5 Reasons Why AI Projects Fail (And How to Avoid Them)

From healthcare and finance to manufacturing and retail, AI projects hold the promise of enhanced efficiency, innovative problem-solving, and new business opportunities. The potential of AI to revolutionize operations, drive insights from data, and automate processes is indisputable. However, despite its enormous potential, many AI initiatives struggle to translate visions into reality.

The allure of deploying AI to modernize and outperform the competition has led countless organizations to embark on AI projects. Yet, along this journey, significant challenges often arise—challenges that can derail even the most promising of endeavors. Some companies might find themselves wrestling with technological complexities, while others may encounter resistance from within the organization due to inadequate change management or overlooked ethical concerns.

This blog post explores the top reasons why AI projects fail and offers actionable insights on how to manage AI projects. Use them to ensure successful, sustainable AI endeavors.

Understanding the Landscape of AI Implementation

AI-based projects are reshaping both personal and corporate environments. The field creates new possibilities and streamlines standard practices. At home, AI powers virtual assistants and smart devices. At work, AI allows for smart automation, predictive analytics and improved service.

Sixty-four percent of business owners are optimistic about AI’s impact on customer relationships and productivity.¹ They’re excited about trends that contribute to successful AI adoption, such as integrating AI with Internet of Things devices and the rise of edge computing (i.e., distributed computer frameworks). These advancements enable more efficient data processing and real-time decision-making.

The Alarming Reality: How Many AI Projects Fail?

Despite the benefits of machine learning, it’s estimated that over 80% of AI information technology projects fail.² That AI-project failure rate is twice the rate of IT initiatives that do employ AI.³ This may be because the projects that don’t use AI tend to have clearer objectives and success metrics.

As for generative AI, consulting firm Gartner predicts that at least 30% of generative AI projects will be abandoned post-proof of concept by the end of 2025.⁴ Common reasons for AI failures include poor data quality and ambiguous business value.

Why AI Projects Fail More Often Than They Succeed

AI projects face numerous challenges from conception to execution. A 2024 RAND report, “The Root Causes of Failure for Artificial Intelligence Projects and How They Can Succeed,” identified five common issues that plague people learning how to manage AI projects. Being aware of these pitfalls can help you be more judicious with when and where you apply AI technology.

1. Misunderstanding or Miscommunication of the Problem

The RAND report highlights that one of the most common root causes of AI project failure is the failure of business leaders to communicate the right problem to be solved with AI.³ Leaders may instruct data teams to solve incorrect problems or optimize wrong metrics due to miscommunication. These failures often arise from a breakdown between business leaders and technical teams, and highlight the necessity for translating business objectives into achievable technical goals.

2. Lack of Necessary Data for AI Training

According to the RAND report, many AI projects fail because organizations lack the appropriate data quality or quantity to train models effectively. Challenges such as unbalanced datasets, lack of context, poor historical data quality and data collected for other purposes hinder the training of effective AI models.³ Additionally, data engineering is often undervalued yet critical for preparing datasets suitable for AI projects.

AI is only as good as the data it receives. Inadequate or faulty data can lead to biased outcomes and incorrect predictions. Check your inputs. If you’re building from datasets, invest in high-quality, diverse data to avoid bias and other limitations.

Both data poisoning and consumer reluctance to trust data compromise AI projects.⁵ Over 75% of consumers worry about the misinformation they’ve seen in generative AI failure examples.⁶ You’ll lose the efficiency you hope to gain if people need (or feel the need) to recheck AI outputs.

3. Focus on Technology Over Real Problems

The report indicates that AI projects often fail due to an organizational focus on using cutting-edge technology instead of addressing real user problems.³ Engineers may be incentivized to experiment with new tools instead of adequately solving the problem at hand, often due to a mismatch between project objectives and actual technological needs.

4. Inadequate Infrastructure for Data Management and Model Deployment

The RAND report notes failures due to insufficient infrastructure for data management and AI model deployment, which can lead to project delays or inability to deploy models.³ AI solutions must seamlessly integrate with existing technologies to function effectively. Failure to ensure compatibility can lead to operational bottlenecks and increased complexity, undermining the potential benefits of the AI system.

Investing in infrastructure helps manage data quality, streamline deployment processes, and promote robust AI operations, ultimately reducing the likelihood of project failure. Assess your current infrastructure’s readiness and plan for potential challenges to a smooth deployment.

