AI in procurement

Procurement refers to how a company obtains the goods and services it needs for business operations. Therefore, an effective procurement strategy is essential.

AI has only recently been applied to procurement and could revolutionize the way that companies are running their business today. Since procurement requires massive amounts of data, both internal and external, AI tools could play a pivotal role in helping organizations dissect and develop new tools to make more informed sourcing decisions and cost optimization.

Internal tech modernization can be essential to help ensure continued improvements that benefit not just procurement, but also finance, accounting and supply chain operators. By being proficient in the data the procurement team can make more precise predictive analytics and forecasting models.

Businesses that want to stay competitive in the economy need to have deeper insights into their existing structured data sources. The advancements in computing power and AI technology can do just that and help organizations build out existing structured data, along with data from unstructured sources like invoices or rate tables.

AI’s advanced ability to extract information is what sets sourcing and procurement departments apart and can unlock a wealth of value for the entire organization.

Procurement professionals, such as CPOs (Chief Procurement Officers), or departments, depending on the size of the organization, are tasked with delivering cost savings, risk mitigation, compliance standards and more.

It’s more important now than ever for organizations to invest in the value of data and embrace the technology-driven world that we live in. AI technology can bring operational insights into data that was not otherwise visible and ensure organizations have the right resources to operate effectively.

AI-powered tools and advancements will only continue to grow at a global scale. Businesses that want to show continued growth should consider integrating AI into their procurement process. This shift can alleviate procurement professionals from mundane tasks, enabling them to focus on strategic decision-making and driving innovation. AI technology is there to complement the human workforce and empower working professionals to achieve the goals set out by the organization.

It’s important to recognize the significance of AI and the impact it will have on how professionals live, work and do business today. To create a sustainable AI transformation, organizations must learn and understand what AI is and how it can have a profound impact on the sourcing and procurement process.

AI can be used in procurement in several ways, with the most common being:

• Machine learning (ML)
  
• Natural language processing (NLP)
• Robotic process automation (RPA)

Machine learning algorithms are used to detect patterns in large datasets and can assist in the decision-making process. The ML algorithms can go beyond what the human brain might be able to analyze and recognize patterns or relationships that are pertinent to making smart business decisions.

Machine learning models can look at past purchase data, supplier performance metrics, market trends, and much more. All these measurable data points can result in more data-driven decisions and more accurate demand forecasting. A more popular use for ML is in accounts payable automation.

A natural language processing (NLP) algorithm is designed to interpret, transform, and generate the human language. Additionally, they can understand written or spoken language and further analyze it and gain insights from the textual data being collected.

An NLP model can facilitate communication with chatbots or virtual assistants, which allows users to directly interact with a business’ procurement team. Furthermore, a NLP algorithm works by automatically categorizing relevant information from customer feedback, proposal requests, and more, and using that information in the most efficient way possible.

Robotic process automation (RPA) algorithms are built to mimic human actions, such as repetitive or rule-based tasks that can automate and streamline time-consuming actions and instead make them quick and efficient.

RPA is not generally considered a form of AI, however, it offers significant benefits when it comes to productivity. Some examples of RPA in procurement are the automation of invoices and the generation of invoices. RPA can reduce errors by using historical data and reducing the chances of a human error occurring.

Generative AI is changing the way procurement organizations operate today by streamlining processes unlike any tools before and shifting the way procurement leaders are operating.

Generative AI, often shortened to gen AI, is a type of AI that can create original content, such as text or images, in response to a user's prompt. The use of gen AI will have a significant impact on procurement as some of its main functions are creating purchase orders and analyzing documents, such as requests for proposals (RFPs) and contracts.

Gen AI in procurement can streamline procurement functions, such as automating repetitive tasks and analyzing vast amounts of data. The new technology can create spend analysis and optimize cash flow and spend management. It can also summarize documents and analyze prices and spend data in real-time making for much better operational efficiency.

Lastly, gen AI can help streamline interactions between procurement teams, internal stakeholders and suppliers. With gen AI, teams can create human-like conversations and provide a user with a tailored response that is helpful. Procurement is ready for transformation from generative AI, but it will depend on the organization’s ability and willingness to tune its AI models.

A recent IBM Institute for Business Value (IBV) report found that 59% of chief procurement officers (CPOs) believe it’s important to apply gen AI to predictive spending and sourcing analytics. However, the first challenge is convincing them to get behind the new technology, which will require showing your CPO what is possible with AI and the challenges they can overcome.

Separately, chief supply chain officers (CSCOs) and chief operating officers (COOs) see the opportunity on the table with gen AI and procurement as it pertains to sustainability.

The IBV report, The CEO’s Guide to Generative AI, found that 77% of CSCOs and COOs say generative AI can identify potential geopolitical and climate risks and recommend proactive risk mitigation. Three in four also say that generative AI enables better visibility, insights and decision-making across ecosystems, which are so important to sustainability and compliance.

With a strong data foundation and an open mind, generative AI can offer more room for improvement throughout an organization and help CEOs navigate disruptions and mitigate risks quickly.

Intelligent sourcing is an AI-powered procurement platform that is built to analyze a supplier's database and manage historical data. The goal is to provide market trends and help the organization find the right suppliers to fit specific procurement needs and help build stronger supplier relationships.

Error detection uses AI to automatically detect an error before it happens and can prevent workflow disruptions at record speed. AI can detect things such as fraud, compliance irregularities, and potential risks throughout a supply chain lifecycle.

Predictive analytics are AI algorithms that take historical sales data and market trends and analyze them to generate demand forecasts. Predictive analytics can also analyze outside factors, such as weather conditions or economic indicators as part of its analysis.

Automated contract analysis is an AI-powered contract management tool that can automatically analyze a contract and extract the most important facts and information in real time. The automated contract analysis tool flags potential risks and non-compliance issues before they reach a user.

Automated purchase order processing is an AI-driven tool that extracts information from purchase orders, which is a critical document formalizing the agreement between the buyer and the seller, and automating the creation and management process.

Supplier risk management uses AI by automating and analyzing information about suppliers before issues arise. AI supplier risk management can detect hidden patterns and anomalies and can help organizations mitigate potential risks that could slow procurement operations.

Invoice data extraction refers to the use of AI in accounts payable teams by extracting data from invoices through an automated process in a matter of seconds. Organizations that don’t operate with source-to-pay systems or other advanced technology could find this as a good option.

Start by identifying specific pain points in your operation where AI could have an impact, such as inventory management or contract management. It could be automating invoice processing or optimizing spend analysis, whatever it may be having distinct AI use cases will help guide your AI implementation.

Implementing an AI procurement strategy is not a siloed effort and should include cross-collaboration among other departments, such as IT and finance. By keeping the lines of communication open organizations can better align goals and take a holistic approach.

Find experienced AI solution providers or consultants who can help to guide your AI implementation. Collaborating with an expert can steer your organization toward the right AI technologies and custom solutions that fit your business needs.

The AI algorithms run on data and function best with high-quality data. Therefore, it’s important to keep your organization’s data clean and valid without errors. In addition, a business might consider investing in data governance tools to further maintain data integrity.

Bringing in AI technology and doing an AI implementation can be a complex process and the procurement operation professionals must be taught how to properly use the AI tools. Keep communication open with stakeholders to address any concerns they may have with the procurement AI.

The implementation of procurement AI is only the first step. The procurement AI tools require constant review and evaluation of performance. Stakeholders and users should contribute feedback regularly so improvements can be made and newer iterations can be made.