AI failure examples: What real-world breakdowns teach CIOs

AI hallucination failure

Fabrication of information by GenAI systems is one of the most prominent and legally significant failure modes that enterprises face

Rebecca Wettemann, CEO of industry analyst firm Valoir, worked with an appliance manufacturer that built a conversational service agent to guide customers through basic repairs. Although the system had access to all product and service manuals, it had over 100 different instruction sets for changing filters across different models. Wettemann said the result was "a munged-together version of multiple sets of instructions, a complete mess." The company had to rebuild its knowledge base with a more modular approach that verified the customer's specific model before delivering instructions.

Lessons for CIOs:

• Hallucinations are not edge cases but known failure modes that require guardrails and validation layers.
• Ground AI agents in verified data sources before allowing customer interaction.
• Implement model performance monitoring to detect fabrication patterns early.

Bias and discrimination failures

AI models can encode and amplify discrimination in ways that create legal exposure, particularly in hiring, lending and service-delivery decisions. The challenge stems from training data that reflects historical inequities or from models that optimize patterns without understanding their discriminatory implications.

Wettemann advises that teams need clear policies and audit capabilities in place. "Teams need to make sure they understand and communicate how their AI is trained and how their data is being used and have clear policies and audit capabilities in place to protect themselves," she said.

Lessons for CIOs:

• Bias often originates in training data and governance gaps, not malicious intent.
• Continuous auditing matters as much as initial testing.
• Implement ongoing monitoring to detect bias emergence as models interact with real-world populations.

Automation gone wrong

Over-automation without proper oversight amplifies mistakes when AI systems make consequential decisions without review mechanisms or human controls.

Jon Knisley, head of AI enablement and value at ABBYY, worked with a major U.S. health insurance client that acquired an LLM-based system to review claims before payment. After six months of development and investment, the system was slow, expensive to run and produced inconsistent results that flagged legitimate claims for vague reasons the operations team could not explain.

When Knisley's team investigated, they found the system was performing simple pattern matching based on specific procedure code combinations and dollar thresholds. None of it required natural language understanding.

"We ended up implementing a set of fairly basic regex strings and business rules that ran in seconds, cost a fraction of the LLM inference fees, and delivered consistent, explainable and accurate results," Knisley said.

A different automation risk emerged at Daylit, where Jerry Shu, co-founder and chief technology officer, built AI agents to generate daily to-dos for CFOs to raise accounts receivable. The pure agent-based system caught most issues, but was not comprehensive enough. "The business impact was serious: even one overlooked AR task in finance can pose a negative economic impact," Shu said. The company overlaid a system that scans time-based events to ensure every critical AR action surfaces, creating a hybrid process that combines AI automation with deterministic backup systems.

Lessons for CIOs:

• Human-in-the-loop controls are essential for high-impact workflows.
• The goal is appropriate automation with clear escalation paths, not maximum automation.
• Critical processes need backup systems to prevent missed actions.

Data quality and model drift failures

Poor data quality represents one of the most common reasons AI initiatives fail to reach production or deliver unreliable results.

Mariusz Pikuła, CTO and co-founder at LLInformatics, has seen model degradation across client engagements. Models are trained on synthetic datasets, or the data changes, but the preprocessing pipeline remains the same.

"Check your data, validate it often, retrain regularly and make sure your model isn't just memorizing the past," Pikuła said.

Dorotea Baljevic, director at global technology research and advisory firm ISG, notes that the discovery of model drift issues often happens too late.

"Discovery can often be too late and is typically noticed by end users before system owners," she said.

Lessons for CIOs:

• AI systems require ongoing monitoring, retraining and ownership - they do not "set and forget."
• Performance must be monitored against real-world outcomes, not just technical metrics.
• Thresholds need clear definitions and instructions for retraining, rollback or retirement.
• Data quality issues are often the root cause when pilots show promise, but production deployments underperform.

Integration and infrastructure failure

AI tools can break when integrated with legacy systems or create cost spikes not anticipated during pilots.

Knisley worked with a global quick-service restaurant franchise struggling with AI-driven document extraction from commercial lease agreements. The company needed to extract more than 350 data fields from 30,000 lease documents each week. They initially tested an LLM, but the model would extract fields incorrectly or miss them entirely, creating downstream compliance risk. The resolution was only 63% accurate.

