It’s hard to attend a manufacturing conference, join an industry webinar or sit through a leadership meeting these days without hearing about artificial intelligence.
Just a few years ago, AI was viewed as something futuristic or experimental. Today, many manufacturers are actively implementing AI tools to improve forecasting, maintenance planning, inventory management, purchasing and financial reporting. These real-world AI in manufacturing examples demonstrate how quickly artificial intelligence is moving from a future concept to an operational necessity. The potential benefits are significant, and companies are moving quickly to capture them. A growing concern is that many organizations are adopting AI faster than they’re updating the controls and governance around it. Conversations with manufacturing and distribution clients often reveal that the technology itself isn’t the biggest challenge. The bigger challenge is making sure the right people understand how these tools work, what decisions they’re influencing and whether proper oversight exists.
Most manufacturers aren’t chasing technology trends for the sake of innovation. They’re responding to real business pressures.
Labor continues to be difficult to find and retain. Experienced employees are retiring and taking decades of knowledge with them. Supply chains remain unpredictable. Margins are under pressure from labor costs, material costs and higher borrowing costs. At the same time, customers expect faster delivery, better communication and more visibility into orders. AI promises to help with many of these challenges. Companies are using it to improve demand forecasting, optimize inventory levels, predict equipment failures before they occur, automate purchasing decisions and analyze large amounts of operational and financial data much faster than people can. As a result, AI has quickly moved from being an IT initiative to becoming a business strategy initiative. That shift means finance leaders can no longer afford to view AI as someone else’s responsibility. Many of these tools directly influence assumptions and estimates that ultimately impact financial statements.
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One of the most common uses of AI is demand forecasting and production planning.
Modern forecasting tools can analyze far more information than traditional spreadsheets, including customer buying patterns, seasonality, economic indicators and other external data. Better forecasts can lead to lower inventory levels, fewer stockouts and more efficient production schedules.
The challenge is that these models still rely heavily on historical data. If business conditions change suddenly, the forecast may appear accurate while actually being far off target.
Predictive maintenance has become another popular application.
Sensors can monitor equipment performance and identify potential failures before they happen. Avoiding a major production disruption can create tremendous value.
The risk is that employees may become overly reliant on system alerts and stop performing some of the routine inspections that historically identified issues. Over time, trust in the technology can replace healthy skepticism.
AI-powered inventory tools can continuously adjust reorder points and safety stock levels based on changing conditions.
These tools can free up working capital and reduce excess inventory, but they rely heavily on accurate master data. If lead times, item classifications, costs or demand assumptions are incorrect, the recommendations may also be incorrect.
Some organizations are using AI to assist with supplier selection, purchasing decisions and supply chain monitoring.
While this can increase efficiency, it may also create control concerns. Traditional purchasing processes often involve multiple levels of review and approval. As automation increases, organizations need to ensure those controls don’t disappear unintentionally.
Finance departments are also beginning to use AI for variance analysis, trend identification, anomaly detection and even drafting management reporting.
These tools can save a significant amount of time. However, a report that looks professional isn’t necessarily accurate. Teams still need to validate conclusions and investigate unusual results.
Interestingly, the biggest risk here isn’t the technology itself. It’s human behavior.
When an AI tool consistently produces reasonable results, people naturally begin to trust it more. They stop challenging outputs as aggressively as they did during implementation. Manual reviews become less thorough. Independent recalculations become less common. Over time, the technology earns trust, and scrutiny gradually decreases. That’s where problems can begin. Unlike people, AI systems don’t typically signal uncertainty. They’ll often produce an answer regardless of whether the underlying data is complete or whether current conditions look anything like the data they were trained on.
Withum’s advice to clients is simple: AI should help people make better decisions, not replace their judgment altogether. The person responsible for a decision should still be able to explain why the result makes sense.
In many organizations, internal controls were designed around people performing specific reviews and approvals. A buyer reviewed purchase requests. A planner reviewed production schedules. A controller reviewed reconciliations. When decisions become automated, those manual checkpoints may disappear. Unless companies intentionally redesign their control environment, gaps can emerge.
A few areas deserve particular attention:
Organizations don’t necessarily need an entirely new control framework. However, they do need to think carefully about whether existing controls still work in an environment where AI is driving more decisions.
As AI adoption accelerates, governance matters more than ever. Register for this on-demand webinar to explore practical strategies to help minimize risk, balance innovation and increase oversight and accountability across your organization.
If your organization is already using AI, here are a few questions worth discussing with management:
That last question is usually where the conversation gets interesting. Many organizations are surprised by how much influence AI can have on inventory valuations, reserve calculations, cash flow projections, impairment analyses and budgeting assumptions.
You don’t need a massive governance project to get started. A few practical steps can go a long way:
Most importantly, don’t assume that because a tool worked well last year, it will continue working well forever.
These AI in manufacturing examples show just how much influence artificial intelligence can have across production, procurement, inventory management and financial reporting. While the benefits of AI for manufacturing companies are substantial, organizations must ensure that governance and internal controls evolve alongside the technology.
AI adoption in manufacturing is only going to accelerate. Organizations that pair innovation with strong governance will be better positioned to realize the benefits of AI while managing risk effectively. If your organization has implemented AI within the last year, consider taking inventory of every decision those tools influence and comparing them to your existing control environment. You may be surprised by what you discover, and it’s far easier to address potential gaps today than during an audit.
Wondering whether your control environment can keep up with AI? Withum can help.
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