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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 diagnostic and predictive AI stage

The best thing to do when our assessment indicates that we are in the descriptive AI phase is to shift our focus to **diagnostic AI**. The following are the steps required to transition into the second phase of the AI journey:


- Start a project with our platform or IT team to make our data more accessible.

- Adopt more advanced data processing procedures, even if these procedures don’t completely eliminate silos yet.

- Move from siloed data toward data connectivity between diverse stakeholders.


## Understanding the three V’s of the diagnostic phase

How do we make the transition from descriptive AI to **diagnostic AI**? We start by considering the three Vs—volume, variety, and velocity.

- **Volume** refers to the available data that we want to understand.

- **Variation** comes from the structure of that data. 
 
- **Velocity** refers to the frequency or speed at which this data is expected to influence company outcomes.


## Understanding company rules

It is vital to identify company rules and to ensure that compliance standards are in place. This is where the benefits of dashboards come in. **Dashboarding** allows us to visualize data outside of Excel and PowerPoint. Our data may still not be centralized, but we’re working towards that. 

How do we continue to move from descriptive AI to diagnostic AI? In some business divisions, we aim to have reusable code. Previously, there weren’t many data scientists or machine-learning engineers. But, businesses are increasingly investing in those positions and expect them to engage closely with business stakeholders.


## Adjusting to the second stage

Some seasoned leaders may find this adjustment challenging. Still, organizations are beginning to shift from relying on the talent and expertise of leaders toward a more data-driven culture. This doesn’t mean that seasoned leaders are no longer relevant. However, we’re increasingly relying on data to make decisions.


Let’s learn about the second and third phases of AI.

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AI Stage Evaluation: Part 2 and 3

The diagnostic and predictive AI stage

Understanding the three V’s of the diagnostic phase