No single AI tool solved the problem. Knisley's team tested five technical approaches before landing on a hybrid approach that achieved 87% accuracy, with a human review workflow for anything below the confidence threshold. "The key lesson was that the AI needed guardrails built around it," Knisley said.

Knisley observed another case where a financial services organization decided to build its own intelligent document processing system using hyperscaler tools. Initial projections looked reasonable, but they underestimated the engineering effort. A three-year total cost of ownership analysis revealed more than $1.5 million in technical labor and infrastructure costs, more than three times the cost of a purpose-built platform.

Lessons for CIOs:

• AI readiness is an architectural issue, not a data science problem.
• Before scaling, test integration early with core systems to find issues.
• Understand how costs scale with real usage, including hidden engineering effort.
• Set clear quotas and real time monitoring for AI consumption across departments.

Legal, compliance and IP failures

AI deployments create regulatory exposure when organizations cannot explain decision-making processes or when data handling violates privacy and compliance requirements. These AI governance failures often come from the gap between technical functionality and regulatory requirements.

Michael Murphy, partner and global AI readiness practice lead at Adaptovate, emphasized that AI governance is not optional. At a global pharmaceutical company, his team helped deploy "verification bots," independent agents whose only job is to sanity-check customer-facing outputs against legal policies.

Shu faced a specific data governance challenge -- ensuring AI agents only return data that a user is authorized to see. Junior accounts receivable staff should not see CFO data, and data should not cross clients. The system involved creating strict tools to directly enforce privacy rules.

Lessons for CIOs:

• If AI decisions cannot be explained or traced, they become liabilities.
• Maintain clear documentation of data sources and lawful basis.
• Establish explicit ownership for privacy and compliance before deployment.
• Require cross-functional review involving legal, IT and business teams before production use.

Vendor and strategy failures

Vendor promises and production realities may differ significantly, leading to costly overcommitments.

"One of our large law firm clients bought a paralegal bot only to find it was a paperweight," Murphy recounted. "The firm hadn't cleaned its internal data or connected its research systems, leaving the AI with nothing to think about."

In Murphy's view, the failure was prioritizing the 'sexy' tool over the unsexy work of data hygiene and re-engineering for an agent-first architecture.

Lessons for CIOs:

• Vendor hype often moves faster than enterprise readiness.
• Demand evidence from production-like environments, not just controlled demos.
• Scrutinize how costs scale with real usage and require clear exit paths.
• Prioritize data hygiene and process definition before deploying sophisticated tools.

How CIOs can learn from AI failure examples

The AI failures detailed above are not anomalies but leading indicators of where AI implementations commonly break down. Organizations treated AI as a one-time deployment rather than a capability that requires ongoing governance, monitoring and ownership. The patterns above represent predictable failure modes that emerge when AI systems encounter real-world complexity, changing conditions and high-stakes decisions. CIOs who recognize these patterns can build defenses before incidents occur by doing the following:

• Build governance frameworks before deployment, not after incidents. Establish clear documentation of data sources, defined ownership for privacy and compliance, and cross-functional review processes before production use.
• Monitor real-world outcomes continuously, not just technical metrics. Track performance against actual business results and watch for changes in data and usage patterns. Define clear thresholds for retraining, rollback or retirement.
• Require human oversight for high-impact workflows. Even simple sanity checks can prevent reputational damage. Pikuła stresses that sensitive use cases, such as finance or healthcare, require human-in-the-loop controls to serve as essential safety nets.
• Pilot with clear success and failure criteria before scaling. Treat vendor promises as hypotheses to be validated, not guarantees. This mindset slows premature scaling and keeps experimentation from turning into costly overcommitment.
• Align AI accountability across IT, data, legal and business teams. Cross-functional alignment ensures that technical teams understand legal requirements, business teams understand AI limitations, and everyone shares responsibility for outcomes.
• Treat AI readiness as an architectural and organizational challenge. Test integration early with core systems, understand cost dynamics and invest in data hygiene and process definition before deploying sophisticated tools. AI failures often occur because readiness is viewed strictly as a data science challenge rather than as an infrastructure and workflow issue.

 Sean Michael Kerner is an IT consultant, technology enthusiast and tinkerer. He has pulled Token Ring, configured NetWare and been known to compile his own Linux kernel. He consults with industry and media organizations on technology issues.