Generative AI can be a true game-changer in your company. This technology enables the execution of even complex tasks without all the intricacies of creating a brand-new AI algorithm/tool from scratch. While implementing a generative AI solution is surely a good idea, there are still some best practices you need to stick to in order to develop and maintain your tool effectively and safely. And that’s what we want to talk about in this post.
We came up with six important practices that will help you make the most of generative AI without wasting time and resources. Some of the guidelines also include the data security aspect so that your AI tool operates within legal limits. Let’s get right to it.
Select a specific use case with the biggest impact on operations
It’s easy to get overwhelmed by the capabilities of generative AI. That’s why we advise you to start with just one business area, preferably the one that has a big impact on your daily operations. When working on a new generative AI solution, start by identifying a use case that addresses a pressing need in your organization. Ideally, it should be an area where AI can significantly optimize workflow(s), increase productivity, save time, or increase revenue.
For instance, if your company struggles with customer support, automating responses with generative AI (by adopting an intelligent customer assistant) could reduce wait times and improve CX. Only when this one area is fixed can you move on to the next one. Otherwise, you’ll find yourself in a never-ending implementation process.
One more hint: It’s good to prioritize use cases that can be measured in some specific way. If you see cost reductions or increased process efficiency. This will help you assess the profitability of your tool.
Assess data (as well as other) requirements
The majority of generative AI tools require data to work with. Your data needs to be of high quality and well-organized. This may require some preliminary work, but it’s surely worth the effort. You can start by assessing what kind of data you need and what needs to be done to prepare your data for AI-related applications (i.e., to train your data model). In some cases, you’ll need to go to some external sources to get more data.
However, there are other requirements that need to be considered, too. For starters, you need to consider technical requirements such as computational power and integration capabilities with existing systems. Most of the time, generative AI tools work in conjunction with other systems and platforms you use in your business. And on top of that, you need to ensure that there are no bottlenecks related to data accessibility or storage. Proper assessment of all these requirements will help you implement your gen AI tool effectively and avoid potential technical problems or delays in implementation.
Establish data privacy principles and guidelines for your team
Artificial intelligence usually uses some kinds of customer data, so it’s important to establish some data privacy principles and guidelines for your team. Of course, these guidelines will differ depending on the regulations in your country (if you operate in the European Union, it’s GDPR). In every case, you need to protect your customers’ data and ensure it’s used in an ethical and safe manner. You should particularly focus on protecting sensitive information (if that’s something you use in your business).
Additionally, it’s very important to ensure that your team understands these principles and knows how to handle data responsibly. It’s a good practice to implement anonymization techniques where possible and implement some access-management features. Another good idea is to conduct regular data and AI audits to ensure everything works within the legal requirements.
Run tests before publishing your solution
You need to make sure your AI solution works correctly and delivers the expected outcome(s). It’s a good idea to run some tests, e.g., on samples of data, to ensure this is the case with your tool. Also, testing procedures come in handy when you want to ensure the reliability and performance of your gen AI tool.
Your team should check how your AI tool behaves with different types of data and under various circumstances. This will help you spot potential problems, inaccuracies, or even bias. Run all these tests before you publish your tool. If you have a team that can help you with the texts, ask them to collect and share feedback on the system’s operations so that you can implement necessary corrections before the start.
Train your team and designate an AI project manager
Your team should be acquainted with your new AI tool and know how to use it. That’s why extensive training and workshops may be necessary. Your training should cover the following areas:
How the solution works and why it was implemented
How to operate it effectively
How to interpret its outputs
How to troubleshoot common issues (and with whom)
If you have a larger company, it’s a good idea to designate an experienced team member who will be responsible for overseeing the AI solution. This person should be accountable for monitoring the solution’s performance, managing updates, and addressing any concerns.
Optimize your solution regularly
Generative AI solutions are not static or once and for all. They require continuous optimization to adapt to changing conditions and improve performance. Our advice is to regularly monitor your AI model’s outputs and check its performance to ensure it works as expected. If some major corrections/modifications are necessary, remember to educate your team on what was done and why. Also, remember to update your model as new data becomes available so that your AI tool can evolve together with your company and always deliver the best possible results. And when everything works properly, it’s time to move on to the next application or business area!
Wrapping up
Generative AI can help you with many different tasks in your business. If you’re thinking about implementing this tool, the first step is to get in touch with a trusted Generative AI Solutions Development Partner that will help you design and implement your gen AI tool. Find out more: Addepto.com.
