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How Leveraging AI in Procurement will revolutionize spend management
These insights can then present opportunities for cost reduction, avoiding expensive surprises, more efficient use of human capital, and mitigating risk proactively.
In a sense, it’s both brains and brawn: the brute speed and reach of modern information processing are what enable AI into ever-increasing applications and enterprise areas.
When that analysis horsepower is combined with human intelligence and judgment it can uncover new insights that are not just interesting, but actionable.
The data inside your siloed enterprise systems can obscure opportunities or harbor financial risk.
Or, through the use of Artificial Intelligence, it can become a powerful strategic asset. Artificial intelligence and Machine Learning are often mentioned in the same breath, and though they are closely related, they aren’t the same.
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Artificial Intelligence
(AI) is the ability of an application to mimic human intelligence to the level at which it is difficult to distinguish between human and machine.
Machine Learning
(ML) is a method of letting an artificially intelligent application figure out the steps needed to accomplish a specific goal, instead of being given step-by-step instructions to perform.
In this way, AI can mine mountains of data to reveal insights that procurement organizations can use to avoid expensive surprises, make more efficient use of human capital, and mitigate risk proactively.
AI can do what humans can’t: quickly analyze massive amounts of mundane and seemingly unconnected data to reveal patterns, correlations and anomalies. Specifically, AI can crunch through millions of data points from disparate data sources both inside and outside the enterprise. Some examples include:
- Transaction Details
- Inventory Records
- Consumption and Usage Data
- Contract Terms and Rates
- Inventory Turnover
- Warehouse Utilization
- Product Stock-Outs
- Supplier Fulfillment
- Commodity Pricing
- Market Information
- Historical Pricing
- Industry Baselines
AI Helps Procurement Advance Beyond Spend Analytics
Historically, spend analysis has focused on seeking opportunities to improve efficiency by determining the amount spent by vendor.
Today, enterprises are more decentralized. Procurement occurs across a variety of functional departments, business units, and geographies. And data often resides in multiple systems, including P-card and T&E. Getting an enterprise-wide view, especially for mid and long-tail spend, is increasingly difficult.
To survive in this modern era, finance and procurement organizations share a common set of challenges:
Spend Reduction
Finding new opportunities to reduce spend
Compliance
Ensuring that suppliers comply with all contractual obligations and industry regulations
Efficiency
Turning vast amounts of data into the actionable information needed to make better business decisions
AI Can Create New Opportunities to Reduce Spend and Streamline Operations
Full Visibility
Full visibility into recurring spend across business units
Identify Recurring Employee Spend
Identifying recurring employee spend with suppliers on P-cards or T&E to control maverick spend
Clear View
A clear view into supplier spend across fragmented categories
Flag Transactions
Flagging large transaction outliers to ensure that the expenses is legitimate
Identify Suppliers
Easily identifying suppliers with significant spend growth
Find Duplicates
Identifying the duplicate transactions that signal supplier invoicing errors, payables, or potential fraud
Increase Spend
Increasing the percentage of spend under management
In addition, AI delivers reduces operational costs by eliminating the error-prone, manual process of data ingestion, categorization and normalization saves time. And finance departments can avoid unwelcome surprises that can compromise cash flow and expose the company to unnecessary risk.
AI can Improve the Outcome of Supplier Negotiations
Using AI, legal departments and contract managers can enter into supplier negotiations at the right time, fully prepared with all the relevant data. Procurement can then take advantage of a stronger negotiating position to:
Identify
Identify opportunities to achieve more favorable terms by re-negotiating renewals before the supplier’s fiscal year ends.
Aggregate
Aggregate all supplier spend under one contract to assure best pricing
Prepare
Be better prepared for contract renegotiations with easy access to total vendor spend and other key contract details.
Be Proactive
Proactively negotiate contract renewals, aggregate demand across the enterprise, and identify suppliers operating without a contract.
Increase
Increase the percent of spend under contract to justify volume discounts.
AI can Simplify Risk Identification
The dire consequences of a supplier fulfillment interruption, breach, default, or other lapse are well understood. AI-based analytics can monitor supplier data in real-time to enable early detection of:
-
Pricing Irregularities
-
Suspicious Spending
-
Usage Anomalies
-
Potential Fraud
-
Contract Variances
Many sourcing teams already perform analyses like these, but because of the time commitment required, they are usually only produced on an ad hoc basis.
AI makes these reports easier to generate, as well as more accurate. AI-enabled applications can proactively send alerts when it identifies opportunities to reduce risk. And since AI-enabled analysis processes can run 24-7, executives can identify business risks before they become a problem.
Other risk mitigation benefits include the ability to easily identify suppliers with spend outside of a contract; avoiding an unwanted automatic contract renewal; and being fully aware of impending expiry or renewal dates.
AI can Elevate the Role of Procurement
By connecting data in disparate enterprise systems, AI can provide a deeper level of analysis that can elevate the role of procurement in the enterprise. Consider these examples:
Industry Data
Third-party supplier or industry data can be linked with accounts payable feeds to create a custom risk profile that gauges supply chain disruption and sustainability.
Customer Satisfaction Data
Customer satisfaction data, sales reporting, and purchasing records can be connected to bring quantitative metrics and greater confidence into quality considerations.
Historical Data
Historical purchases and external market information can be married to offer predictive insights that would inform negotiations and order quantities.
These are just a few of the ways procurement organization can leverage AI to contribute strategic value to product development, inventory management, risk assessment and other strategic areas.
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