Artificial intelligence (AI) is becoming more common – and more critical – in supply chain planning. As businesses adopt and deploy new AI-powered tools, leaders are realizing that they need to address much more than just the technicalities of a rollout. Processes and governance are integral to AI’s success, and determine whether your organization makes the most of your new demand planning tool, or it sits collecting dust.
Consider this example: within StockIQ, a feature called Global Unusual Sales detects outlier orders – such as orders that are abnormally large – and flags them, allowing you to strategically omit them from skewing forecasts. This outlier detection is so robust that you can even configure the AI to auto-create events for unusual sales it detects.
While this feature is powerful, it’s not something you can “set and forget.” To achieve maximum results, it needs direction and oversight.
This article breaks down why AI governance is critical for supply chain excellence, using the example of Global Unusual Sales. Plus, we’ll give you a straightforward AI governance framework you can use in your own organization.
Why Does AI Governance Matter in Stock Management?
Artificial intelligence governance is the practice of deciding who controls an AI system, how its behavior gets defined, and what happens when that behavior needs to change. Think of it as the organizational habits surrounding AI: the roles, review processes, and documented decisions that keep an AI system aligned with your business.
For manufacturers, distributors, and other supply chain organizations, the impacts of AI governance are immediate and operational. When it’s lacking, your planning system can quietly drift out of alignment, while the AI continues to appear like it’s working. Research from McKinsey shows that as AI becomes more autonomous and embedded, “gaps in governance and risk management will become increasingly costly.”
In inventory management specifically, the stakes of that drift are direct, and often financial. Inventory AI doesn’t produce reports that sit in a folder. Its outputs move downstream into safety stock calculations, replenishment recommendations, and projected inventory positions that finance teams use to make capital decisions. Without adequate, clear governance, costs can climb and stock levels can balloon.
However, when you get AI governance right, the benefits are numerous. Your systems can operate at their peak capacity, improving visibility, lowering inventory levels, and boosting profitability.
AI governance is part of “Responsible AI,” a term coined by PwC. Their research shows that when executives use responsible AI practices, they boost ROI, efficiency, customer experience, and innovation.
Understanding Governance with Global Unusual Sales Settings
To best understand the value of AI governance, let’s explore one AI-powered feature within StockIQ: Global Unusual Sales. Here’s a quick description of what this useful tool does:
The Unusual Sales screen provides insight into unusual sales in the system; this provides awareness into sales that could potentially significantly impact your forecast, and gives you the option to manage adjusting them out of the forecast, via events.
This screen is powered by StockIQ’s AI model, and it considers such factors as the item’s neighbors, nearby sales activity as well as the overall pattern for the product, and determines if an outlier meets the model’s criteria for being considered an outlier.
Let’s say a customer makes a very large order. A “typical” demand forecasting tool will likely include that figure in your forecast, no questions asked. But this “outlier” can lead to skewed inventory analysis & recommendations. What if it’s a one-time occurrence? Then incorporating it into the forecast can inaccurately inflate results. Or, on the other hand, what if it reflects a repeatable pattern that should be integrated into your forecasts?
Here are some things to know about Global Unusual Sales:
- Sensitivity: Controls how far actual sales must deviate from the statistical forecast before StockIQ classifies a transaction as unusual and surfaces it for review.
- Auto-event creation: When this setting is enabled, an “event” will automatically be created for sales that surpass the following thresholds.
- Thresholds: The specific numeric boundaries that define the bands of acceptable demand variation around each SKU’s forecast.
AI governance over these settings matters because of the way miscalibration amplifies throughout the system. For example, if unusual sales that should have been excluded are instead absorbed into the baseline, they inflate that error metric.
Human context is also a critical part of governance. Unusual sales can be one-time events that should be normalized, planned promotions that should be modeled, or early signals of real demand shifts, and the distinction between those categories is not something an algorithm can reliably make on its own. Only a human with context – someone who talked to the sales team, checked the customer record, or happened to know that a competitor had a supply problem last month can make that call correctly.
A Practical Governance Model: Who Owns What
Mastering AI governance requires clarity on who is responsible for each layer of the decision-making process, and what that responsibility entails day-to-day.
The following model is designed for mid-market manufacturers and distributors: practical, scalable, and implementable without adding headcount.
Supply Chain Planning Owns the Day-to-Day
Supply chain planning should typically own the working process. This team is closest to the forecast, replenishment recommendations, alert volume, and planner workload. They should be responsible for reviewing unusual sales alerts, validating whether flagged sales should become events, and identifying when the current sensitivity settings are creating too much noise (or not enough signal).
Planning should also be the first group to recommend parameter changes. For example, if planners see that too many normal customer orders are being flagged as unusual, they may recommend raising thresholds or narrowing which unusual sales checks are enabled.
Sales and Marketing Own Business Context
AI can detect patterns, but it does not always know the story behind the pattern. Sales and marketing should own the context around promotions, customer events, new account activity, lost accounts, and intentional demand-shaping campaigns.
Finance Owns the Cost and Risk Lens
Finance should not need to approve every unusual sales alert. But finance should have visibility into the settings that affect inventory investment, working capital, and service-level trade-offs.
If AI settings become too conservative, the business may under-forecast and risk stockouts. If settings become too aggressive, the business may overcorrect for spikes and carry more inventory than needed. Finance’s role is to help evaluate the business impact: how settings influence excess inventory, projected inventory value, service goals, and the cost of missed demand.
System Administrators Own Access and Change Control
Admins should own who can change Global Unusual Sales Settings, but they should not be the only decision-makers. Their role is to manage access, document changes, and make sure configuration updates follow an agreed process.
Leadership Owns the Policy
Operations, supply chain, or S&OP leadership should own the overall governance policy. That means defining how often settings are reviewed, who participates in reviews, and what level of change requires approval.
AI Governance: A Pillar of AI Success
AI can make stock management faster, smarter, and more responsive, but only when the business understands how it is being guided. As we’ve discussed, StockIQ’s Global Unusual Sales Settings give teams powerful control over how demand spikes are detected, when alerts are created, and whether unusual activity should be isolated from the forecast.
That’s why AI governance matters. With regular reviews, subset testing, and documented decisions, teams can keep AI aligned with changing business strategy, seasonality, promotions, and customer behavior.
StockIQ helps distributors move from reactive inventory management to proactive inventory planning. By combining AI-driven forecasting, unusual sales detection, replenishment insights, and inventory optimization tools, StockIQ helps teams prevent stock problems before they turn into service failures, excess inventory, or missed sales opportunities.
Ready to put smarter governance behind smarter planning? Schedule a StockIQ demo today.
FAQs
1. What are Global Unusual Sales Settings in StockIQ?
Global Unusual Sales Settings are system-level controls in StockIQ that govern how the platform detects and responds to demand anomalies across your SKU catalog. They define how sensitive the detection is, whether events are created automatically, and where the thresholds are drawn that separate normal demand from outliers.
2. Why is AI governance important?
AI governance is important because AI settings directly influence how demand signals are interpreted, which alerts planners see, and how inventory decisions are made. Without clear governance, small configuration changes can create forecast noise, missed demand signals, excess inventory, or stockout risk.
3. What is a practical governance framework I can use in my organization?
A practical framework is to define who owns each part of the process: supply chain planning monitors alerts and recommends changes, sales and marketing provide demand context, finance reviews cost and risk impacts, admins control access, and leadership approves policy.