Risk Identification through AI
All Procurement professionals are confronted with risks. In a globalized market, it is actually quite easy for Procurement to deal with potential risky partners. Recently, Procurement has changed. From an entity focused only on acquiring products and services for the best price and controlling expenses to a strategic asset of every organization.
Through the development of new technologies such as Artificial Intelligence. Organizations will change how they manage exposure throughout the procurement lifecycle based on contract and supplier analyses, and by managing and optimizing the supply base through the aggregation of suppliers and their contracts into a single AI-powered system. Beyond value measured in savings, many bold procurement organizations are expanding their analytics efforts into the risk analysis territory.
The consequences of a supplier fulfillment interruption or default, can be very negative for organizations. The good news is that it can be monitored in real-time through AI-based analytics.
Most organizations still rely on sourcing teams to perform analyses/reports. However, this solution is very time-consuming and not performed with real-time analytics.
It is safe to bet that AI will make these reports easier to generate, as well as more accurate. Let’s imagine AI-enabled applications that can proactively send alerts when it identifies ways to reduce risk. And since AI-enabled analysis processes are based with real-time data, decision-makers can identify business risks before they even become a problem.
Another idea would be 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. Since AI can extract most data through Natural Language Processing related solutions, it is not impossible to dream about such a solution.
The goal is to get a level in which an AI can make improvements with time, powered by massive amounts of data.
Contracts
Most S2C & P2B can be automated!
Procurement professionals are surrounded with time-consuming tasks involving large volumes of information which create a bottleneck for organizations who need to react quickly to sudden change in the market.
In the context of contracts, Machine Learning techniques (a subset of AI) can identify patterns and compare large volumes of documents/data, both against each other and against algorithms. By doing so, the AI can highlight where a document differs from an accepted market, when an element represents a risk to the organization, etc.
We can envision that an AI could review a finalized set of contracts to identify trends, such as the total negotiation time of particular deals and the types of contract issues that arose, among other elements. The learnings can help drive future enhancements to standard contract terms, support smarter decision-making and increase the velocity of closing key contracts.
AI will impact the entire contract management process. Today, it is used to search the existing contracts in organizations for terms and conditions that may carry a risk for the buyer. Tomorrow, it will help Procurement professionals in the writing process, suggesting appropriate clauses and conditions. Then AI will identify the information needed from the supplier to manage the contract and performance, and ensure this is captured, recorded and reported.
Sourcing
In the context of sourcing-related tasks, Machine Learning techniques can also make a difference. Instead of relying on out-of-date reports, Procurement professionals will have the opportunity to study with real-time data the market.
The access to real-time internal and external data will significantly enhance sourcing accuracy. The AI will deliver information about suppliers and current market conditions, but also tailored-made recommendations based on your data. An AI-powered sourcing solution will ensure a data-driven strategy and make sure that Procurement professionals are getting the best possible deal they can from the sourcing process.
For current suppliers, we can expect that an AI will analyze supplier-related data such as on-time in-full delivery performance, evaluations, and credit scoring and provide information to use for future decisions regarding certain suppliers.
External Data
All AI based solutions are dependent on data.
The real added-value will be the capacity to leverage external data. In a few solutions, the data sources that can be added on top of spend data go far beyond integrated market price through the use of an API. Procurement professionals can also integrate financial risk scores, news feeds, sustainability and corporate social responsibility (CSR) scores and other third-party data sources related to risk.
Procurement professionals will be able to see in a clear way thanks to data visualization techniques combined with AI, how much they have spent with a given supplier, the financial stability of this same supplier and the potential risks facing him based on where this supplier is located (country risks, currency, etc.). This requires far more than a static database with reporting capabilities–it demands accurate, dynamic, real-time intelligence that can only be derived from AI-driven methodologies.
All current decision-making tools can accurately provide insight into past performance and predict future outcomes. A few of them can even combine external and internal data. However, the huge difference them and future AI-powered solutions are that AI has the ability to analyze external factors to predict risks.
In my opinion, most Procurement organizations are either unprepared or have taken a ‘cautious’ approach to AI. Soon enough, AI will become an obligation for Procurement professionals in order to keep up with the industry.
