Ten AI Use Cases That Should Be on Every Leader’s Radar

Last month, Google Cloud published an extensive list of 321 real-world AI implementations that are absolutely worth taking a look at.

While the breadth of applications is pretty remarkable, what caught my attention were that many of the use cases, which span lots of industries, felt immediately actionable.

Organizations could essentially start implementing them today without massive organizational overhaul or technical complexity.

The value in a list like this isn’t that you’ll copy these exact use cases; rather, the value is reading about what other organizations are doing to find inspiration for your own.

Here are ten highlights for leaders and marketers weighing the benefits of AI adoption.

1. Accelerating Marketing Campaign Development: Carrefour deployed its Marketing Studio in just five weeks, enabling marketers to build personalized campaigns across various social networks with just a few clicks. The key insight here isn’t just the speed—it’s that they started with a specific, contained use case that delivered immediate value.
2. Enhancing Email Efficiency: Just Salad’s customer service team reduced time spent writing emails by 30-35% using AI assistance in Google Workspace. This isn’t about replacing human interaction—it’s about making it more efficient and allowing teams to focus on building relationships rather than drafting responses.
3. Streamlining Content Creation: Adore Me transformed their product description process from a 30-40 hour monthly task to something that takes just one hour. They didn’t try to automate everything at once—they started with a specific, time-consuming task that was ripe for improvement.
4. Improving Document Analysis: Thomson Reuters found that AI could make some document processing tasks up to 10 times faster. What’s notable is that they focused on processing entire documents in context, maintaining quality while dramatically improving speed.
5. Optimizing Customer Support: Best Buy reduced average call times by 30-90 seconds while improving both customer and agent satisfaction. They didn’t replace their support team—they augmented them with tools to make their jobs easier and more effective.
6. Enhancing Creative Development: The Estée Lauder Companies built an AI language assistant that helps brand leaders generate various creative content and summarize meetings. It’s a practical example of using AI to support creative work rather than replace it.
7. Elevating Sales Communications: Dun & Bradstreet developed an email tool that helps sellers create tailored, personalized communications to prospects and customers. The focus was on improving quality and personalization, not just speed.
8. Streamlining Meeting Management: Vimeo teams boosted productivity by using AI to automate note-taking and generate meeting summaries. This allowed them to focus on collaboration and creative thinking rather than administrative tasks.
9. Accelerating Research and Analysis: Ipsos built a data analysis tool that eliminates the need for time-consuming requests to data analysts. The tool helps market researchers work more independently while maintaining accuracy.
10. Improving Campaign Performance: Working with Google, Monks helped Hatch build a personalized ad campaign that delivered an 80% improved click-through rate and 46% more engaged site visitors—while reducing campaign delivery time by 50% and costs by 97%.

Why These Examples Matter

What makes these implementations notable isn’t their complexity—it’s their clarity of purpose.

Each organization identified a specific challenge and applied AI in a focused way to address it. They didn’t try to boil the ocean. They didn’t attempt complete departmental transformations. Instead, they started with concrete, measurable objectives.

If you’re considering AI adoption, these examples offer a valuable template:

Start Small but Strategic: Choose specific use cases that can deliver measurable value quickly. The goal isn’t to transform everything at once—it’s to build momentum through successful implementations.

Focus on Augmentation: The most successful implementations enhance human capabilities rather than trying to replace them. They make your team more effective, not obsolete.

Measure What Matters: Each of these examples came with clear metrics—time saved, efficiency gained, or performance improved. Define your success metrics before you begin.

Build on Success: Once you’ve proven value in one area, you can expand to others. Success breeds success, and small wins build confidence for larger initiatives.

The AI Adoption Journey

What you should find encouraging about these examples is their achievability. None required massive organizational overhaul or years of implementation. They started with clear problems, applied focused solutions, and delivered measurable results.

As you consider your own AI initiatives, look for similar opportunities—areas where AI can enhance existing processes rather than replace them entirely.

The goal isn’t to revolutionize everything overnight. It’s to begin a thoughtful journey toward more efficient, effective operations.

The future of AI isn’t about dramatic transformation—it’s about practical implementation. Where will you start?

I built a spreadsheet that will help you prioritize and analyze your use case. Email me and I’ll send it to you.