How Large Organizations Manage AI Projects

Artificial Intelligence (AI) has emerged as a key driver of innovation across industries. When we think of AI, many assume that data scientists alone handle the entire process, especially in smaller organizations. However, in larger organizations, AI-related projects involve various specialized roles, all working in a coordinated fashion. This article walks through how these teams work together, ensuring successful AI implementation in large enterprises.

The Key Players in an AI Project

AI projects in large organizations follow a structured approach, with each person contributing their expertise to ensure a robust solution is delivered.

1. Project Sponsor:

Every AI initiative begins with a business need or problem, identified by the project sponsor. This individual typically comes from a senior management or executive level and has the authority to fund the project. They play a critical role in aligning the project’s goals with the organization’s business objectives.

2. Project Manager:

Once a project is greenlit, the project manager steps in to oversee the entire process, from initiation to completion. Their primary role is to ensure that the project stays on track, both in terms of timeline and budget. They collaborate closely with all stakeholders, ensuring smooth coordination between teams.

3. Scrum Master:

Unlike traditional software projects, AI projects focus on extracting insights from data rather than building applications. This is where agile methodology, led by a Scrum Master, becomes important. The Scrum Master ensures the team follows agile practices and breaks the project down into manageable sprints. Their focus is on maximizing team efficiency and removing roadblocks to progress.

The Core Team in an AI Project

An AI project’s core team includes specialized roles that handle different aspects of the data lifecycle, from data extraction to model building.

1. Data Engineers:

Data engineers are responsible for sourcing, cleaning, and preparing the data required by the data scientists. Often, data within a company is spread across different systems and may exist in various formats, both structured and unstructured. Data engineers ensure that the right data is accessible and in the correct format for analysis.

2. Business Intelligence (BI) Experts:

BI experts work closely with data engineers to help visualize the data and generate initial insights. They often create dashboards and reports, providing a visual representation of the data that can be easily understood by stakeholders.

3. Data Scientists:

Data scientists bring domain expertise and technical skills to the table. Their role is to build predictive models using the data prepared by the engineers. By leveraging machine learning algorithms and statistical techniques, they generate insights and forecasts that help solve the business problem.

Collaboration and Data Flow

The collaboration between these roles is crucial for the success of an AI project. Here’s how it typically works:

Step 1: Data Identification

Data engineers and BI experts identify the necessary data sources, transforming and preparing the data for analysis. This involves cleaning up data inconsistencies and ensuring the data is structured in a way that meets the data scientist’s specifications.

Step 2: Model Development

Once the data is prepared, the data scientist steps in. Using their expertise in AI, machine learning, and statistical modeling, they create a predictive model. This model is rigorously tested and refined to ensure that it delivers accurate predictions.

Step 3: Visualization and Reporting

After the data scientist has built the model, the BI experts take the predictions and integrate them into user-friendly dashboards or reports. These visualizations make it easier for the business users to understand the findings and take actionable steps.

Step 4: Agile Process

The entire process follows an agile framework, overseen by the Scrum Master. Each step of the project is divided into sprints, with regular reviews and adjustments made as the project progresses.

Once the project is complete, the work doesn’t end there. The AI models need continuous monitoring to ensure they perform as expected. Business users monitor the results and may provide feedback, which can lead to further fine-tuning of the model.

Conclusion

AI projects in larger organizations require a highly coordinated team effort, with every role contributing to different stages of the project. Data scientists may be at the heart of AI projects, but without data engineers, BI experts, project managers, and sponsors, these projects would not come to fruition. Larger organizations ensure that AI initiatives are well-structured, leveraging each expert’s skills to deliver meaningful results.