Using AI in procurement: What you need to know

Artificial Intelligence (AI) is increasingly becoming a pivotal tool in transforming procurement processes, offering a range of applications that enhance efficiency, reduce costs and improve decision-making. In fact, unsurprisingly, we named it one of the top 10 trends in global procurement in our State of Procurement in Manufacturing Report 2024. 

Here we break down how to use AI in procurement:

Smarter supplier selection and evaluation: AI technologies can analyze vast amounts of supplier data, including performance metrics, payment terms, competitive pricing, compliance records, and financial stability, to assist procurement teams in making informed decisions. By evaluating suppliers against specific criteria, AI can recommend the best matches for the organization's needs, ensuring quality and reliability.

Optimizing contract management: AI can streamline contract management by analyzing contract data to extract key information, identify potential risks and suggest improvements. This capability minimizes errors and enhances efficiency in contract creation, review and negotiation processes. By automating these tasks, organizations can ensure compliance and mitigate risks more effectively. Generative AI can also simulate negotiation scenarios and predict the outcomes, allowing buyers to evaluate and identify the most effective tactics.

Enhancing supplier relationship management: AI tools can provide insights into negotiation strategies, contract management and dispute resolution, fostering stronger supplier relationships. By analyzing historical data, AI can predict supplier behavior and recommend actions to improve collaboration and resolve conflicts.

Walmart, for example, is using AI to automate their negotiations with vendors. This AI-driven approach to negotiation helps in achieving more favorable terms and conditions by leveraging vast amounts of data to make informed negotiation decisions.

Finance, accounting and compliance: Organizations can use AI tools to monitor procurement processes for identifying potentially fraudulent activities or anomalies. Accounting teams can benefit from improved scanning and parsing solutions for supplier invoices. The finance teams can also benefit from automated bank account validations and foreign exchange impact analysis.

Spend analysis and spend management: With spend often fragmented across dozens (if not hundreds) of suppliers, business units and site locations, it can be a minefield to try to identify and capture enterprise-wide savings opportunities. AI can power platforms that can help analyze your spend, benchmark you against your peers and uncover savings.  

Demand forecasting and planning: AI's ability to analyze historical data and market trends enables accurate demand forecasting. This helps procurement teams to devise effective strategies and make informed sourcing decisions, ensuring that inventory levels are optimized to meet demand without excessive overstocking.

Knowledge capturing and sharing: Generative AI systems can capture valuable insights and institutional knowledge, acting as a virtual assistant for procurement professionals. This facilitates knowledge sharing within the organization, ensuring that critical information is accessible and utilized effectively. 

Generative AI for task automation: Generative AI is transforming procurement by automating repetitive tasks, which frees up time for procurement professionals to focus on strategic decision-making. This includes automating paperwork, purchase orders, and even some aspects of negotiation as mentioned above.

Inventory management: AI is being used to optimize inventory levels by predicting future consumption patterns based on historical data. This helps in reducing excess stock and minimizing stockouts, ensuring that inventory levels are aligned with actual demand.

Predictive maintenance: In manufacturing, AI is used for predictive maintenance of machines. By analyzing data from sensors and historical maintenance records, AI can predict when a machine is likely to fail or require maintenance, thereby reducing downtime and maintenance costs.

Mitigating supply chain disruptions: Generative AI can be used to build risk evaluation models that can monitor external risk factors, process vast amounts of data to predict risk key performance indicators (KPIs) and enable preventative management before supply chain disruptions occur. By enabling proactive measures, AI can help maintain smooth operations, ensuring that goods and materials are delivered on time and within budget.

Challenges in adopting AI in procurement

Despite its potential, the adoption of AI in procurement faces challenges and span technical, organizational and regulatory domains, including: 

Technical familiarity and awareness

• Lack of technical expertise: Many procurement leaders and their teams may lack the necessary technical knowledge to understand and implement AI tools effectively. They will need to invest in upskilling in these areas to capitalize on its benefits and remain competitive. Another option is to recruit skilled AI professionals to bridge the expertise gap and lead AI initiatives within the organization.
• Data management: Managing large volumes of data, ensuring data quality and maintaining data privacy are significant hurdles. Current data architectures often struggle to support the performance-intensive workloads required for AI applications.

Data governance

• Data privacy and security: Ensuring data privacy and security is paramount, especially given the sensitivity of data involved in AI applications. This includes compliance with regulations like GDPR and maintaining robust data governance practices. 

Investment in unified and composable data management platforms to handle data integration, quality, and privacy effectively can help overcome this challenge. Enterprises will also need robust data governance frameworks that include encryption, access controls and regular data audits to ensure data security and compliance.

• Data sharing resistance: In collaborative projects, there can be resistance to sharing data due to privacy and security concerns, which can impede AI adoption.

Organizational challenges

• Cultural resistance: There may be resistance within the organization to adopting new technologies due to fear of job displacement or skepticism about AI's benefits. As this article clearly outlines, the boon to businesses goes far beyond a mere buzzword. 

Procurement leaders will need to implement change management strategies to address cultural resistance. This includes clear communication about AI's benefits and involving employees in the AI adoption process. 

• Alignment across stakeholders: Achieving alignment among various internal stakeholders, such as finance, IT and security, is crucial but often challenging. Engage them early in the AI adoption process to ensure alignment and buy-in.

Governance and oversight

• Regulatory compliance: Navigating the regulatory landscape and ensuring compliance with evolving AI regulations can be daunting. This includes understanding and adhering to ethical guidelines and frameworks. Enterprises should form dedicated teams to stay abreast of regulatory changes and ensure compliance with AI-related laws and guidelines.
• Oversight mechanisms: Establishing clear processes and frameworks for technology oversight and governance is essential to mitigate risks and ensure responsible AI use.

Overcoming these barriers requires a strategic approach, including selecting the right AI solutions, ensuring organizational readiness and focusing on change management and capability building.

As you can see, the capabilities of AI in mining data for actionable insights, cost savings and efficiencies is unparalleled. However, according to one McKinsey survey,  the majority of Chief Procurement Officers stated that  “they lacked technology platforms that could perform thorough, integrated, real-time data processing” and said, consequently, “that less than 20% of their organizations’ available procurement data was currently used.” That isn’t good enough in today’s world. To compete - and win - AI must be incorporated into processes, but also systems and strategies. 

AI offers transformative potential for procurement, from smarter supplier selection and enhanced contract management to improved demand forecasting and knowledge sharing. By adopting AI, organizations can achieve greater efficiency, reduce costs and enhance strategic decision-making in procurement processes. But they must move, and move fast or risk getting left behind.