According to Gartner, only 54% of AI projects make it from pilot to production. Indeed, many AI projects fail but the technology is not necessarily to blame. Instead of looking for the problem in complex algorithms and large datasets, project leaders must focus on how they manage such projects The success of AI initiatives largely depends on effective planning and strong leadership.
Data-driven projects are not like others, so the approach to their management is fundamentally different. What most teams do wrong is go blind into AI projects treating them as traditional software development projects, which leads to wasted time and resources.
To avoid a mistake that might cost you dearly, let’s find out what differentiates AI projects and how you can manage them better.
To plan and manage AI projects with maximum efficiency, you need to understand what sets them apart from traditional software development projects. Here are the core differences.
Let’s start with the development lifecycle. The traditional software development lifecycle (SDLC) typically involves planning, analysis, design, development, testing, deployment, and maintenance, supported by well-defined requirements. In other words, it’s predictable.
AI projects also have specific stages with clear and measurable objectives. In general, these stages are planning, data acquisition and exploration, modeling, and deployment. However, data-driven projects are highly iterative as the AI model may be trained dozens of times to achieve the required accuracy and precision. What’s more, AI projects are more about experimenting. Iterations involve experimenting with different algorithms and parameters to improve model performance. If any changes are made throughout the project, more often it’s because the experimental results require so, not because it was an initial requirement.
One more difference lies in the post-release activities. The final phase of traditional SDLC is maintenance and support, which includes addressing any issues and releasing new software updates. But once an AI project is deployed, it’s not just bug fixing. It’s crucial to continuously monitor the performance, retrain and fine-tune models with fresh data, as well as audit the system for ethical issues and security flaws.
As you see, traditional and data-driven projects are different in nature, so you need a flexible approach to effectively handle all AI project stages and minimize risks where possible.
When traditional project management is mostly about timelines and resource allocation, AI revolves around data and heavily relies on it. However, in many organizations, especially those started long before the Internet era, data is located in disconnected systems and databases, making it difficult to access and integrate.
To address these challenges, PMs should work closely with data specialists to understand the nuances of the collected data and how it would impact model performance. Prioritizing tasks such as data acquisition, preprocessing, and validation ensures that the data used for training AI models is accurate, representative, and bias-free.
Additionally, you should establish robust data governance processes to maintain data security, integrity, and compliance with regulatory requirements. This may include defining data ownership, access controls, and data lineage to track the data flow throughout the project lifecycle.
While data might also be important in a general software development project, roles related to data management may not be as specialized, with a greater emphasis on software engineering and development tasks. When it comes to an AI project, you need highly specialized talent on board, including data scientists, data engineers, data analysts, data architects, and other experts. A professional team of data specialists is the backbone of an AI project that helps you navigate the complexities of the data-driven development process.
When assembling a team for an AI project, PMs should also ensure the team follows the best practices of responsible AI considering the ethical and legal implications, and maintains accountability and privacy of data.
In traditional project management, flexibility means you can adjust project plans and timelines in response to unforeseen circumstances. As for AI projects, flexibility isn’t just about dealing with requirements changes. Given the complexity and unpredictability of AI projects, PMs should adopt a flexible project management approach and be ready to make changes on the go, accommodating to evolving circumstances throughout the project lifecycle.
For a better understanding, let’s imagine a company developing a voice assistant. During user testing, they discover that certain voice commands are consistently misinterpreted by the AI. To address this issue, a project leader decides to prioritize improvements to the natural language processing algorithm, even if it means deviating from the original project scope and timeline. Following iterative development cycles allows the project team to move forward or backward to fine-tune the algorithm and continuously improve the solution.
So overall, AI projects are more flexible and data-centric compared to traditional ones. Now, let’s discuss what project management techniques you can apply to the specific needs of data-driven projects.
When it comes to traditional project management methods, it’s all about handling the basics like project scope, deadlines, and budgets. They’re good for the projects we deal with on a daily basis, the ones that don’t focus on artificial intelligence. But when it comes to AI projects, the PM triangle is not enough. Project managers should tweak their management strategies to efficiently address AI development challenges.
Here is how you can succeed in leading a data-driven AI project.
As an AI project manager, you don’t have to be an expert in data pipelines or ML algorithms. But to efficiently plan and navigate the project, you should know the basics of AI and clearly understand the key stages of AI project development, including data collection, preprocessing, model training, evaluation, and more.
Because data is the heart of AI and its most valuable asset, you should ensure its usage aligns with legal and ethical requirements while respecting privacy and intellectual property rights. For example, it’s essential to study the data sources for bias before using particular data for model training. Unreliable data will make the AI model itself biased, and, therefore, lead to inaccurate answers.
When measuring an AI project, it’s not enough to consider technical metrics only. Accuracy, reliability, and precision won’t show how much business value can be delivered. Instead, you should focus on business metrics like revenue growth, customer satisfaction, and cost reduction.
Agile is undoubtedly one of the most widely used approaches to project management, but it fails to handle the challenges of data-driven projects. Therefore it might make sense to adopt a more novel approach that combines Agile principles and data-centric guidelines. One of such is called Cognitive Project Management for AI methodology (CPMAI). It is designed to address the unique challenges of complex AI projects, where data and performance requirements are continuously changing.
With data in its core, CPMAI is iterative and incremental with each iteration having six key phases, namely Business Understanding, Data Understanding, Data Preparation, Model Development, Model Evaluation, and Model Operationalization.
Coordinating a diverse team combining AI developers, data scientists, domain experts, and data engineers can be challenging. To ensure everyone is aligned and understands the technical nuances of their specific task, you as a PM should clearly understand the roles and responsibilities of each member, set up regular communication channels, and encourage collaboration between cross-functional experts.
Plan for scalability from the beginning. Make sure you build flexible architectures that can adapt to increasing data volumes and user loads. Begin by developing the simplest model required to solve a specific problem and then scale it up gradually as needed. We also recommend starting with a small-scale pilot project when implementing generative AI. This way, you can iterate faster, experiment more, and validate your AI models before dealing with larger datasets.
Read also: How to integrate AI
Techniques for managing AI projects
Without exaggeration, AI projects are full of challenges and risks. But if you understand the intricacies of such projects and effectively employ data-centric project management techniques, you can bring your AI project to success. This is where having a team with the right expertise becomes crucial.At Modeso, we work with clients in various domains helping them integrate AI into their products. When developing data-driven solutions, we adapt our project management strategy to fit the specific requirements of such projects. If you plan to adopt AI, contact us to discuss the details.
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