5. AI Is Applied to Problems Too Difficult to Solve

Finally, AI projects often fail when applied to problems that are currently beyond technological reach or complexity. AI has limitations and is not suitable for solving every type of problem, especially those involving complex human judgment or nuance that AI cannot easily replicate.³

Additional Reasons for AI Project Failures

In addition to the five reasons for failure noted in the RAND report, AI projects might also fail due to:

Underestimating Resource Requirements

It’s easy to underestimate the expense of AI solutions. Many AI projects fail due to ballooning costs. The price tag can increase as businesses try to scale projects and run into hidden costs associated with cloud storage and API usage.⁷ You need to allocate enough resources for ongoing support and map out the costs associated with projected use. Beyond financial investment, companies frequently underestimate the substantial time, skilled personnel and data needed to train and maintain AI systems. A lack of proper resources can lead to incomplete projects and unmet expectations.

Failure to Address Ethical Concerns

Ignoring the ethical dimensions of AI—such as data privacy, bias and accountability—can lead to public backlash and regulatory challenges. Ethical considerations must be at the forefront of AI project planning. As AI systems gain autonomy, companies must establish clear guidelines and conduct regular audits.

Be transparent about your organization’s use of both consumer data and AI tools. A study conducted by KPMG found 63% of consumers worry over the potential for generative AI to expose personal data to breaches or misuse.⁸ Stock image supplier Getty Images reports that 90% of their consumers want to know when an image has been generated with AI software.⁹

Stay compliant with industry standards and outline your privacy policy in clear terms that cover how you intend to collect and use consumer data. Then, conduct regular audits of your tools.

How to Manage AI Usage and Projects Effectively

Effectively managing AI projects requires strategic planning and practical execution. Students in the Milwaukee School of Engineering’s online Master of Science in Machine Learning program learn how to address and overcome all of the five common reasons that AI projects fail.

To begin improving the outcomes of your AI projects today, consider the following:

Align AI Projects with Clear Business Goals

Each project should align with your overarching business objectives to create a focused direction for your team and maximize the project’s impact. Begin with a clear understanding of how AI can support specific business objectives. Then, establish measurable success criteria that align with strategic goals to ensure the project remains focused and relevant.

Engage key stakeholders throughout the project to gain insights and maintain alignment with business needs, facilitating a cohesive approach to AI development.

Plan, but remember AI is a rapidly evolving field. Stay adaptable.

Allocate Resources Proactively

Make sure you have skilled personnel and a robust data infrastructure in place. Assemble a cross-functional team with expertise in data science, machine learning, and domain-specific knowledge. Continuous training and development should be a priority to keep up with AI advancements.

Evaluate and secure the appropriate technological infrastructure, including data storage, computing power, and software tools, to support AI capabilities effectively.

Proper preparation in this area guards against potential disruptions and keeps the project moving smoothly.

Keep Ethical Considerations in Mind

Keep ethical considerations in mind at every stage. Develop a clear ethical framework that addresses issues like bias, fairness, and transparency to guide AI development and deployment.

Regularly review AI applications for compliance with regulatory standards and data privacy laws to maintain legal standing and public trust. Regular audits help with transparency, which in turn helps maintain trust. Companies should strive to build credibility with consumers and contribute to the responsible development of AI technologies.

Facilitate Seamless Integration with Existing Systems

To maximize the efficiency that should be gained by using AI, regularly evaluate the compatibility of AI solutions with legacy systems to identify integration issues early and adapt accordingly. As you implement AI systems, consider a phased rollout of AI projects, starting with small, manageable pilots. This approach allows for testing, feedback, and adjustments before full-scale implementation.

Master AI Project Management Through MSOE’s Online Machine Learning Programs

By understanding and addressing the challenges of AI projects head-on, organizations can harness the full power of AI to realize their strategic ambitions, steering clear of failures and toward successful, impactful AI implementations. Better prepare for a successful career in AI and machine learning with the Milwaukee School of Engineering. MSOE’s online machine learning programs prepare you to excel in AI and machine learning projects. Our online programs focus on imparting practical skills and essential leadership capabilities for AI project management.

Our comprehensive courses professionals with the skills to harness AI and machine learning’s full potential. Choose from the online Applied Machine Learning Graduate Certificate or the online Master of Science in Machine Learning.

For more information on admissions and how MSOE can help you master AI project management, contact our admissions page. Or, if you’re ready, take the first step toward becoming a leader in AI technology by getting started on your application.