A global pharmaceutical company’s AI-powered supply chain system was automatically approving supplier shipments worth a month of supplies. The system analyzed delivery schedules, quality certifications, and pricing data, all appeared normal according to its algorithms.
But an experienced supply chain manager would have immediately questioned why a critical raw material supplier suddenly doubled their delivery frequency while maintaining the same total volume. This pattern suggested potential quality issues or manufacturing problems that required immediate investigation.
The AI missed early warning signs of supplier distress that ultimately led to a $15 million product recall when contaminated materials reached production facilities.
The real cost wasn’t just financial; it was months of regulatory delays and damaged relationships with customers.
This wasn’t a technical failure; It revealed the importance of establishing proper governance mechanisms for high-stakes automated decisions.
This example illustrates a truth about AI: every system operates with inherent uncertainty.
No AI is 100% accurate because it’s making decisions based on patterns in data, not definitive answers. This risk-reward equation applies whether you’re deploying analytical AI for predictions, generative AI for content creation, or agentic AI for autonomous decision-making; each carries the potential for costly errors that require strategic management.
The question for executives isn’t whether your AI will make mistakes—it will. The question is whether you understand the cost of those mistakes and how they compare to the value AI automation delivers.
“One small AI use case, poorly managed, can have organization-wide devastating effects. As an executive, it’s your responsibility to understand the risks before they become crises.”
The AI Risk-Reward Equation: When Errors Are Worth the Trade-Off
Smart executives approach AI as a calculated risk equation: What’s the upside of automation versus the downside of mistakes?
Consider a fraud detection system (Analytical AI) that’s 95% accurate. It will make errors, but if those errors cost you $2 million annually while the system prevents $50 million in fraud, that’s a profitable trade-off. The key is understanding both sides of the equation before you deploy.
The reality: In 2018, AI false positives cost U.S. ecommerce merchants $2 billion in lost sales [1]. But those same systems likely prevented tens of billions in actual fraud. The winners weren’t those with perfect AI—they were those who understood their error costs and managed them strategically.
How Much Will AI Errors Cost Your Business?
As a leader, your job isn’t to eliminate AI errors—that’s impossible. Your job is to understand the cost of those errors and weigh them against the business value AI automation will deliver.
Every AI system makes two types of costly mistakes:
False Positives: When AI Sees Problems That Aren’t There
- Example: Fraud system blocks legitimate customer transactions
- Real Cost: $2 billion lost by U.S. merchants in 2018 [1]
- Hidden Costs: Customer service calls, reputational damage, customer churn
- Customer Impact: 20% of customers switched banks after fraud detection errors [1]
False Negatives: When AI Misses Real Problems
- Example: Predictive maintenance fails to detect equipment failure
- Real Cost: Unplanned downtime, safety incidents, regulatory fines
- Hidden Costs: Emergency repairs cost 3-5x more than planned maintenance
- Business Impact: Complete system failures that could have been prevented
Why Most AI Risk Management Fails: The Circle vs. Diamond Problem
Here’s why most AI initiatives create unexpected risk: executives focus on perfecting the technology while ignoring the decision-making process.
At the RoAI Institute, we call these the “Circle” (insights) and the “Diamond” (decisions). Think of AI like having a very smart analyst who gives you recommendations (the Circle), and a decision-making process that determines what actions to take (the Diamond). Most companies focus on perfecting the Circle—getting better recommendations—but ignore the Diamond, where the real risk lives.
Why this matters for risk management: You can have a technically solid AI that still creates business disasters if you haven’t thought through how decisions get made, who’s accountable, and what safeguards exist.
Example: A utility company’s AI predicts storm damage, forecasting 1,000 outages with 85% accuracy. But because they haven’t established clear decision thresholds, chaos ensues.
The operations team sees “1,000 outages” and stages massive resources: 200 repair crews, 50 trucks with specialized equipment, and emergency contractors on standby. Cost: $2 million in preparation.
When the storm hits and only 200 outages occur (within the AI’s error range), they’ve wasted $1.6 million on unnecessary preparation. Worse, when the next storm prediction shows 500 outages, management hesitates, stages minimal resources, and 800 actual outages leave customers without power for days.
The Diamond is where you establish clear rules: “For predictions over 500 outages, stage X crews. For predictions over 1,000, escalate to executive approval. For predictions with confidence below 90%, increase human oversight.” Without these frameworks, even perfect AI becomes a business risk.
The Diamond is where you manage risk through human oversight, escalation procedures, and accountability structures.
When Should Humans Override AI Decisions?
Not every AI decision needs human oversight, but the consequential ones do. The critical question: Where in your AI system is the technology making decisions with significant financial, safety, or reputational consequences?
Risk-Based Decision Framework:
The most effective approach is to establish clear dollar thresholds that determine the level of human involvement required. This creates consistent decision-making across your organization while ensuring appropriate oversight for high-stakes situations.
