Why AI projects fail

Misdiagnosing problems, forcing solutions, harbouring impossible expectations: The AI transformation graveyard is littered with ambitious projects doomed by leadership’s fundamental misunderstandings. While executives chase the promise of AI, they blindly deploy technologies without strategic clarity, turning potentially revolutionary tools into expensive corporate ornaments that solve nothing.

This article draws insights from a RAND report based on interviews with experienced AI practitioners. It provides guidance on developing a robust AI strategy and avoiding common pitfalls. 

Note that this analysis only concerns traditional AI projects and does not apply to Gen AI.

Understanding the Stakes: The High Failure Rate of AI Projects

While AI adoption is rapidly increasing, a significant number of projects fail to deliver on their promises. Estimates suggest that over 80% of traditional AI projects fail, a rate twice as high as that of traditional IT projects.

This highlights the need to understand the factors contributing to this high failure rate and implement strategies to mitigate these risks.

Why do AI projects fail: Organisational Factors Take Centre Stage

The RAND report identifies five key root causes of failure. Many of which stem from organisational challenges rather than purely technical limitations:

1. Leadership-Driven Failures

Misaligned Objectives: 84% of interviewees cited leadership failures as the primary cause of project failure. This often stems from a lack of clarity about the intended business problem and the metrics for success. For example, a business leader might request an algorithm to predict product pricing when optimising profit margins is needed. This disconnect between business needs and technical execution leads to the development of solutions that fail to address the core issue.

Unrealistic Expectations: AI hype can inflate expectations about its capabilities and timelines. Business leaders may expect quick wins and fail to appreciate the time-intensive nature of data acquisition, cleaning, and model training.

Lack of Sustained Commitment: Frequent shifts in priorities can result in AI projects being abandoned prematurely, preventing them from delivering tangible results. Leaders must commit to long-term project timelines, ideally at least one year, for meaningful outcomes.

2. Data-Driven Failures

Data Quality and Availability: What is the role and value of data for AI? Effective AI models require large volumes of high-quality data. However, many organisations struggle with data quality issues. This is often due to inadequate investment in data infrastructure and a shortage of skilled data engineers. Legacy data collected for compliance or logging purposes might lack the necessary context or granularity for AI training.

Data Imbalance: Imbalanced datasets can lead to biased models. In healthcare, for example, a dataset might have a disproportionately large number of negative test results. This leads to a model that struggles to identify rare conditions accurately.

Traditional vs. Generative AI Requirements: While traditional AI models require extensive training data, generative AI models come pre-trained on vast datasets. Organisations implementing traditional AI face significant data volume requirements, whereas those adopting generative AI primarily need to ensure data quality and accessibility for effective prompting and integration. However, specific use cases requiring fine-tuning of generative AI models still demand carefully curated training datasets, though typically smaller in volume than traditional AI approaches. No matter the solutions you need: data governance is key for your AI transformation.

3. Underinvestment in Infrastructure

Building and deploying AI models requires robust infrastructure for data management, model training, and deployment. Underinvestment in this area leads to longer development cycles, higher failure rates, and a reduced ability to leverage data assets effectively. Read this article to help you choose a cloud deployment model for AI. It highlights several factors warrant careful consideration.

To address this question, Devoteam developed an AI architecture framework with the purpose of :

• Accelerating the design phase using common use cases
• Accelerating the implementation phase through reuse
• Proven frameworks and solutions

This framework is built upon core design principles, including modularity, scalability, flexibility and reusability.

💡 See how Devoteam built a state of the art AWS infrastructure for Gen AI

4. Focus on Technology Over Problem-Solving

Technical teams can sometimes be drawn to the latest AI trends, leading to the implementation of complex solutions where simpler ones would suffice. A focus on solving the business problem, rather than chasing technological novelty, is crucial for successful AI implementation. Discover why a problem-focused approach, not AI-first thinking, is key to digital transformation.

5. Immature Technology

While AI is rapidly advancing, specific problems remain beyond its current capabilities. Attempting to apply AI to solve such problems will inevitably fail. Leaders need to understand AI’s limitations and select projects that are a good fit for the technology’s current state of development. Tools like TechRadar by Devoteam help you estimate the maturity level of ermerging technologies.

6. Additional Considerations

Talent Acquisition and Retention: Putting people first is key in any (AI) transformation. Finding and retaining skilled AI professionals, particularly data and ML engineers, is crucial for success. Investing in training and development for existing staff can also be beneficial.

Agile Adaptation: Traditional software development processes may not be suitable for the iterative nature of AI projects. Flexibility and close collaboration between technical and business teams are essential.

Recommendations for Successful AI Implementation

To overcome these challenges and increase the likelihood of AI project success, CEOs, CTOs and CIOs should consider the following recommendations:

By addressing these organisational and technical considerations, CEOs and CTOs can create a more conducive environment for AI project success, unlocking AI’s transformative potential to drive innovation and achieve strategic objectives.

Understanding why AI projects fail is crucial for organisations seeking to develop AI strategy. Factors like unclear objectives, poor data quality, lack of skills, inadequate infrastructure, and unrealistic expectations contribute to these failures. Essentially, many organisations struggle to align AI initiatives with business needs, resulting in projects that don’t solve real problems or deliver tangible value. Many organisations are looking for a framework to measure the ROI of AI projects. This emphasises the importance of careful planning, realistic goal-setting, and a strong understanding of AI’s potential and limitations.