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Why does Every Organization Needs Artificial Intelligence?

As humans learn more about Artificial Intelligence (AI) and develop what it can do, more and more organizations are implementing AI into their processes

Read time: 6 minutes

As humans learn more about Artificial Intelligence (AI) and develop what it can do, more and more organizations are implementing AI into their processes. The increase into the development of AI has helped it become affordable to use in most, if not all, organizations.Companies are learning that now is the perfect time to implement AI into their repetitive processes to create a level of automation that increases productivity and allows the individuals within organizations to focus on more detailed work that cannot be done by AI. But before we discuss how to implement AI into your organization, we should first look at the steps a company should take before beginning with AI.

Steps an Organization Should Take Before Implementing AI

  1. The first step any organization should take before implementing AI into their processes is to get familiar with Artificial Intelligence. Learning more about AI will help in later steps of the implementation, and help you determine the places in your organization that will benefit the most from an AI implementation.There are many free resources to learn about AI, including YouTube videos, university lectures, and open-source libraries and kits that will help you develop a better understanding of what goes into implementing an AI solution.
  2. Once you’ve familiarized yourself with the basics of AI, you should identify the problems you want Artificial Intelligence to solve and how much that may cost.AI can be implemented in an organization’s existing products and services, or it can be something as simple as a chatbot on the main website. If your organization is looking for something simple, a chatbot would be a good first step. They are very easy to set up, and they take care of users asking many of the same, repetitive, questions. This will free up your support teams to focus on other projects and only be needed if the chatbot does not already have an answer.
  3. After identifying current processes that would benefit from AI, or discovering gaps that AI would fill, it is time to start designing your solution. It is always best, for the first few AI projects, to utilize external and internal teams to complete these solutions. In this way, you have both experts who already know how to implement an AI solution, and internal team members who can learn for future AI projects.Starting small with your first project is important, as you don’t want to take on too much at once with an AI implementation. A simple month-long project could turn into a six-month project if careful planning is not done. Now that you know some of the steps an organization should take before implementing Artificial Intelligence, let us discuss the different models of AI implementation that an organization may take.

AI Implementation Models

There are three different implementation methods that an organization will take when deciding to implement AI into their organization.

  1. The first model is the “hub” model. The “hub” model, as the name suggests, focuses all AI and analytics systems into a central hub. A central hub for Artificial Intelligence is perfect when deploying new AI systems, as it provides a fully centralized team to handle every step of the implementation. A “hub” model should be gradually developed over time, as the task of developing such a large unit of the business would be very complicated and time consuming all at once.The way the “hub” model is set up is that the systems and teams involved in AI are in a centralized location, loaning out their experience to the different business units whenever necessary. The development of the hub should be driven by the different AI tasks that the organization has determined are needed within the company. This allows the hub to grow slowly over time, as opposed to all at once.
  2. Another implementation model of AI within an organization is the “spoke” model. This model is the opposite of the “hub” model, instead focusing on spreading the different AI team members and systems throughout the different business units of the company.This model offers the different business units the ability to have a support team on deck for any AI tools they have implemented into their section of the business. This also allows the different business units to develop their own AI tools and systems for their specific use, as opposed to deploying them organization-wide.
  3. The final model is a “hybrid” model, called a “hub-and-spoke” model. This takes the components of a “hub” model and the components of a “spoke” model and creates the ideal model. This method allows the central hub to handle a small handful of responsibilities with the AI team lead at the center.The spokes then work within the different departments to create business unit-specific tools that can help the business unit. The spokes focus on execution team oversight, adopting AI solutions, and performance tracking, while the hub deals with hiring for AI team members, performance management, and AI governance.

Ways of Using AI within your Organization

There are several different ways to use Artificial Intelligence within your organization that are simple to implement and don’t involve high costs. A centralized knowledge center is a great initial way to start using AI in your organization. Having a central knowledge base offers users the ability to quickly find and parse through documents relating to their questions, without having to use the time of an employee to answer the same questions over and over.

Like the centralized knowledge center, you can also setup an automated live chat, like a chatbot, that will answer questions for users. Additionally, you can integrate with popular applications, like Salesforce or Jira, and automate processes within those applications. This allows employees to save time and increase their productivity.

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About the Author

Riley Dickens is a Consultant at Encryption Consulting, working with PKIs, creating Google Cloud applications, and working as a consultant with high-profile clients.

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