Example thresholds (customize based on your organization’s scale and risk tolerance):
- Low-risk decisions (under $10,000 impact): Automate completely
- Medium-risk decisions ($10,000-$500,000 impact): Require manager approval
- High-risk decisions (over $500,000 impact): Escalate to executive team
Advanced Strategy: Use Certainty Levels
Deploy AI systems that provide not just predictions but also certainty levels. This creates a safety net for uncertain decisions while allowing confident predictions to proceed automatically.
How it works in practice: Instead of AI simply flagging a transaction as “fraudulent” or “legitimate,” it provides a prediction with a confidence score—for example, “85% confident this is fraud” or “40% confident this is fraud.” You then set thresholds: predictions below 70% confidence automatically route to human analysts for review.
Real impact: One healthcare study found this approach reduced false positives by 23% [3]. The AI system continued making high-confidence diagnoses automatically, but uncertain cases received human expert review, combining AI efficiency with human judgment where it mattered most.
Business benefit: This approach can substantially reduce false positives while maintaining the same level of automation for clear-cut decisions.
Generative AI Marketing Example:
Consider how a global consumer goods company might deploy generative AI to create personalized email campaigns for millions of customers. Rather than having the AI simply generate “good” or “bad” content, they could implement confidence scoring for each generated message.
How it would work: The AI generates marketing copy and provides confidence scores like “94% confident this message will drive engagement” or “67% confident this approach resonates with this customer segment.”
The framework in action:
- High confidence (>85%): Messages deploy automatically to customer segments
- Medium confidence (70-85%): Marketing managers review for brand alignment and tone
- Low confidence (<70%): Senior marketing leadership approves before deployment
Potential impact: This approach could prevent brand damage when the AI generates messaging that’s technically accurate but culturally tone-deaf for certain regions. High-confidence campaigns could still reach the majority of customers automatically, while uncertain content receives human oversight—protecting brand reputation while maintaining marketing efficiency.
The Executive’s AI Risk Checklist
Before approving any AI initiative, ask these five questions:
□ What specific business decision will this AI make?
□ What’s the dollar cost if it’s wrong 5% of the time?
□ Who’s accountable when it makes mistakes?
□ How quickly can we reverse a bad decision?
□ What’s our escalation process for high-risk situations?
The Bottom Line: Manage Risk, Don’t Avoid It
AI errors are inevitable. Organizational crises from AI errors are optional.
The organizations thriving with AI aren’t those with perfect algorithms—they’re those with sophisticated risk management frameworks. They understand that managing AI isn’t a technical problem; it’s a leadership responsibility.
Your choice: Build these frameworks now, or wait until a preventable AI mistake costs your organization millions.
The companies that will dominate their industries in the next decade won’t be those that avoid AI risk—they’ll be those that manage it strategically while capturing AI’s transformative value.
Take Action Now: Assess AI Risk and Maximize AI Value
Don’t let AI uncertainty hold you back from transformative business outcomes. In a focused 90-minute session, we’ll use our structured assessment approach to analyze the risk-reward equation specific to your organization and provide you with actionable insights.
What you’ll get:
- Discussion of your organization’s specific AI risk-reward equation using our proven framework
- Preliminary RoAI (Return on AI) score with industry benchmarking
- Discussion of the risk and reward in your current AI initiatives based on the RoAI score
Contact me today: Laks@roaiinstitute.com
References
[1] Softjourn, “AI False Positives: How Machine Learning Can Improve Fraud Detection,” Softjourn Insights, March 17, 2025. [Online]. Available: https://softjourn.com/insights/how-machine-learning-can-reduce-false-positives-increase-fraud-detection
[2] C. Strohm et al., “Can incorrect artificial intelligence (AI) results impact radiologists, and if so, what can we do about it? A multi-reader pilot study of lung cancer detection with chest radiography,” European Radiology, vol. 33, no. 12, pp. 8842-8851, 2023.
[3] C. Cem, “Machine Learning Accuracy: True-False Positive/Negative,” Marketing Scoop, April 2, 2025. [Online]. Available: https://www.marketingscoop.com/ai/machine-learning-accuracy/
[4] ACAMS, “Artificial Intelligence: The Implications of False Positives and Negatives,” ACAMS Today, August 2, 2024. [Online]. Available: https://www.acamstoday.org/artificial-intelligence-the-implications-of-false-positives-and-negatives/
[5] IBM, “False Positives Are a True Negative: Using Machine Learning to Improve Accuracy,” Security Intelligence, March 20, 2020. [Online]. Available: https://securityintelligence.com/false-positives-are-a-true-negative-using-machine-learning-to-improve-accuracy/
[6] Veriti.ai, “The True Cost of False Positives: Impact on Security Teams and Business Operations,” Veriti Blog, March 5, 2025. [Online]. Available: https://veriti.ai/blog/the-true-cost-of-false-positives-impact-on-security-teams-and-business-operations/