How to Get Started Using Generative AI

Only 34% of organizations using generative AI are formalizing their processes, according to a recent Alteryx survey. Most are either dipping their toes in the water or still discussing GenAI in meetings without leveraging it. If your organization is in this position, it can be frustrating to feel like you’re one step behind. However, there is still time to be among the early adopters who will see drastically improved efficiency, customer satisfaction, and data inquiry capabilities.

To get started using generative AI, your company needs to prepare its data for the technology. Next, you need to target the generative AI capabilities that will most benefit your organization and then build those systems directly within your digital ecosystem. That way, you do not expose your enterprise data outside of your organization. Below, we will walk you through each step and provide resources to help you get started with generative AI.

Step #1: Preparing Your Data

If you want customers and employees to rely on the insights of generative AI, you better make sure the data these systems are using is high-quality. This is a problem for most companies, as 77% of data professionals believe that their organizations suffer from a lack of data quality.

What happens if generative AI systems use low-quality data? The system will deliver inaccurate or outdated information, cratering the trust employees and customers have in GenAI and heightening frustration with your applications. Generative AI will also begin to “hallucinate” in cases where no relevant data is available, simply making up information as it goes.

In our recent e-book on getting your data ready for generative AI, we outline five key data quality metrics to track for better outcomes. We also discuss ways to improve data quality iteratively. That way, you do not need to wait years to begin utilizing generative AI.

Step #2: Choosing the Right Use Cases

Generative AI could help just about any part of your company’s operations. However, it is important to target the use cases that will drive the most business value to your organization. Simply put: Consider your company’s top goals and then figure out how generative AI can advance them.

For example, let’s say you run a fast-food chain with many franchises. Franchisees may be having difficulties understanding all the rules for opening a new location, lowering the success rate of their franchises. You can use generative AI as a copilot that can field all franchisees’ questions. You could even have GenAI provide insights on the best locations for a franchise and which menu items are most likely to succeed in any given area.

In our recent blog post, we offered an in-depth look at generative AI use cases for businesses, which included streamlining data inquiries for employees and personalizing the customer experience.

Step #3: Build Generative AI Systems Within Your Digital Ecosystem

Now that your data is up to snuff and you know exactly how you would most like to use generative AI, the next step is to build these capabilities within your own digital ecosystem. This gives you full control to leverage generative AI exactly how you want. Plus, you can avoid the headaches of voluntarily giving away your enterprise data to train the public ChatGPT.

As we discussed in our blog post about privacy concerns within the public ChatGPT , a countless number of companies have already suffered data leaks because of information that employees submitted in inquiries. That included Samsung, which allegedly suffered three separate incidents, among them being an instance where a worker “reportedly submitted an entire meeting to the chatbot and asked it to create meeting minutes.”

Instead of voluntarily giving your data to competitors and just about anyone else on the internet, ensure your data stays within your company by building capabilities within your internal systems.

At Programmers Inc., we can help you build these GenAI systems and support them for the long haul. That way, as language learning models (LLMs) and other technologies evolve, your organization can dynamically leverage them to make even more intuitive and helpful applications. Learn more about our generative AI services.

Final Thoughts

No organization wants to be left out of the generative AI revolution. However, many companies are simply talking about this technology instead of beginning a robust generative AI initiative.

You can build a viable foundation for your company’s generative AI efforts by preparing your data (including enhancing data quality), selecting the use cases that will most benefit your organization, and finally, iteratively building these capabilities within your own digital ecosystem. Take the first step in that journey by downloading our e-book on getting your data ready for generative AI.

Only 34% of organizations using generative AI are formalizing their processes, according to a recent Alteryx survey. Most are either dipping their toes in the water or still discussing GenAI in meetings without leveraging it. If your organization is in this position, it can be frustrating to feel like you’re one step behind. However, there is still time to be among the early adopters who will see drastically improved efficiency, customer satisfaction, and data inquiry capabilities.

To get started using generative AI, your company needs to prepare its data for the technology. Next, you need to target the generative AI capabilities that will most benefit your organization and then build those systems directly within your digital ecosystem. That way, you do not expose your enterprise data outside of your organization. Below, we will walk you through each step and provide resources to help you get started with generative AI.

Step #1: Preparing Your Data

If you want customers and employees to rely on the insights of generative AI, you better make sure the data these systems are using is high-quality. This is a problem for most companies, as 77% of data professionals believe that their organizations suffer from a lack of data quality.

What happens if generative AI systems use low-quality data? The system will deliver inaccurate or outdated information, cratering the trust employees and customers have in GenAI and heightening frustration with your applications. Generative AI will also begin to “hallucinate” in cases where no relevant data is available, simply making up information as it goes.

In our recent e-book on getting your data ready for generative AI, we outline five key data quality metrics to track for better outcomes. We also discuss ways to improve data quality iteratively. That way, you do not need to wait years to begin utilizing generative AI.

Step #2: Choosing the Right Use Cases

Generative AI could help just about any part of your company’s operations. However, it is important to target the use cases that will drive the most business value to your organization. Simply put: Consider your company’s top goals and then figure out how generative AI can advance them.

For example, let’s say you run a fast-food chain with many franchises. Franchisees may be having difficulties understanding all the rules for opening a new location, lowering the success rate of their franchises. You can use generative AI as a copilot that can field all franchisees’ questions. You could even have GenAI provide insights on the best locations for a franchise and which menu items are most likely to succeed in any given area.

In our recent blog post, we offered an in-depth look at generative AI use cases for businesses, which included streamlining data inquiries for employees and personalizing the customer experience.

Step #3: Build Generative AI Systems Within Your Digital Ecosystem

Now that your data is up to snuff and you know exactly how you would most like to use generative AI, the next step is to build these capabilities within your own digital ecosystem. This gives you full control to leverage generative AI exactly how you want. Plus, you can avoid the headaches of voluntarily giving away your enterprise data to train the public ChatGPT.

As we discussed in our blog post about privacy concerns within the public ChatGPT , a countless number of companies have already suffered data leaks because of information that employees submitted in inquiries. That included Samsung, which allegedly suffered three separate incidents, among them being an instance where a worker “reportedly submitted an entire meeting to the chatbot and asked it to create meeting minutes.”

Instead of voluntarily giving your data to competitors and just about anyone else on the internet, ensure your data stays within your company by building capabilities within your internal systems.

At Programmers Inc., we can help you build these GenAI systems and support them for the long haul. That way, as language learning models (LLMs) and other technologies evolve, your organization can dynamically leverage them to make even more intuitive and helpful applications. Learn more about our generative AI services.

Final Thoughts

No organization wants to be left out of the generative AI revolution. However, many companies are simply talking about this technology instead of beginning a robust generative AI initiative.

You can build a viable foundation for your company’s generative AI efforts by preparing your data (including enhancing data quality), selecting the use cases that will most benefit your organization, and finally, iteratively building these capabilities within your own digital ecosystem. Take the first step in that journey by downloading our e-book on getting your data ready for generative AI.

Let us know how we can help you.