ERP solutions are excellent at helping companies be organized. An ERP is a single source of truth, containing everything from financial data to customer orders, invoices to inventory, and more. It keeps everything working smoothly.
However, the problem is that most ERP processes today operate in a 2012 manner.
Data is still manually transcribed from PDF documents to ERP web forms. Workgroups still use email for approval processes. Demand is still predicted by planners based on last year’s spreadsheets.
So it is no surprise that AI has entered ERP implementations, not as a novelty, but as a practical tool to streamline processes and provide quicker data-driven decision-making. For many organizations, working with a generative AI development agency is also becoming a practical way to accelerate adoption.
This article provides seven examples of AI’s capability inside ERP and illustrates how it can help organizations decrease costs while frequently maintaining the existing ERP’s core capabilities.
Why combining AI with ERP saves companies money
ERP systems collect the data that all organizations need; AI provides intelligence to that data. With AI, organizations will have the capability to discover patterns within data, discern irregularities in data, and automate repetitive tasks that are performed by humans. As soon as an organization adopts AI with the support of an AI assisted development partner on specific business processes, it can generate measurable benefits from the reduced level of inventory, man-hours, lost productivity, and delayed payments.
The most exciting aspect of this idea is that it is not something an organization should worry about doing in the future; rather, organizations are beginning to create small use cases with the hopes of scaling once they begin receiving those benefits from using AI with their ERP system.
1. Demand Forecasting That Isn’t Outdated Each Season
The majority of businesses don’t incur financial losses due to fluctuating customer demand; rather, they incur losses due to inaccurate monthly, quarterly, and annual demand forecast reports.
Historically, ERP vendors provided forecasting capabilities based on “rules of thumb” or historical averages. As long as customer demand patterns remain the same, all is well. But what happens when a retailer experiences more seasonality than ever before? When does a competitor roll out a promotion? When customer demand spikes in one area while at the same time, customer demand declines in another area?
How AI makes a difference
With AI-driven demand forecasting, retroactive analysis will allow retailers to rapidly identify and respond to demand trends, account for rapidly changing variables, and minimize reliance upon guesswork.
Where the savings come from
By providing more accurate demand forecasting, there is less inventory on hand (overstock), fewer instances of inventory stock outages (stock out), and less reliance upon last-minute decision-making for replenishing inventory (billing/invoicing) during peak selling periods.
2. Processing Invoices Automatically Eliminating the “Nightmare” of Manual Data Entry
Accounts Payable is full of little things that add up very quickly: opening invoices, entering line items, matching documents, and following up on missing approvals.
Now, take that and multiply it by the hundreds or thousands of invoices every month, and now AP is just a cost center for a company that has to do all of this work manually.
AI provides a solution
AI can read invoices and pull out important information from them automatically, validate the information against the Purchase Order and/or the Receipt, and then send the invoice to the appropriate person for approval.
Where AI Saves You
AI saves the time spent per invoice, reduces human error, and prevents duplicate payments or late fees.
3. Detecting Fraud And Avoiding Costly Errors Before They Are Posted To The Ledger
Not all losses in finance are caused by outrageous acts of fraud. Some are smaller and very common, such as: re-issuing an invoice, being paid “just a little less or too much,” or changes made by vendors to their account information that go unnoticed.
The downside is that finance departments typically do not discover errors until after payments have been made or during internal audits.
How Does AI Help
AI can identify behavior that is inconsistent with normal operating procedures. It is capable of identifying unexpected changes (flags) to normal and time-sensitive payments, along with identifying inappropriate activity (transactions) that do not match what is expected in the normal course of business.
Where Do Cost Savings Come From?
The primary advantage of an early detection system is that it reduces the amount of money lost because of poor decision making, decreases duplicate processes and the need for rework due to the reduction of risk, and eliminates the requirement to perform manual processes on a continual basis.
4. Predictive Maintenance that Prevents Unplanned Downtime in Manufacturing, Logistics, and Field Operations
An unanticipated stop in the schedule of production, distribution, and/or delivery of goods is one of the quickest ways to lose money in manufacturing, logistics, and field service.
