Updated: March 31st, 2026

While generative artificial intelligence (GenAI) continues to roll out across the platforms we use every day, enterprise leaders are exploring how to apply it within their own operations.

In marketing, AI has the potential to reshape how businesses connect with and engage their target audiences, while also streamlining marketing workflows.

Let’s take a look at some of the use cases, benefits, and challenges of using generative AI across marketing and advertising.

What is generative AI in marketing?

AI in marketing is the use of AI tools to produce outputs that support marketing objectives.

These tools can leverage an organization’s data to generate content relevant to marketing needs. Given the right instructions, GenAI can help marketing teams streamline workflows, modify assets, create audience-specific copy, and support campaign measurement and optimization.

How does GenAI work in marketing?

Generative AI in marketing uses model knowledge and connected data sources to support a range of marketing tasks.

At a high level, the workflow looks like this:

Data access and context ingestion: Models take inputs such as brand guidelines, product information, customer data, and user prompts to inform what they generate.
Content and output generation: Models then generate marketing outputs such as messaging, creative assets, and campaign variations tailored to different audiences, goals, and channels.
Human review and testing: Marketers review, edit, and refine these outputs, then test different variations in live campaigns.
Performance-informed iteration: Results from those tests are fed back as model inputs to guide future outputs, supporting ongoing optimization.

Using AI in marketing: Common use cases

Generative AI supports a range of practical use cases across marketing.

Here are some of the most common examples:

Creating and scaling content

Enterprises can use AI to help streamline content creation and produce variations at scale for different audiences and channels.

Key applications include:

Generating marketing content across formats, including email campaigns, blog posts, social media content, product descriptions, and ad copy.
Creating multiple content variations for different audiences and use cases, enabling targeted messaging and A/B testing.
Turning raw inputs into usable marketing assets, such as summarizing transcripts and extracting key quotes for use in customer stories or case studies.

Personalization and localization

AI makes it easier for marketers to tailor content and experiences to specific audiences, increasing relevance and engagement across channels and markets.

Key applications include:

Personalizing customer interactions in real time, such as product recommendations, dynamic website content, and AI-powered assistants that respond to individual user needs.
Adapting content for defined audience segments, using customer data such as preferences, behavior, and purchase history.
Localizing content for regional markets, including transcreating messaging to reflect cultural and linguistic nuances.

SEO and content optimization

AI can help streamline search engine optimization (SEO) in a variety of ways:

Identifying gaps in content and keyword coverage, helping teams prioritize new topics and pages.
Generating content briefs and keyword-informed outlines based on search demand and competitor analysis.
Producing keyword-optimized titles, headings, and metadata.

AI can help businesses maintain and grow their social media presence in several ways.

Key applications include:

Creating social media content, including posts, captions, and variations tailored to different audiences and platforms.
Interpreting engagement data to help marketers understand what content performs best and how to improve future posts.
Assisting with audience engagement, such as drafting replies to comments or generating responses to common questions.

Event marketing and outreach

AI can help streamline event marketing by supporting messaging and communication across the attendee lifecycle.

Key applications include:

Using attendee data to segment audiences and tailor outreach, helping teams target the right people with relevant messaging.
Creating event-specific marketing assets, such as promotional copy, landing pages, and supporting materials.
Generating event communications, including invitations, reminders, and follow-up emails.

Influencer marketing

AI can help brands identify, evaluate, and collaborate with influencers to expand reach and connect with niche audiences.

Key applications include:

Using audience and performance data to find partners that align with the brand’s target customers.
Assisting with campaign planning and setup, such as drafting briefs, outlining deliverables, and structuring collaboration agreements.
Supporting influencer content creation, such as brainstorming ideas, drafting captions, and aligning content with brand guidelines.

Brand and PR management

Generative AI can also help teams monitor, manage, and protect their brand reputation across channels.

Key applications include:

Monitoring customer sentiment across channels to identify trends and emerging issues.
Generating brand-aligned content for trust-building, including proactive communications.
Drafting responses to negative feedback, helping teams address issues quickly and consistently.

Enhancing content discovery

AI helps marketers maximize the ROI of their content libraries by making it easier for customers and internal teams to find relevant resources using natural language search.

Key applications include:

Enhancing on-site search with semantic reranking, matching user intent to the most relevant content.
Summarizing and extracting key information for website visitors, helping them quickly get answers without reading full pages.
Improving internal content discovery, helping teams quickly find and reuse existing materials.

How to integrate generative AI into your marketing plans

Successful AI adoption requires a strategic shift from isolated experimentation to integrated, scalable workflows that align with core business objectives.

Rather than replacing human creativity, the goal should be to augment existing talent by embedding AI into the repeatable parts of the marketing lifecycle.

This approach enables marketing teams to accelerate high-volume tasks — such as draft generation and data classification — while freeing up resources for higher-value creative and strategic work.

