As the frenzied hype around generative AI cools off and as we get into the year of ideation, earlier adopters of AI are starting to see the results of initial experimentation. And these conversations are increasingly shifting to a more problem-oriented mentality. A lot of people were understandably swept up in the excitement of all that AI can do, only to find that some use cases were too risky or that those problems could be solved with traditional methods that were less costly. And that becomes the challenge: deciding not what AI can fix, but what it should fix.

On the whole, AI has proven its ability to reduce costs and increase revenue. An IDC study found that for every $1 companies invest in AI, they are realizing an average of $3.50 in return, and 5% of organizations worldwide are realizing an average return of $8. No business can afford to turn its back on that kind of competitive advantage.

Our new ebook, 9 Ways AI Can Uplevel Your Business Right Now, lays out meaningful ways that businesses across industries are leveraging AI to gain a competitive edge. They fall into two categories: the new, generative AI, and the seasoned, predictive ML. 

So as we all start to ask the questions, ”What’s our most pressing business need?” and “Is AI the best option to solve that problem?” you can reference the book covering these nine real-world, ready-to-go use cases: 

The winning ways to apply generative AI:

1. Build a customer service agent assistant 

2. Understand customer sentiment

3. Process and analyze unstructured documents

4. Predict customer behavior

When traditional methods, such as applying predictive ML, work best: 

1. Forecast demand 

2. Predict customer behavior

3. Conduct predictive maintenance 

4. Detect cybersecurity threats

5. Catch and prevent fraud

AI promises to change the world, but change comes in waves and incrementally. Given that generative AI applications in enterprises are still in their early days, it’s expected that organizations will implement AI through these individual point solutions for specific use cases. However, it’s crucial to recognize that your company should investigate and establish a comprehensive AI strategy, much like it needs an overarching data strategy. 

The key is to identify a starting point that will yield significant and measurable results, not only to justify that initial investment but also to make a strong case for continued funding and AI initiative expansion. By focusing on areas where AI can drive meaningful outcomes, you’ll build a solid foundation for a broader, more integrated AI strategy that aligns with your company’s overarching goals. 

For more information on how to get started with the nine use cases outlined above, download 9 Ways AI Can Uplevel Your Business Right Now.