The reason for this is that conventional methodologies for maintaining a company’s equipment do not generally produce the results companies want to see. With regard to conventional methodologies, preventative methods tend to be wasteful (servicing too soon), while reactive methods tend to be very costly (servicing too late).
How AI helps
AI predicts failures based on equipment history, sensor data (if available), and maintenance patterns. It helps teams fix the right thing before it breaks.
Cost Savings Resulting from AI Use
Less emergency repairs required, fewer production interruptions, and more effective utilization of maintenance resources.
5. Intelligent Procurement That Stops Overpaying by Default
When procurement is under pressure: short lead time, irregular suppliers, and an immediate demand for products (materials), the urgency to secure products fast increases the likelihood of incurring higher costs due to hasty purchasing over thoughtful purchasing.
AI Solutions
Using AI to assist with procurement provides the ability to evaluate supplier performance, identify price inconsistencies, and suggest the best time and quantity to purchase.
Cost Savings Through AI
By having less risk with suppliers, fewer costly interruptions, and making price decisions from an analytical perspective rather than from intuition, businesses will experience the most significant cost savings.
6. Inventory Optimization That Fixes “We Have Stock, Just Not Here”
If you have multiple warehouses or other locations that manage inventory, chances are high you’ve experienced this same situation:
Some locations (warehouses) have far more inventory than needed, while other locations are out of stock, leading to urgent and expensive shipping. The total quantity of the inventory looks adequate; however, the distribution of the stock is poor among locations.
How AI helps
AI is able to forecast where demand will rise, allowing for the redistribution of inventory between locations before shortages occur.
Where Cost Savings Come From
Utilizing AI to forecast and balance inventory will reduce the amount of dead stock, eliminate rushed inventory transfers, lower expedited shipping costs, and maintain a higher level of service than would have been possible without increasing your total inventory level.
7. Workforce Planning That Reduces Overtime and Scheduling Chaos
Hiring excess labor is not always the cause of high labor costs. High costs often result from the mismatch between staffing levels and workload.
When organizations experience this mismatch, their costs are increased by:
- Costs from overtime and burnout during periods of peak activity
- Costs from underutilization during periods of low activity
How AI Can Assist
AI can use information about operational demand, historical data, and workforce availability to automatically forecast staffing requirements. This use of AI allows organizations to make better use of their data for shift planning and optimizing the allocation of labor to tasks.
Where AI Is Eliminating Cost
With AI, companies can reduce overtime, fill fewer holes in their schedules, and maintain a consistent level of productivity—all without the hassle of trying to keep up with constant fires.
Why Do These AI Use Cases Provide Quick Return on Investment (ROI)?
Not all AI projects provide instant rewards. The above-mentioned AI use cases allow for quick improvements because they focus on solving problems where companies are already losing money due to inefficiencies from human errors, manual processes, etc.
Eliminating Redundant/Manual Operations
ERP groups spend a considerable portion of their time completing repetitive manual operations, including entering invoices, approving invoices, matching documents, and verifying exceptions. AI will automate each of these processes so that employees do not spend valuable time completing activities that do not need to be completed manually.
Avoiding Costly Errors
Duplicate payments, fraud, incorrect forecasts, and unexpected downtime will create losses that surpass expectations. With AI’s ability to identify anomalies earlier in the process, companies will be able to address an issue before it results in a loss of profit.
Increasing Decision-Making Efficiency in Broader Impacts
Decisions for planning, purchasing, inventory distribution, and staffing create an impact on large budget items. A small increase in efficiency of these types of decisions through the use of AI will multiply quickly since these decisions affect many transactions, orders, and employee work hours.
Less Difficult to Implement and Measure
The implementation of these use cases is usually possible without uninstalling an existing ERP system, making it easy to measure ROI in weeks versus months.
Final thoughts
When considering AI for ERP, it’s important to remember that this won’t be a complete overhaul of your entire system, nor will it be a complete replacement. Instead, it’s likely going to improve existing workflows by increasing speed, intelligence, and cost efficiency. You may want to focus your early efforts on one expensive workflow or process and improve it, and then build on that success before trying to introduce an entire new “AI Transformation Roadmap”. Once you’ve demonstrated the ability to achieve significant cost savings from one process, it will be much easier to implement similar improvements in other areas of your business.
