5 Tips for Organizing Artificial Intelligence Information

All computing technology your business utilizes should be organized within an easily navigable infrastructure so employees can find information and tools they need quickly. Artificial intelligence is no different. Here are five tips for organizing AI information.

1. Implement Model Governance

Implementing model governance will help you reach high returns on your investment in AI technology. This is a process by which you can organize and monitor your infrastructure. You can track activity in the workflows, control access and implement updates or patches. Model governance helps ensure the infrastructure is solid and there are guidelines and standards developers and other employees must follow to keep the infrastructure clean. When you have a well-organized infrastructure and the ability to track access and changes, you'll have increased visibility and the capabilities to detect and fix issues before they become large problems.

2. Choose a Structure for AI Organization

There is no one structure you must use for organizing your AI infrastructure. You should research different standard structural models, what other organizations have to say about them and any custom organizational models organizations in your industry have developed. These pre-existing models can help you figure out what you need and what infrastructural concepts will be beneficial to your needs. Three common AI organizational structures are Fully Embedded, Star and Matrix models. The Fully Embedded model is best-suited for organizations with mature AI infrastructures. Every department has its own AI development team and these teams are able to easily communicate cross-functionally. The Star structure is a good choice for small businesses and those organizations who are new AI users. This model has one central AI team that supports the rest of the organization. The Matrix structure sits between the former two models. It's typically utilized by medium to large organizations with some AI experience. There are multiple AI teams, but they're focused on specific projects or problems rather than supporting one department each.

3. Know When To Be Flexible

AI can help your organization greatly increase its flexibility and agility. That flexibility can be a boon to your organization when you're rapidly scaling up your business and innovating your tools and technology. However, it can also be difficult for employees to adjust to. You need to make sure people know when to be flexible and when to stick closely to standards and guidelines. You should also be prepared for barriers to flexibility and change. For example, teach people about AI implementation and organization and make sure they understand that AI is there to assist them and your organization's customers, not to make them obsolete.

4. Organize for Scale

If your organization's goal is to grow over time, you need to make sure your entire business model is scalable, from your goals to the tools your employees use. This scalability extends to your organizational models for computing technologies too, including AI and cloud computing capabilities. To organize your AI technology for optimal scalability, try to avoid gray areas for responsibility. Consider the complexity of your business model, your AI's capabilities and assets, the level of innovation you want to achieve with your technology and the pace at which you want to do so.

5. Plan How To Make the Change

Once you know what you need from your AI organizational structure and have explained the necessity to your employees, you'll need to plan out how to transition from your existing model to an AI-focused model or to alter your infrastructure to include AI organization. You should avoid using the plug-and-play model and instead invest well in plans and tools to incorporate AI and its related infrastructure smoothly.

While AI can automate many aspects of your workflows, implementing it within a disorganized or crowded infrastructure will make it difficult for employees to interact with the program and for developers to access it. You need to make sure your AI infrastructure makes sense for your needs and is well-organized.