AI Failure & Keys to Success

Key Takeaways

1. A sharp rise in AI solution failures in high-stakes applications means massive emerging risk for enterprises and society.
2. Common AI failure modes include flawed algorithms and models, biased or missing training data, edge cases, perception and sensor errors, and security.
3. Early involvement of experts, rigorous data audits, robust testing and evaluation, and other key recommendations can help organizations reduce risk and improve outcomes.

While some companies are reporting tens and even hundreds of millions of dollars in savings from AI implementations for call centers and sales enablement, a reported 46% of organizational AI projects "are scrapped between proof of concept and broad adoption," according to the 2025 S&P Global Market Intelligence report.

In addition to the hurdles organizations encounter in turning AI initiatives into production-ready solutions, more users are experiencing a growing number of real-world AI failures with real-world consequences — 1,200 and counting, according to MIT's AI Incident Tracker, spanning issues like cybersecurity, discrimination, misinformation, system safety, and more. Real-world AI failure risks reported among these incidents range from chatbots providing dangerously incorrect medical advice or encouraging users to commit suicide, to self-driving car crashes, to social media algorithms that amplify scams or hate speech, among many others.

As organizations strive to deploy AI in high-risk, safety-critical domains like transportation, healthcare, and infrastructure, it's crucial for engineers, executives, and policymakers to identify failure modes and understand what can be done to mitigate unintended or harmful outcomes, including discrimination, socioeconomic inequities, privacy violations, and human injury — all of which can lead to massive financial losses and legal liabilities.

Common AI failure modes

In simple terms, an AI failure is any outcome where an AI system doesn't do what it's supposed to do — it deviates from intended performance or causes harm. Broadly speaking, AI failures tend to fall into five buckets.

1. Biased or Insufficient Data: The AI's training data is biased, incomplete, or unrepresentative, leading to skewed or ignorant decisions. For example, a 2025 study of AI-generated social care case note summaries found the physical and mental health needs of women were downplayed relative to men's, which the researchers pointed out "may lead to gender-based disparities in service."
2. Algorithmic or Model Issues: Flaws in AI design or implementation. A well-known example occurred in 2021, when an AI-powered home valuation model significantly miscalculated housing prices. This error cost the company more than $300 million and ultimately led it to shut down.
3. Out-of-Distribution (Edge Cases): AI systems may not be fully equipped to handle rare or atypical cases outside their primary training data. McDonald's piloted an AI-powered voice ordering system that led to order errors, increased wait times, and public backlash, underscoring the importance of training data quality and diversity — and real-world testing — to support model robustness and accuracy.
4. Perception & Sensor Errors: The AI receives misleading inputs from sensors or misinterprets them. In 2023, a robotics company employee was crushed to death by a sensor-equipped industrial robot when it failed to differentiate him from a box of vegetables.
5. Adversarial Attacks & Security: Failures induced by malicious actions. Incidents targeting Amazon's Q AI coding assistant and OpenAI's ChatGPT chatbot highlight the risks of data breaches and malware stemming from adversarial attacks. Additionally, potential vulnerabilities in over-the-air (OTA) updates and vehicle-to-everything (V2X) communication systems used by self-driving cars have led to scrutiny over public safety and cybersecurity risks involving AI-operated vehicles

Recommendations to mitigate AI failure

Organizations can reduce risk and improve outcomes when deploying AI in high-stakes environments with proactive strategies, including the following.

• Involve data science and domain experts early: Engage data scientists, domain specialists, and risk management professionals from the outset to help confirm that AI system requirements, data sources, and evaluation criteria are well defined and aligned with organizational goals.
• Conduct rigorous data audits: Regularly review and audit training and operational data for bias, gaps, and representativeness. Addressing data quality issues early helps prevent downstream failures due to skewed or insufficient data.
• Implement robust testing and validation: Test AI systems extensively using real-world and edge-case scenarios. Validate performance not only in typical cases but also in rare or high-impact situations.
• Monitor for out-of-distribution and adversarial inputs: Continuously monitor deployed AI systems for unexpected inputs, adversarial attacks, or environmental changes that could trigger failures.
• Establish clear accountability and incident reporting: Define roles and responsibilities for AI oversight. Create transparent processes for reporting, investigating, and learning from AI incidents.
• Align with industry standards and best practices: Follow established frameworks such as ISO/IEC 23894 and NIST AI RMF for risk management, documentation, and continuous improvement.
• Foster a culture of cross-disciplinary collaboration: Encourage ongoing communication between engineers, business leaders, legal teams, and end users to inform AI system design and deployment with diverse perspectives.

Real-world failures and real-world success

The consequences of AI failure ripple far beyond technical teams, impacting business outcomes, regulatory landscapes, and public trust. Recognizing the shared stakes and actively engaging with key risks, critical safeguards, and proactive strategies can help organizations develop AI solutions that are less likely to fail in the field and more likely to achieve widespread adoption.