How to use AI for personalization—and 8 other AI use cases
AI is everywhere, but where will you actually see return on investment? We cover nine AI use cases and the tech, tools, and skills you need to achieve them.
Jan 28, 2025 • 4 Minute Read
AI promises to boost productivity, reduce costs, improve the customer experience, and generally take your organization to the next level.
But it isn’t a silver bullet. In fact, 80% of AI projects will fail.
If you want yours to succeed, you need to identify a real problem AI technology can solve. We’ve identified nine key use cases for AI, including the technologies, tools, and skills your teams need to put them into action.
We’ll dive into the AI personalization use case here, but if you want all the insights, including how to use AI for cybersecurity and threat detection, knowledge graphs, and more, get the 9 Real-World AI Use Cases guide.
Why use AI for personalization
These days, personalization goes beyond including your name on an email greeting. It’s about delivering the right product recommendations, content, or services at the right time across multiple platforms.
Think about the streaming service that shows you exactly what you’re in the mood for or an e-commerce site that suggests the perfect add-on product to your order. If you get personalization right, you can create those feel-good moments where customers think, “Wow, they really get me!”
Tailoring experiences manually might work for a handful of customers, but when you’re dealing with thousands—or even millions—it’s not realistic. AI enables personalization at scale. It can do the majority of the work, analyzing data and delivering personalized recommendations or content in real time.
AI can also improve the relevance of those recommendations. Customers won’t stick around if your product or service doesn’t match their needs or interests. AI understands customer preferences and serves up exactly what they’re looking for.
The benefits of AI-powered personalization
AI-powered personalization benefits everyone, from your organization as a whole to your end customers. When AI’s involved in personalization:
- Organizations boost the effectiveness of marketing and outreach, ultimately increasing return on investment (ROI) and customer lifetime value
- Employees save time with automated tools that handle segmentation, recommendations, and personalization, freeing them up to focus on more important tasks
- Customers enjoy experiences that feel custom-made for them—relevant offers, tailored content, and recommendations that actually make sense
3 ways to use AI for personalization
Here’s how to leverage AI-powered personalization in your organization for the most impact.
1. Create personalized recommendations
E-commerce, streaming platforms, or content-heavy websites can provide tailored product or content recommendations. Consider using technologies like:
2. Deliver personalized customer journeys
Create personalized email campaigns and website experiences with dynamic customer journeys. To do this, consider using technologies like:
3. Build custom personalization models for unique needs
Need something unique? Build bespoke AI models to do things like analyze customer sentiment, tailor in-app experiences, or predict future preferences based on behavior. Consider using technologies like:
TensorFlow or PyTorch integrated with your CRM or other systems
Critical tech skills needed for personalization with AI
Your teams will need these skills to make the most of AI-powered personalization.
Platform familiarity
Learn platforms like Salesforce Einstein or Amazon Personalize to set up and optimize personalization experiences.
Data analysis
Understand customer data, including behaviors, preferences, and trends.
Programming languages
Develop proficiency with Python libraries, like TensorFlow or PyTorch, for building advanced personalization models.
Marketing and customer experience strategy
Understand how to align personalization with business goals.
Integration
Get experience integrating AI personalization tools with existing systems, like CRMs or e-commerce platforms.
Metrics to track the success of AI-driven personalization
Once you implement AI for personalization, track success using these metrics:
- Engagement rates: Monitor metrics like click-through rates, time spent on site, or interaction with personalized content
- Conversion rates: Track the percentage of personalized recommendations or campaigns that lead to purchases, subscriptions, or other key actions
- Customer retention: Evaluate whether personalized experiences reduce churn and keep customers coming back over time
- Customer satisfaction: Use surveys, reviews, or net promoter scores (NPS) to gauge how customers feel about the relevance and quality of their experiences
- Marketing ROI: Measure ROI for personalized campaigns compared to generic approaches
Uncover 8 more AI use cases for your organization
If you’re ready to use AI to make an impact, get your free copy of the 9 Real-World AI Use Cases guide. It includes more tips for AI-powered personalization, plus eight additional use cases to get the most out of your AI investments.
Need help building your teams’ AI skills? Our hands-on learning platform helps everyone build the AI skills they need with expert-led courses, labs and sandboxes, and assessments. Learn more.