Here are a few steps to get you started:

1. Audit and identify time-consuming tasks

Document your current marketing workflows to find repetitive bottlenecks, such as manual content tagging, early copy drafting, and multi-language localization. This is where generative models can provide immediate time savings.

2. Establish a secure data foundation

Ensure your proprietary brand assets and customer data are organized, permissioned, and accessible within a secure environment. This enables more accurate, grounded outputs while maintaining control over sensitive information.

Further reading: Secure AI: How to safeguard your AI systems

3. Deploy specialized models for specific needs

Move beyond one-size-fits-all tools by matching specific tasks to optimized models, such as using Cohere’s Command R+ for complex reasoning and Rerank to improve the relevance of customer-facing search results.

4. Implement in phased, measurable waves

Start with a focused pilot in a high-impact area — such as supporting email personalization at scale — and use the results to refine your approach before expanding to broader workflows.

5. Empower teams through technical training

Invest in upskilling your creative and analytical teams to work effectively with AI. Encourage a shift from execution to oversight, where teams guide, evaluate, and refine AI-generated outputs.

Benefits of generative AI in marketing

Applying generative AI across marketing workflows delivers a range of clear benefits for marketing teams.

Greater efficiency: Automating repetitive marketing tasks — such as drafting content, tagging assets, or handling routine communications — frees up time for higher-value work.
Personalization at scale: Tailoring messaging across channels and audience segments without requiring proportional increases in time or resources.
Faster content production: Generating, iterating, and adapting content quickly, reducing the time between idea and execution.
Consistent brand execution: Maintaining tone, messaging, and positioning across channels and teams by grounding outputs in shared brand guidelines.
Better decision-making: Surfacing actionable insights from large marketing and customer datasets to inform strategy and campaign optimization.
Stronger customer relationships: Delivering more relevant and timely interactions that improve engagement and trust in the brand.

Mitigating common risks of AI in marketing

The use of generative AI in marketing is not without its risks. Organizations need to address a range of challenges to ensure AI can be used responsibly and effectively across marketing workflows.

Bias in data

Risk: Biases in model training data or external data sources can lead to unfair, discriminatory, or non-compliant outputs, potentially damaging brand reputation.

Solution: Organizations should select models trained on diverse datasets, regularly audit outputs to identify bias, and apply guardrails or prompt-level controls to mitigate issues where they arise.

Lack of transparency

Risk: AI systems are often seen as “black boxes”, making it difficult to understand or explain how specific outputs are generated. This lack of transparency can reduce trust among stakeholders.

Solution: Organizations should use techniques that improve traceability and explainability, such as retrieval-augmented generation (RAG) and citation mechanisms, and maintain human oversight at key decision points.

Over-reliance on automation

Risk: Relying too heavily on AI tools can lead to reduced human input at critical stages of a campaign, where creative judgment and strategic thinking are needed.

Solution: Teams should maintain human involvement in planning, review, and decision-making, using AI to augment — not replace — creative and strategic work.

Data privacy concerns

Risk: Using generative AI with sensitive data can introduce data exposure or compliance risks if inputs, outputs, and system integrations are not properly controlled.

Solution: Enterprises should implement strong data governance and access controls to ensure sensitive data is handled securely across inputs, outputs, and integrations, and work with providers that support secure deployment options and meet relevant regulatory and compliance standards.

Quality control

Risk: Using AI-generated content in marketing campaigns can lead to factual errors, off-brand messaging, or inconsistencies due to model inaccuracies and limited contextual understanding.

Solution: Organizations should build workflows that include human review and validation before content is published, supported by clear guidelines and evaluation processes to maintain quality and accuracy.

Talk with Cohere's experts to help integrate AI into your marketing workflows

Partnering with Cohere gives your organization the strategic guidance and technical excellence required to turn AI potential into a sustainable competitive advantage.

Our team of experts specializes in deploying secure, enterprise-grade models that align with your specific brand requirements and operational goals.

Contact our team today to discuss how our solutions can streamline your marketing workflows and drive measurable growth across your business.

What is generative AI in marketing?

How does GenAI work in marketing?

Using AI in marketing: Common use cases

Creating and scaling content

Personalization and localization

SEO and content optimization

Social media management

Event marketing and outreach

Influencer marketing

Brand and PR management

Enhancing content discovery

How to integrate generative AI into your marketing plans

1. Audit and identify time-consuming tasks

2. Establish a secure data foundation

3. Deploy specialized models for specific needs

4. Implement in phased, measurable waves

5. Empower teams through technical training

Benefits of generative AI in marketing

Mitigating common risks of AI in marketing

Bias in data

Lack of transparency

Over-reliance on automation

Data privacy concerns

Quality control

Talk with Cohere's experts to help integrate AI into your marketing workflows

AI in marketing: How GenAI in marketing drives value