7 Best Examples of AI in Marketing Use Cases

AI in marketing has evolved from simple task automation to a core driver of enterprise growth. Leading teams now use AI to refine targeting, personalize at scale, and make smarter decisions in real time. Yet many organizations remain limited to point solutions instead of adopting AI as an integrated strategy.

From real-time campaign optimization to hyper-personalized content and predictive customer insights, AI is reshaping the marketing playbook. CMOs aren’t just experimenting, 63% plan to significantly increase their investment in generative AI to reimagine how marketing works across the enterprise, according to Gartner.

Explore how top enterprises are applying AI in seven key areas to boost efficiency, sharpen targeting, and deliver clear marketing impact with human expertise guiding every step.

The role of AI in today’s marketing ecosystem

More than 80% of marketers globally are already using AI, and the market is projected to exceed $107 billion by 2028 (Statista). Its biggest impact shows up in core areas like targeting, personalization, content creation, and performance optimization. This section breaks down how AI improves speed, precision, and outcomes across the marketing lifecycle.

• Audience segmentation at scale allows marketers to move beyond traditional demographics. Using clustering and customer behavioral analytics, brands can identify micro-segments based on intent, engagement patterns, or lifecycle stage, unlocking more effective targeting.
• Predictive customer insights help teams anticipate future behavior, such as churn risk, purchase probability, or upsell potential. These insights drive timely interventions and improve resource allocation across channels.
• Real-time personalization adapts content and offers in the moment, based on live user behavior and contextual data. This enhances customer experience and improves conversion across digital properties.
• Media optimization uses AI to assess performance signals across platforms and automatically reallocate budgets toward high-performing channels and placements, improving media efficiency and return on spend.
• Content generation and tagging reduce manual workloads by automating creative tasks such as copywriting, asset tagging, and localization. This shortens production timelines and increases content discoverability.
• Marketing mix modeling and attribution provide a clearer view of which channels and messages drive outcomes. AI models quantify the impact of each touchpoint, helping teams make more informed budget and planning decisions.
• Customer journey orchestration uses AI to guide interactions across touchpoints based on behavior, context, and business objectives. It ensures customers receive the right message or experience at the right time.
• Brand sentiment analysis & risk monitoring applies natural language processing and image recognition to track how the brand is perceived across social media, review platforms, and news sources. This enables faster response to reputational risks and shifting sentiment.
• Campaign testing and optimization benefit from AI models that continuously test and refine messaging, formats, and delivery times, identifying combinations that yield the best performance.

Together, these capabilities position AI as a strategic asset in marketing. It enables enterprises to shift from static campaigns to adaptive, insight-led programs that accelerate revenue growth, improve marketing efficiency, and increase customer lifetime value.

Also Read: Connected Marketing — 7 Ways to Unify Your Marketing Operations

7 Strategic AI marketing use cases from emerging global brands

Some of the most impactful AI marketing use cases come from brands that focus on driving relevance, responsiveness, and measurable results, rather than just scale. These use cases show how AI, when aligned with business goals, enhances customer analytics, improves multichannel conversion, and drives long-term revenue and customer retention:

1. Booking.com: AI-powered comment moderation and sentiment analysis on TikTok

As TikTok grew into a major discovery platform for travel, Booking.com faced rising volumes of user comments. It faced the following challenges:

• Manage thousands of daily comments efficiently
• Categorize customer sentiment at scale
• Translate user conversations into insights for both engagement and campaign optimization

Booking.com implemented Sprinklr’s AI-powered Comment Moderation to automatically categorize messages, detect sentiment, and route comments to the right teams. Support teams responded faster, sales flagged high-intent leads, and all replies met brand and SLA standards.

Sprinklr replaced manual moderation with automated workflows. Real-time dashboards provided instant customer sentiment analysis across campaigns. In one case, over 300 comments about positive stay experiences were automatically tagged for future marketing use.

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Impact: By combining engagement and care workflows in a single platform, Booking.com not only ensured faster, more personalized responses but also turned customer feedback into actionable insights for campaign optimization.

Over a 60-day test period, Sprinklr analyzed 9,500+ inbound comments, flagged 2,000 as engageable, and saved the team more than 17 hours of manual work. These insights also guided decisions on boosting high-performing TikTok content, ensuring that ad spend was directed toward posts generating the strongest positive sentiment.

2. Tylko: AI-led product visualization for boosting AOV and purchase confidence

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Impact: The tool allowed customers to preview furniture at scale, customize dimensions, and visualize fit within their own space. As a result, product page interaction time tripled, indicating deeper engagement, while average order value rose significantly due to greater personalization and buyer confidence.

