How to effectively manage an AI project

Artificial Intelligence (AI) is changing how organisations operate, from automating workflows to unlocking new insights through data analysis. But while much of the conversation focuses on how AI can enhance project management, less attention is given to the complexities of managing AI as a project output.

Implementing AI across a business is not a typical IT upgrade or process improvement. It’s a multidisciplinary initiative that often involves new technologies, uncertain outcomes, and significant cultural change. In this blog, we explore what it takes to successfully deliver an AI implementation project and how a structured methodology like PRINCE2® can provide the governance and control needed to manage its risks.

Understanding the nature of AI projects

AI projects are unique in several ways. Unlike traditional software development, AI implementation often involves experimentation. Outcomes are not always predictable, and solutions may need to evolve over time based on how the AI performs with real data.

There are also multiple layers to consider. You may be deploying a machine learning model, integrating it with existing systems, aligning it with regulatory requirements, and managing workforce concerns about automation or ethical implications. This complexity means that AI projects are rarely confined to one department — they often involve stakeholders from IT, data science, operations, HR, and compliance.

Planning and initiating an AI project

Before diving into development, it’s important to clarify what your AI project is actually aiming to deliver. Will the organisation be building a custom AI system from the ground up, or configuring an existing platform like ChatGPT for internal use? Each option comes with different levels of complexity, cost, and control. Equally important is defining the function the AI should serve. Is it intended to automate customer service, support decision-making, enhance internal productivity, or something else entirely?

With so many potential use cases across departments, narrowing the focus, defining clear objectives and aligning the project with real organisational needs will help ensure it delivers meaningful value, rather than being a knee-jerk reaction to AI fever.

In a PRINCE2 environment, this begins with a well-defined business case and project brief. These documents should outline the intended benefits of the AI solution, the problem it is solving, and how success will be measured.

Clear governance is also essential. Appointing roles such as the project executive (to maintain business justification), project manager (to oversee delivery), and senior users (to represent those affected) ensures accountability across technical and operational domains.

Managing delivery through iterative learning

Unlike many traditional projects, AI implementations often require an iterative approach. Models need to be trained, tested, and refined based on performance. In this context, stage boundaries can be used to reassess progress, review lessons learned, and decide whether to proceed, pivot or halt the project.

It’s important to build in space for trial and error, especially during data preparation and model training phases. Not every AI solution works straight away. Using tolerances and exception reporting helps manage expectations and avoid unnecessary pressure on teams to deliver perfection too early.

Key risks in AI project management

AI projects come with a specific set of risks that must be actively managed. These include:

• Data quality risks: AI relies heavily on high-quality, unbiased data. Poor data can lead to flawed outcomes or reputational damage
• Integration risks: Ensuring that the AI solution can work within existing systems and workflows without disruption
• Change resistance: Employees may be sceptical or concerned about how AI will affect their roles. Engagement and communication are essential
• Ethical and compliance risks: AI models must be explainable and aligned with legal and ethical standards, especially in regulated industries.

PRINCE2’s risk management approach is well suited to AI projects. Using a risk register, incorporating regular reviews, and escalating significant concerns to the project board ensures that potential issues are addressed proactively and transparently.

Aligning stakeholders and expectations

AI can generate excitement, but also confusion. Not all stakeholders will fully understand how it works or what to expect. Clear communication is therefore a critical success factor.

PRINCE2’s emphasis on stakeholder engagement and quality planning helps here. Defining quality criteria for outputs - such as accuracy thresholds or explainability standards - helps avoid misalignment and provides a shared reference point for evaluation.

It’s also important to prepare the business for change. AI may alter roles, processes, or decision-making structures. Including change management activities in your project plan supports smoother adoption and helps secure long-term benefits.

Delivering lasting value from AI initiatives

AI projects are not just about deployment; they’re about integration and continuous improvement. This means ensuring that the solution continues to deliver value after going live, and that teams are equipped to manage, monitor, and enhance the AI system as needed.

A focus on benefits management and lessons learned supports this long-term view. Post-project reviews provide an opportunity to assess outcomes, gather feedback, and inform future phases or projects.

Structuring AI projects for success

Managing an AI project requires a balance of technical understanding, structured delivery, and strong stakeholder engagement. By applying PRINCE2 principles and processes project managers can provide the structure needed to navigate uncertainty and deliver real, measurable value.

Looking to lead an AI implementation project with confidence? Explore our PRINCE2® training options to strengthen your project governance skills and support the delivery of complex, innovation-driven initiatives.