Use cases for AI

1. Use cases for AI

Welcome back!

2. Business areas for AI

AI is already being applied to areas across the business including marketing,

3. Business areas for AI

sales,

4. Business areas for AI

customer service,

5. Business areas for AI

and engineering. It is being used to automate processes, help customers solve problems, detect fraud, and analyze text such as sentiment of social media posts and reviews. With increasing access to generative AI tools, which are also becoming rapidly better, there are even more possibilities. Let’s look at a couple of examples in three business areas.

6. Marketing: Dynamic Pricing

Starting with a marketing use case, specifically, dynamic pricing. AI can optimize pricing strategies in real-time through the analysis of

7. Marketing: Dynamic Pricing

market demand,

8. Marketing: Dynamic Pricing

competitor pricing,

9. Marketing: Dynamic Pricing

customer behavior, inventory, and more. In this way, the company can maximize revenue.

10. Marketing: Segmentation & Targeting

Here is another example for segmentation and targeting. Customer data - behavioral information, demographics, purchase history

11. Marketing: Segmentation & Targeting

could be used within an AI solution to

12. Marketing: Segmentation & Targeting

segment users into cohorts.

13. Marketing: Segmentation & Targeting

Likewise, this information could be used for predicting the likelihood of a purchase and recommendations.

14. Marketing: Segmentation & Targeting

Finally, it can support ad targeting with the optimization of placement and budgets for ads with the goal of increasing return on investment.

15. Marketing: Content

A final marketing example is content. By now, it is clear that generative AI solutions can help create long-form text and images all of which can be used for marketing materials. Adding specific customer data, it can suggest content that will truly resonate with customers. In most cases, it will reduce the time to a draft, saving marketers time and helping them start the creation process at “good” versus “ok”.

16. Customer Service: Chatbots

In customer service, most people have interacted with a chatbot at some point in time either online or over the phone.

17. Customer Service: Chatbots

It has become commonplace because it is one of the more affordable options for an AI-powered solution.

18. Customer Service: Chatbots

Likewise, there are a lot of companies with easy to implement chatbot products.

19. Customer Service: Chatbots

Chatbots allow customers to interact with the business 24/7, which also means instant responses. They also allow for a “first layer” to customer service saving time and effort of human customer service reps for more complicated problems. The result could be reduced customer wait times, higher revenue - through chatbot-assisted purchases - and avoided costs - because a 24/7 service team can be avoided. Similar to the marketing example, advanced chatbots can also personalize the experience based on customer history.

20. Engineering: Cybersecurity

There are several ways an AI solution can support an engineering team from developer productivity to an improved infrastructure. One example is a more effective security system that can detect malicious cyberattacks.

21. Engineering: System Health

Similarly, it can monitor the system and predict failures through the analysis of continuous system logs and telemetry.

22. Engineering: System Health

Set up properly, it could even alert and proactively schedule maintenance.

23. Engineering: Documentation

One last example is documentation. Generative AI can help write comprehensive documentation of systems and processes so engineers can spend their valuable time on development. Secret or trademarked projects should be cautious on which AI solution is used to create this documentation. It's good practice, in general, to be mindful of the terms of use. There are many business use cases. Within any of these situations, it is important that employees leverages AI and do not rely on it. It is another tool to support them towards an outcome.

24. Let's practice!

Hopefully, you are starting to get an idea of areas of your business which may be ready for an AI proof of concept. We will cover a framework for how to identify and prioritize use cases in the next lesson.

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