3. Veed.io: AI search and editing for faster, smarter video creation

Video content drives higher social engagement, but editing and repurposing clips at scale is a major drain on time and resources. Veed.io addresses this with AI-powered editing workflows that allow even non-technical users to work with professional-grade precision. Emotion recognition helps surface clips with the right tone, while transcription and semantic search let teams cut by keywords, sentiment, or context — replacing hours of manual scrubbing with a few targeted searches.

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Impact: Global organizations have used Veed.io’s capabilities to transform long-form recordings into bite-sized reels, ads, and highlight clips. AI search enables them to isolate compelling moments instantly, while automated tagging ensures assets are easy to reuse across campaigns. The result is a significant reduction in production timelines, faster campaign launches, and a more consistent flow of high-quality content across multiple channels.

Pro tip: Streamline your asset management with Sprinklr

Use AI-powered asset management to speed up content creation without sacrificing brand control. Sprinklr’s asset manager tags, transcribes, and enables context-based search so you can instantly find and reuse the right clips. With built-in governance for brand voice, usage rights, and compliance, you’ll produce faster, reduce risk, and stay consistent across regions.

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4. Bloom & Wild: Predictive AI for occasion-based gifting and lifecycle marketing

In lifecycle marketing, the timing of outreach often determines how effectively a brand can capture attention, influence purchase decisions, and build lasting customer relationships. A message sent too early risks being ignored, while one sent too late misses the opportunity altogether.

Bloom & Wild tackled this challenge by using predictive AI to anticipate customer needs rather than react to them. With data science and MLops frameworks, teams analyzed purchase histories and behavioral signals to predict intent. These models automated timely email and push notifications, ensuring outreach matched key customer moments.

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Impact: The brand improved campaign ROI and re-engagement. AI models analyzed historical purchase patterns and seasonal signals to anticipate moments such as gift-giving occasions or risk associated with customer churn. This allowed Bloom & Wild’s marketing teams to deliver timely, relevant messages that drive reactivation and repeat purchases.

Pro Tip: Power lifecycle marketing with Conversational AI

Sprinklr’s Conversational AI and Agent Assist work together to support the full customer journey. Conversational AI manages real-time engagement across channels like WhatsApp, Messenger, Instagram, and web chat. It handles routine queries automatically and routes complex issues to agents with full context.

Agent Assist equips those agents with historical data, recommended responses, and guided workflows to improve resolution speed and accuracy.

By unifying these capabilities, enterprise teams can reduce handoff delays, respond more effectively at scale, and tie service interactions back to marketing campaigns. This ensures timely, relevant engagement across every stage of the customer lifecycle.

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Know More: 3 New Ways Artificial Intelligence Is Powering the Future of Marketing

5. Mubert: Generative AI music for scalable content production

To help marketers and creators access custom, copyright-safe music without licensing delays, Mubert allows users to input prompts or describe use cases. Its AI then generates original audio tracks instantly, tailored to the specified needs.

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Impact: Usage grew significantly among creators, who used the platform to generate high volumes of original audio without incurring licensing fees or facing copyright restrictions. This enabled faster content production and broader distribution across channels.

6. Patagonia: AI for sustainable sourcing insights

As Patagonia scaled its operations, it faced a key challenge — using AI in a way that supported its environmental mission without compromising transparency, data privacy, or brand trust. The company needed a way to surface actionable insights from customer data while staying true to its core values.

Patagonia used AI-powered text analytics and sentiment detection to analyze reviews, surveys, and social conversations. This allowed teams to identify growing concerns about synthetic fibers and rising demand for recycled materials and regenerative practices.

Impact:

• Ethical Adoption: Patagonia implemented only energy-efficient and transparent AI tools that aligned with its mission.
• Value-Aligned Use Cases: AI was applied to predict environmental impacts in the supply chain and guide sustainable material innovation.
• Stakeholder Governance: Employees, customers, and environmental groups were involved in evaluating AI use to ensure alignment with shared values.
• Industry Advocacy: Patagonia used its influence to promote responsible AI adoption across sectors, reinforcing its leadership in ethical innovation.
• By applying AI selectively and responsibly, Patagonia turned data into actionable insights while staying true to its sustainability promise.

Pro Tip: Don’t just track sustainability sentiment — contextualize it

Measuring overall sustainability sentiment is helpful but does not show which issues matter most. Training AI models to identify topics like sourcing, ethics, and carbon footprint lets you target the signals that truly influence consumer trust.

Sprinklr Insights, combines competitive benchmarks, predictive forecasting, and unified data streams to turn broad sustainability conversations into clear priorities. This approach helps you detect emerging risks early and prove the real impact of your sustainability efforts.

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7. Threedium: AI-generated 3D assets to improve product page engagement

Threedium wanted to improve the way online shoppers interacted with product visuals, especially in fashion and luxury. To address this, they created AI-generated 3D renderings from 2D images. This allowed users to rotate, zoom and inspect product details from multiple angles in real time.

