Your AI Made the Wrong Decision. What Happens Next?
Estimated reading time: 8 minutes
An AI system produces an incorrect result.
What happens next depends entirely on whether your organisation knows it happened.
Consider a practical scenario. An organisation uses artificial intelligence to support a business process. One day, the system produces an incorrect result. Perhaps a legitimate customer is rejected. Sensitive information appears in an output. An employee receives inaccurate advice. A customer receives an inappropriate recommendation.
Someone discovers the problem. What happens next? Organisations have established processes for cybersecurity incidents, privacy breaches, workplace incidents and technology failures. AI incidents can be less straightforward. Research published in July 2026 examining AI incident governance highlights an emerging challenge. Organisations still lack consistent approaches to defining, identifying, reporting and learning from AI related incidents.
Source: July 2026 research on open problems in AI incident governance. Full citations in the References section below.
What Counts as an AI Incident?
The answer is not always obvious. Consider several scenarios:
- An AI assistant exposes confidential information
- An automated system incorrectly rejects an application
- A chatbot provides materially incorrect customer advice
- An AI generated report influences an executive decision using inaccurate information
- A model behaves differently following an update
- An employee relies on an AI output that creates a compliance problem
Some situations may clearly involve privacy, cybersecurity or regulatory obligations. Others may fall into less defined territory. That ambiguity can create a practical problem. If employees do not know what constitutes an AI incident, they may not know when or how to report one.
This is where governance becomes visible. An AI governance assessment can help organisations establish clear definitions of incidents and reporting responsibilities before situations arise. Clear incident definitions should also inform security questionnaire responses and compliance documentation.
AI Systems Can Fail Without Technically Breaking
Traditional technology incidents are often visible. A server stops responding. An application crashes. A user account is compromised. Data becomes unavailable. There is usually a clear signal that something is wrong.
AI systems can fail differently. The application may continue operating normally while producing an inappropriate, inaccurate or harmful output. There may be no error message. From a technical perspective, the system may appear healthy. From a business perspective, something has gone wrong.
An AI system does not need to stop working
to create a material incident.
This makes human reporting, monitoring and clear accountability particularly important. Employees need to be able to identify concerning behaviour even when the underlying technology appears to be functioning.
Who Owns the Incident?
AI incidents can cross traditional organisational boundaries. A problematic output could involve:
- IT, because technology is involved
- Cybersecurity, because information or system integrity may be affected
- Privacy, because personal information may be involved. Data protection leadership should be part of incident assessment
- Legal and compliance, because the output could create regulatory consequences
- Risk, because the incident could expose weaknesses in organisational controls
- Business leadership, because the AI supported a business decision
Without defined responsibilities, an incident can move between teams without clear ownership. Escalation becomes unclear. Investigation stalls. Learning does not happen. This is where information security management becomes important, ensuring clear coordination across the teams involved.
Governance Question
If an employee discovered concerning AI behaviour today, would they know who to report it to and what information to capture? If that answer is unclear, governance may need revision.
A Practical AI Incident Process
Organisations do not necessarily need an entirely separate incident management function for AI. Existing cybersecurity, privacy and risk processes can often be adapted. A practical approach could include five stages. Establishing this process may require tools and implementation support to ensure workflows are clear and accessible to all teams involved.
1. Recognise
Employees need a simple way to recognise potentially significant AI behaviour. The threshold should focus on business impact rather than requiring employees to understand how the underlying model works.
2. Record
Capture what occurred, which AI system was involved, the affected process, relevant inputs and outputs, and who discovered the issue. This creates evidence for investigation and future learning.
3. Assess
Determine the potential impact. Could customers, employees, sensitive information, regulatory obligations or important decisions be affected?
4. Escalate
Clear thresholds should determine when an incident requires involvement from security, privacy, legal, risk or executive leadership. Through our vCISO service, we help organisations establish these escalation pathways as part of broader governance.
5. Learn
The organisation should understand why the event occurred and whether changes are required. That could involve adjusting controls, changing human review requirements, modifying the system, updating procedures or reconsidering whether AI should be used for the process.
Human Oversight Matters After Deployment
AI governance discussions often focus on approving systems before they are introduced. Operational experience after deployment can be equally valuable. AI systems interact with changing information, users and business environments.
An organisation may only discover certain risks after people begin using the technology in real situations. Incident reporting therefore provides another source of governance information. Patterns may reveal that employees misunderstand the tool, human review is insufficient, certain use cases create unexpected risk or a provider's model changes have affected behaviour. This continuous oversight is foundational to effective security leadership.
This is not a failure of initial governance. It is a recognition that governance must be continuous, not a one time approval.
Do Employees Know Where to Report AI Problems?
This is a simple but useful test. Imagine an employee notices that an approved AI tool has produced something concerning. Would that employee know:
- Whether the issue should be reported?
- Where to report it?
- What information to capture?
- Who is responsible for reviewing it?
If the answer is unclear, the organisation may have an AI incident visibility gap. Policies alone may not solve this. Employees need practical reporting processes that fit naturally into the way they work. Security awareness should extend beyond traditional cybersecurity to help employees understand their role in identifying and reporting AI related incidents.
Executive Implications
Boards and executives do not need to investigate individual AI outputs. They do need confidence that material incidents can reach the right people and that the organisation is learning from experience.
Useful questions include:
- What do we currently define as an AI incident?
- How can employees report one?
- Who assesses the business impact?
- When are privacy, security, legal or risk teams involved?
- What incidents require executive escalation?
- Are incidents recorded so patterns can be identified?
- How do lessons influence future AI use?
These questions become increasingly relevant as organisations move from isolated AI experiments to broader operational adoption.
Key Takeaways
- AI incidents can occur even when the underlying technology continues operating normally
- Organisations need practical definitions and reporting processes so employees can recognise and escalate concerning AI behaviour
- Existing cybersecurity, privacy, risk and incident processes may provide a useful foundation, but responsibilities need to be clear
- Recording and learning from AI incidents helps organisations improve controls as their use of AI grows
- Governance must continue after deployment, not end when a system is approved
Test Your AI Governance
The Executive Readiness Review Includes AI Risk Assessment
As AI moves deeper into business operations, executives need confidence that material risks are understood and managed. The Executive Readiness Review helps leadership teams understand:
- How AI is currently being used across the organisation
- Whether incident reporting and escalation processes exist
- Governance maturity across the AI lifecycle
- Priority areas for improvement
Start by understanding where your organisation stands today.
Start the Executive Readiness Review →References
- July 2026 research on open problems in AI incident governance and operational safety. Accessed July 2026
- NIST AI Risk Management Framework. Guidance on identifying and managing AI related risks. nist.gov
- ISO 42001:2023. Artificial intelligence management systems. Standard on establishing and managing responsible AI. iso.org
- Australian Government. Responsible AI principles and guidance. Accessed July 2026. industry.gov.au
This article is for general informational purposes only and does not constitute legal, technical, or professional cybersecurity advice. SeComPass recommends engaging a qualified adviser before making decisions based on this content. Guidance referenced above was current as at July 2026 and is updated periodically by relevant government and standards bodies.
