What If AI Makes a Mistake? Understanding Risk vs. Current Reality

The Mistake Question 

Controllers worry AI agents will make mistakes damaging customer relationships or creating financial exposure. Understanding how AI makes mistakes, how often, and comparison to current manual handling risk provides realistic risk assessment. 

AI mistakes differ fundamentally from human mistakes in pattern, frequency, documentation, and fixability. 

How AI Agents Make Mistakes 

Mistake Category 1: Misunderstanding Customer Statements 

What happens: Customer provides ambiguous response. AI interprets incorrectly and proceeds based on wrong understanding. 

Example: Customer says "I'll get that to you soon." AI interprets as payment commitment. Customer meant providing documentation. 

Frequency: 2-5% of conversations have interpretation issues 

Detection: Call recordings reveal misunderstanding. Customer contact or staff review identifies issue. 

Resolution: Refine conversation scripts to confirm understanding. "Just to confirm, you'll send payment by Friday, correct?" 

Impact: Low. Misunderstandings get corrected through follow-up before causing material issues. 

Mistake Category 2: Applying Wrong Rule 

What happens: AI applies decision logic incorrectly due to configuration error or edge case not considered in rules. 

Example: VIP account not properly flagged. AI contacts customer who should receive personal attention only. 

Frequency: Less than 1% when properly tested 

Detection: Customer complaint or staff oversight identifies issue. 

Resolution: Fix rule configuration. Add account to VIP list. Prevent recurrence. 

Impact: Moderate. Potential relationship damage but isolated to specific accounts. Correctable. 

Mistake Category 3: Technical Failure 

What happens: Platform issue causes call drop, message failure, or data sync problem. 

Example: API connection fails. AI cannot access current account status and proceeds with outdated information. 

Frequency: Less than 0.5% with reliable platforms 

Detection: Automated monitoring alerts to technical issues. Failed connections logged. 

Resolution: Platform provider fixes issue. Retry affected exceptions. 

Impact: Low. Technical issues are temporary and detected quickly. 

How Humans Make Mistakes 

Mistake Category 1: Inconsistent Application 

What happens: Staff apply rules differently based on mood, workload, or personal judgment. Same situation handled differently on different days. 

Example: One day staff offers payment plan flexibly. Next week under pressure, staff declines similar request. 

Frequency: 10-20% of exceptions handled inconsistently 

Detection: Difficult. No systematic review of manual handling. Inconsistency discovered only when customer complains or management reviews retrospectively. 

Resolution: Training helps but human variability persists. 

Impact: Moderate to high. Customers perceive unfairness. Relationships damaged. Inconsistent handling becomes precedent expectation. 

Mistake Category 2: Forgotten Follow-Up 

What happens: Staff intends to follow up but forgets due to workload, distraction, or task falling through cracks. 

Example: Customer commits to payment Friday. Staff forget to call Monday when payment not received. 

Frequency: 5-15% of commitments lack systematic follow-up 

Detection: Discovered when account remains overdue. No systematic tracking prevents proactive detection. 

Resolution: Better systems help but human forgetfulness continues. 

Impact: High. Delayed collections extend DSO. Customer learns commitments not enforced. 

Mistake Category 3: Undocumented Activity 

What happens: Staff handle exception but documentation incomplete or missing. Future staff lack context. 

Example: Customer explains payment delay due to invoice dispute. Staff note "will pay next week" without dispute details. 

Frequency: 20-30% of exceptions have incomplete documentation 

Detection: Becomes evident when different staff member handles account and lacks context. 

Resolution: Process improvement helps but documentation quality remains variable. 

Impact: Moderate to high. Repeated questions frustrate customers. Resolution time extends. Pattern analysis impossible. 

Risk Comparison 

Frequency 

AI mistakes: 2-6% of exceptions  

Human mistakes: 15-35% of exceptions 

Advantage: AI makes fewer mistakes 

Consistency 

AI mistakes: Same mistake repeats across similar situations until fixed  

Human mistakes: Variable mistakes across situations and time 

AI risk: Single error affects multiple exceptions before detection  

Human advantage: Mistakes are distributed, not systematic 

Net assessment: AI systematic errors are fixable organization-wide. Human variable errors persist individually. 