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Impact: Threedium’s clients saw a significant lift in how shoppers engaged with digital storefronts. Interactive 3D visuals increased average dwell time per page, with shoppers spending more time exploring products in detail, a proven driver of higher conversion.

Retailers also reported lower product return rates, as customers developed a clearer sense of fit, texture, and quality before purchase. On the acquisition side, campaigns featuring 3D assets outperformed static creative, delivering higher click-through rates and stronger engagement across social and display channels.

For enterprise marketers, these improvements translate into two critical levers: higher conversion efficiency on existing traffic and reduced post-purchase friction, both of which compound ROI in e-commerce at scale.

While large enterprises often dominate AI headlines, these examples highlight how innovation is flourishing in leaner, agile marketing teams. What connects them is not the size of their budgets, but the clarity of their use cases. For marketers evaluating AI adoption, the next section explains the key value that can be experienced from AI implementations.

Know More: What is Distributed Marketing? Management Concepts & Benefits

Strategic takeaways: How marketers can apply AI more effectively

AI in marketing has evolved from a tool for efficiency to a source of strategic advantage. These real-world examples demonstrate how leading brands are leveraging AI to enhance audience targeting, expedite campaign testing, optimize media spending, and deliver context-aware personalization across various channels. The result is smarter decision-making, higher conversion rates, and stronger customer relationships throughout the marketing lifecycle:

1. AI is a force multiplier, not a replacement: These brands didn’t use AI to cut costs, but to increase responsiveness, personalization and speed across marketing workflows.

2. Predictive intelligence drives timing precision: Bloom & Wild demonstrates how AI enables the transition from static journeys to behavior-triggered, conversational lifecycle marketing.

3. Visual AI boosts buyer confidence: Threedium and Tylko prove that immersive AI experiences can improve average order value and reduce hesitation in high-consideration purchases.

4. Generative content tools democratize creation: Platforms like Veed.io and Mubert enable smaller teams to produce high-quality assets faster, without relying on external production resources.

5. Effective personalization requires contextual intelligence: High-performing brands achieve personalization by leveraging behavioral data, location, context and engagement history, ensuring every interaction is timely, relevant and tailored beyond superficial cues.

6. AI delivers the greatest value when mapped to defined business KPIs: Organizations that align AI use cases with measurable marketing management goals, such as improving conversion rates, reducing churn, or enhancing customer satisfaction, achieve stronger accountability and clearer ROI.

7. Cross-functional integration is essential for scalable AI adoption: Embedding AI into both marketing and technical workflows accelerates deployment, strengthens data infrastructure and improves cross-team alignment around insights and execution.

8. Creative differentiation grows when human intuition meets AI intelligence: The most compelling brand experiences emerge when marketing teams combine creative vision with AI-powered insights, producing content that is both emotionally resonant and operationally optimized.

These use cases show that AI is most effective when tied to clear goals and integrated into workflows. From predictive insights to generative tools, it enables marketers to accelerate execution, deliver more relevant customer experiences, and achieve measurable performance gains. The focus now is on how to apply AI strategically to enhance customer experience.

Enterprise-ready AI marketing: How Sprinklr delivers at scale

Sprinklr Marketing is a unified, AI-powered platform built to activate AI across the entire customer journey, from insight to execution. It integrates listening, planning, execution, and reporting across more than 30 channels, allowing teams to move from insight to action within a single, governed workspace. With real-time sentiment analysis and trend detection, marketers can respond quickly to market shifts, optimize campaigns on the fly, and deliver consistent brand messaging across regions and teams.

The platform consolidates key functions such as omnichannel campaign execution, creative asset production, and social advertising management. AI-driven workflows reduce content production costs by up to 50%, while integrated ad management improves ROAS by over 30%. Enterprise-grade governance—including approval workflows, role-based access, and audit trails—ensures compliance across all markets, empowering local teams without compromising global standards.

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A global telecommunications company used Sprinklr to solve a common enterprise challenge: balancing centralized governance with local agility. Previously, LinkedIn campaigns were managed centrally, slowing execution and limiting responsiveness. With Sprinklr Distributed, local teams gained the autonomy to launch campaigns independently, while global teams retained control over performance tracking and compliance. The result: 259 campaigns launched in a year, 108 hours saved in execution time, and faster speed to market across regions.

Making AI actionable: What’s next for your brand

AI is reshaping how leading brands personalize at scale, optimize performance and generate measurable business impact across marketing channels. These examples show that AI is not bound to one sector or tactic. It is a strategic capability that unlocks value when applied with clarity and purpose.

As AI adoption accelerates, enterprise marketers must assess where automation, insight and generation can deliver the highest impact, whether in social listening, dynamic content, or omnichannel decision-making. Sprinklr Marketing offers the infrastructure, intelligence and integration required to operationalize AI across the marketing lifecycle.

Discover how AI can drive measurable impact across your marketing organization.