGC content creation
• Celebrity endorsements
• Product testing and feedback
• Marketplace reputation management
• Regional and niche influencer campaigns

Trusted by Himalaya, Wipro, Symphony, Baidyanath, and the Good Glamm Group, Hobo.Video is redefining the influencer marketing playbook.

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