Procurement powered by Artificial Intelligence: the risks and challenges

The use of artificial intelligence has made companies work smarter and improve profitability. All segments are benefiting.

In addition to autonomous vehicles, predictive maintenance and customer-oriented chatbots, AI can have an immediate positive impact on results, helping companies select suppliers that provide goods and services at the lowest price and with the lowest possible risk.

We can list here some opportunities and challenges in the use of Artificial Intelligence and increase the effectiveness in Procurement.

Spending analysis

Spending analysis can be equipped with artificial intelligence software to collect, clean, classify and analyze expense data and help procurement teams to identify excessive costs. For example, it is possible to identify when duplicate suppliers were used to purchase the same goods, when urgent purchases were made without using better terms than existing contracts and when there were less than ideal payment terms.

However, to find these opportunities, AI software needs to be good enough to classify the data. Standards- and statistics-based AI techniques can have weaknesses in relation to one-off purchases and infrequently used suppliers. They can also get lost in new languages and geographic locations, which is happening more and more frequently as supply chains become global. The best way to achieve ROI is to pilot a system in which there is a high volume of transactions involving repeated standard purchases, so that there are more opportunities to increase efficiencies.

Strategic supply of suppliers

When using AI, Procurement executives can be prepared with knowledge about market conditions, future mergers and acquisitions, as well as product comparisons and real-time support. This ensures a data-driven strategy for choosing suppliers and that the purchase is in the best possible terms.

The use of AI also reduces the time required to analyze all supporting data. The evaluation of responses to a bidding process can be reduced by up to 80%. It can also be used continuously to provide supplier recommendations on demand. Responding to market opportunities in seconds versus weeks can accelerate time-to-market, by receiving needed parts and materials more quickly.

Guided purchasing is another AI innovation that allows employees to quickly purchase goods and services from preferred suppliers with minimal support from procurement teams. Employees can use voice-activated commands to find the best price or a supplier who can deliver on time when there is an urgent request. Many of these systems allow direct communication with suppliers with built-in rules to ensure that the purchase process complies with the procurement policies.

Many automatic personal assistants also have the advantage of being able to learn from experience. But if the AI system is fully self-taught, there is a risk that it will be corrupted by outside influences, so communications and procedures need to be protected from hackers or malicious employees. This was evident in the case of Microsoft's Tay, who was taught by social media trolls to use inappropriate language and hate speech before being removed from the market for further testing.

Automated contract analysis

Most organizations do not have a database with all their contract data, and they certainly do not have an easy way to extract all of that information. As a result, there is no quick and efficient way, for example, to view and compare contracts.

Using artificial intelligence, companies can review and organize contracts more quickly, as well as find large amounts of contractual data to significantly reduce the possibility of contractual disputes and increase the number of contracts they can negotiate and execute.

For example, company contracts can be accessed based on renewal dates to check conditions and negotiate accordingly. Finance and Procurement teams can verify that price discounts are not being applied consistently across the organization, in accordance with the terms of the contract, or even monitor the drafting of specific clauses in different departments.

The beauty of AI contracting software is that it helps organizations maintain consistency in terms and usage across all contracts, which makes it easier to identify instances of non-compliance and ensures that less-than-ideal provisions are addressed quickly.

The challenge: data and application integration

However, none of the benefits of AI can be achieved without a solid database. Companies need to invest in data management, in addition to data and analytics, to get a 360-degree view of their business operations. Only when your CRM, ERP and financial systems are fully integrated, will they be able to access all the necessary data.

Initially, point-to-point integrations may seem more economical when only a few systems are connected. But over time, with more and more data shared with different departments, suppliers and partners, a third-party integration platform can result in lower development and maintenance costs, while providing consistent scalability and data manipulation that is needed.

These scenarios lead us to conclude that, once companies have a solid database with all the necessary integrations and data sharing, new platforms based on machine learning can be used to reinforce the best procurement practices. Although AI procurement systems are not always accurate today, machine learning allows algorithms to learn from data, allowing platforms to continually improve.

As we begin to see spending analysis platforms classifying data at levels of 98% accuracy - the same level as human analysts - it is increasingly likely that AI will become a reliable tool for the Procurement process.