Documentation 

AI mistakes: All interactions recorded and reviewable  

Human mistakes: Most interactions undocumented or partially documented 

Advantage: AI enables retrospective review and learning 

Fixability 

AI mistakes: Rule change fixes issue across all future exceptions  

Human mistakes: Training may improve but individual variation continues 

Advantage: AI mistakes are structurally fixable 

Detection Speed 

AI mistakes: Detected through systematic review or customer complaints  

Human mistakes: Detected primarily through customer complaints 

Advantage: AI enables proactive detection through sampling 

Risk Mitigation Strategies 

For AI Agents 

Call sampling: Review 10-15% of calls monthly for quality issues 

Escalation monitoring: Track what triggers escalation. Excessive escalation indicates overly conservative rules. 

Customer feedback: Solicit feedback on AI interaction quality 

VIP protection: Flag relationship-critical accounts for human-only handling 

Gradual deployment: Start with subset of exceptions. Expand as confidence builds. 

Continuous refinement: Monthly script and rule improvements based on review findings 

Current Manual Approach 

Spot checking: Manager occasionally reviews staff work 

Customer complaints: Reactive detection when customers raise issues 

Process documentation: Written procedures staff should follow 

Training: Periodic refresher on exception handling 

Quality measurement: Subjective assessment of staff performance 

The Accountability Question 

"If AI Makes Mistake, Who Is Responsible?" 

Answer: Company remains responsible regardless of whether AI or human makes mistake. 

AI scenario: AI contacts customer inappropriately. Company apologizes, addresses issue, refines rules. 

Human scenario: Staff contacts customer inappropriately. Company apologizes, addresses issue, provides training. 

Responsibility is identical. Method of mistake (AI vs human) does not change company accountability. 

Legal and Compliance Perspective 

AI documentation advantage: Complete call recordings provide clear record of what was said. Proves or disproves customer claims. 

Manual handling gap: Incomplete documentation creates he-said-she-said disputes. Difficult to verify what occurred. 

Compliance value: Regulated industries benefit from complete audit trail AI provides. 

Real Mistake Examples and Outcomes 

Example 1: AI Misinterprets Payment Plan Request 

Situation: Customer asks "Can I split this into two payments?" AI interprets as request for 30-60 day split. Customer meant split between two payment methods today. 

Detection: Customer calls back confused about payment plan documentation 

Resolution: Staff clarifies misunderstanding. Makes note in account. Updates AI script to confirm "Do you mean two payments over time, or paying today with two methods?" 

Outcome: Customer mildly inconvenienced. Issue resolved immediately. Script improvement prevents recurrence. 

Comparison to manual: Human might make same misunderstanding but no systematic improvement occurs. 

Example 2: VIP Account Not Flagged 

Situation: Strategic account receives automated collection call instead of personal controller outreach. 

Detection: Customer contacts account manager expressing surprise at automated call 

Resolution: Account flagged VIP immediately. Controller makes personal call apologizing. Account excluded from AI going forward. 

Outcome: Minor relationship friction. Personal follow-up resolves. Process improved to verify VIP flags before deployment. 

Comparison to manual: Different staff member might have made same call manually. Relationship friction similar. No systematic prevention. 

Example 3: Platform API Failure 

Situation: ERP API temporarily unavailable. AI accesses cached data showing old account status. Makes calls based on outdated information. 

Detection: Monitoring alerts to API issue within 10 minutes. Staff pause AI activity. 

Resolution: Wait for API restoration. Review affected accounts. Make follow-up calls where needed. 

Outcome: 3 customers contacted with slightly outdated information. Manual calls clarify. Technical issue resolved within 2 hours. 

Comparison to manual: API issues affect manual operations similarly. Staff cannot access current data either. 

The Risk Reality 

AI agents make mistakes. So do humans. The question is not whether mistakes occur. The question is frequency, consistency, documentation, and fixability. 

AI advantages: 

• Lower mistake frequency (2-6% vs 15-35%) 

• Complete documentation enables detection 

• Systematic fixes prevent recurrence 

• Consistent application across exceptions 

AI disadvantages: 

• Single error affects multiple exceptions before detection 

• Technical dependencies create failure modes 

• Customer acceptance varies (10-15% reject AI interaction) 

Net assessment: For routine exception handling, AI mistake risk is lower than manual handling risk. For relationship-critical accounts, human judgment remains appropriate. 

About the Author 

This content is published by ERP AI Agent, a consulting practice specializing in AI agents for mid-market ERP exception processes. 

Published: January 2025 Last Updated: January 2025 Reading Time: 7 minutes