Many companies are working quickly to expand their use of artificial intelligence. While they often highlight their automation successes, they also face a bigger, less visible challenge, building trust.
Even the most accurate model can become a problem if people don’t trust it, if it can’t explain its decisions, or if it misses crucial context. Adding expert oversight to AI workflows is now essential for success.
This article will show you how to build human expertise into AI systems from the start, so trust is part of every step.
Why AI Systems Still Need Experts
AI is very good at identifying patterns, detecting outliers, and making predictions. It can even mimic some types of reasoning. But it still doesn’t have the real-world experience or judgment that experts bring.
For example, an anomaly detection model might detect a spike in transaction volume and flag it as an outlier. However, a domain expert might immediately recognize that it’s simply due to a scheduled dividend payout or an anticipated market rebalancing. Without this context, the system generates a false alarm. Across thousands of signals, users start to ignore alerts.
This isn’t just inconvenient. It shows a real problem in how people and machines work together.
Embedding humans in AI isn’t just for emergency brake functions; it’s a governance imperative. As the AIFN article “AI Governance in Real Time” puts it, “True AI governance isn't just about compliance; it's about architecting trust at scale.”
Where Experts Belong in the AI Lifecycle
Embedding experts is not a one-time consultation; it requires ongoing support. It’s an architectural choice. Their input shapes the system at multiple stages:
- Model development: Informing data labeling, feature selection, and known exceptions.
- Validation workflows: Reviewing early results to refine thresholds and expected behaviors.
- Production monitoring: Vetting anomalies, suppressing known-good deviations, and triaging alerts.
- Feedback loop: Feeding validated expert decisions back into the model. This helps ongoing learning.
With this approach, expert input becomes a crucial part of the design, rather than just a backup plan.
A Real-World Example: LSTM + Expert Feedback
Let’s say you’ve built a deep learning model, perhaps using Long Short-Term Memory (LSTM) for time series anomaly detection. It flags sudden drops in performance metrics based on deviations from historical trends.
But not every change is a problem. Sometimes there are good reasons for them. Without expert context, the model can create a lot of unnecessary alerts.
In our peer-reviewed IEEE paper on scalable AI-driven quality control, we introduced an expert-in-the-loop mechanism. It mathematically suppresses false positives based on validated feedback. This system reduced alert fatigue and improved precision over time, particularly in situations where manual review is impossible.
How to Make It Scalable
Adding expert feedback doesn’t mean you’ll always need manual reviews. Systems can be designed to learn from expert input and improve over time. Here are some practical ways to do this:
- Customizable alerting: Allowing users to set thresholds based on severity, persistence, or business priority.
- Dependency-aware detection: Evaluating anomalies in context across peer groups or sectors. This reduces noise from systemic events.
- Exception catalogs: Maintaining expert-reviewed lists of explainable anomalies to prevent repeated alerts.
These principles echo a growing industry consensus. As McKinsey’s 2024 AI report notes, “Most leading companies are retooling AI systems with built-in human review processes to avoid compliance and trust failures.”
From Automation to Collaboration
Human-in-the-loop isn’t a detour from AI advancement; it’s the core of building resilient, adaptive systems. As many leaders in the AIFN community have insightfully noted in this article, “If AI is the engine of automation, human skills are the steering wheel.” The future of enterprise intelligence is not autonomous; it’s collaborative.
Final Takeaway
The best AI systems do more than get things right; they also learn and adapt. They ask for expert input and keep improving. To move from simple automation to real teamwork, add experts to your AI workflows and turn them into trusted partners. Start integrating expertise today.
