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The world is being transformed by artificial intelligence (AI). Many businesses and industries are implementing this technology to improve predictability, streamline processes, and stay ahead of the competition. But introducing AI software and practices isn’t always easy.

In this course, you will learn how to successfully integrate AI into any organization. You’ll learn how to determine whether your organization could fully benefit from AI, as well as the technical capabilities necessary for deployment. You’ll learn how to perform business analysis and present a case for implementation.

Further, you’ll learn project management best practices to help install AI successfully. You’ll use the Scrum framework to build and deploy AI and to maintain the system when in production to collect data and model scenarios.

AI Project Management: Deploying and Maintaining AI for Business

The goal here is to confirm the business case for starting an AI project. Let’s reconsider the example of an e-commerce site. If this is an organization’s primary business, the project proposal could include a recommendation engine that considers the current shopper’s previous purchases. That way, the AI can assess what else customers are likely to buy based on their profiles or  suggest complementary products based on what’s currently in their shopping cart.


# The role of product owners

New project proposals come from product owners. The product owner is familiar with the business domain and enjoys the respect and trust of the business’ stakeholders. Therefore, the product owner makes decisions and sets priorities.

The product owner does not need to be a technical expert, but they must understand which technologies would benefit the company. They need to know how competitors and the industry are employing AI.


# Business purpose of an AI project

An AI project’s business purpose usually falls into one of three categories. These categories are listed below:

- Increase revenue

- Decrease cost

- Reduce risk

## Increase revenue

Making more sales and attracting more online customers is the key to increasing revenue.

## Decrease cost

AI can reduce costs in many ways, including improving customer retention and satisfaction or lowering company overhead by automating certain tasks. One example of a cost-cutting measure includes using automated marketing testing to lower advertising budgets.


## Reduce risk

Risk reduction concerns compliance with health and safety regulations. Compliance does not necessarily directly lead to higher profits. That’s why it’s sometimes easier to market AI projects that have a good probability of improving revenue growth to management than risk-reduction projects. However, every business must comply with the regulatory health and safety standards, making risk reduction an important and urgent application of AI.

# Collaboration between the product owner and data scientists

**Data scientists** are responsible for estimating both the time required to deliver the AI project and how long the project will take. They also determine what resources the product owner needs. In short, the data scientists are able to take the existing business data, pre-processes it to be fed into an AI model, and then construct the AI model. 

Data scientists may find it challenging to market new AI projects because they can be costly to implement. To construct the business use case, the product owner and data scientists have to collaborate closely before presenting the case to the management team. 

Before the presentation, it is also important for them to go through an assessment of the AI to make sure it’s small enough to introduce to the business in a short amount of time. Much of the collaboration between the data scientists and the product owner is trying to determine what a minimum viable product would entail and accomplish.



Let’s learn about what role product owners and data scientists play in AI adoption. We’ll also find out the best reasons to start an AI project.

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Developing the AI Business Case: Part 1