By Dr. Aruna Dayanatha PhD, CMC
Introduction: Imperfection is a Starting Point—Not a Disqualification
In human resource management, it’s long been understood that individuals may excel in most areas while showing inefficiencies in a few. Rather than discarding such talent, progressive organizations invest in diagnosing, developing, and optimizing that potential. The same philosophy must now guide how we treat AI systems.
As AI becomes more integrated into business processes—from customer service to data analysis and decision-making—it’s important to recognize that AI models are not born perfect. Like human employees, AI has strengths, blind spots, and learning potential. Rather than expecting flawless performance, organizations must adopt a developmental mindset: assess where AI excels, where it underperforms, and evolve it accordingly.
Human and AI Parallels: Recognizing Partial Performance
Both people and AI systems can:
- Perform exceptionally well in some domains.
- Make errors in unpredictable or niche contexts.
- Improve when given structured feedback and retraining.
Just as a skilled employee may struggle with time management or stakeholder communication, an AI model may handle structured data brilliantly but fail to interpret sarcasm in language or adapt to cultural nuance in customer interactions. These are not failures of adoption, but invitations to refine and evolve.
Applying Performance Management Principles to AI
Organizations can borrow principles from HR to build a framework for AI performance governance:
1. Strength-Based Deployment
Start by identifying what the AI system does exceptionally well. Use it primarily in those areas while shielding or supporting it in weaker domains.
Example: An AI assistant that drafts excellent reports but struggles with real-time voice recognition should be used for asynchronous content generation rather than live interactions.
2. Root Cause Analysis of Failures
When AI misfires, dig deeper. Was the training data insufficient? Was the use case outside its original design? This is parallel to understanding whether a human performance issue is due to unclear expectations or insufficient resources.
3. Continuous Feedback and Iteration
Just as employees benefit from coaching, AI models require ongoing monitoring and tuning. Set up review loops where outputs are audited, errors logged, and retraining datasets curated based on observed weaknesses.
4. Individualized Development Plans—For AI
Instead of thinking in terms of static systems, consider each AI model as evolving. Fine-tune language models on domain-specific vocabulary. Update vision models with new image types. Develop versioned performance scorecards.
5. Supervisor Oversight (Human-in-the-Loop)
Even the best employees benefit from managerial oversight—so should AI. Design systems where humans can intervene when the AI is unsure, flagged, or drifts from expected behavior.
The Role of Digital HR and AI Stewards
This approach blurs the traditional lines between HR and IT. Digital HR teams or AI governance councils must oversee AI model performance like a digital workforce. They:
- Track model maturity.
- Maintain version control and audit history.
- Recommend “upskilling” (e.g., new training data).
- Determine retirement or redeployment of AI models.
The same way organizations no longer think of people as “fixed assets,” they must stop treating AI as plug-and-play technology. AI evolves—and that evolution must be led.
Evolving AI into a Super-Efficient Contributor
When organizations embed this performance development mindset into their AI lifecycle, several benefits emerge:
- Models remain relevant longer.
- Performance improves with contextual exposure.
- Risk is reduced via proactive bias/error management.
- Stakeholder trust grows as transparency and oversight increase.
Ultimately, just like with humans, AI does not need to be perfect to be valuable. It needs to be monitored, understood, supported, and continually improved.