Procurement is evolving, and Artificial Intelligence (AI) is at the forefront of this transformation. From automating supplier negotiations to enhancing risk assessment, AI in procurement is revolutionizing how businesses manage costs, contracts, and supplier relationships.

Despite AI’s growing adoption across industries, 64% of companies still do not use AI technologies in procurement. However, with 76% planning to implement AI soon, organizations risk falling behind if they don’t act now. 

With AI reshaping procurement, the real question is—how prepared is your team to leverage it?

This guide will walk you through the 11 essential steps to becoming AI-ready in procurement. Whether you're exploring AI procurement automation, AI-driven supplier evaluation, or contract intelligence, you’ll learn:

• How to assess whether AI is the right fit for your procurement processes.
• The different types of AI solutions available and how to choose the best one.
• Practical steps for successful AI implementation with clear KPIs.

By the end of this guide, you will have a clear plan for using AI in procurement. This will help you make decisions faster, save money, and work more efficiently.

Let’s break down the 11 essential steps to becoming AI-ready in procurement.

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The first step is determining whether AI is necessary or if a simpler automation solution, such as RPA, would be more effective

Step 1: Assessing the Need for AI in Procurement

Not every procurement process requires AI. Before investing in AI, organizations should first determine whether AI is necessary or if a simpler automation tool, such as Robotic Process Automation (RPA), would be more effective.

When Does AI Add Value?

AI is most beneficial for complex, high-frequency procurement tasks that require pattern recognition, predictive insights, or decision-making based on large datasets. Common use cases include:

• Contract Intelligence – AI extracts key clauses, deadlines, and risks from supplier contracts.
• Supplier Risk Management – AI evaluates suppliers based on compliance records and external risk factors.
• Automated Negotiation Support – AI reviews past pricing data to provide negotiation insights.

For example, an AI-powered contract analysis tool can instantly identify non-compliant clauses, reducing legal risks and saving significant manual effort.

When is RPA a Better Fit?

For rule-based, repetitive workflows that do not require learning or pattern recognition, RPA may be a more efficient solution.

AI can analyze thousands of supplier proposals to detect trends and recommend the best vendors.

RPA, on the other hand, is more suited for tasks like automating purchase order approvals or reordering standard stock items.

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Without reliable, structured data, AI will produce inaccurate or inconsistent results. In cases where data is fragmented or incomplete, additional work may be required before AI implementation can deliver real value.

Step 2: Choosing the Right AI Model

Selecting the right AI model depends on procurement objectives, data availability, and the required level of customization. Organizations can choose between pre-trained AI models, transfer learning, or custom AI based on their needs.

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Step 3: Defining AI Use Cases in Procurement

To implement AI effectively, organizations must define specific procurement tasks where AI can deliver measurable value. Without clear use cases, AI adoption risks becoming unfocused and inefficient.

Common AI applications in procurement include:

• Contract Intelligence – AI extracts key clauses, deadlines, and risks from supplier contracts, reducing manual review time.
• Supplier Risk Assessment – AI evaluates supplier reliability by analyzing financial stability, compliance records, and performance trends.
• Spend Analytics & Cost Optimization – AI detects cost inefficiencies, highlights overpriced contracts, and suggests savings opportunities.
• Automated Supplier Selection – AI ranks supplier proposals based on price, quality, and past performance, improving decision-making.

To define the right AI use cases, procurement teams should identify pain points, align AI applications with business goals, and assess data availability. If procurement data is incomplete or fragmented, AI solutions may require additional data structuring before delivering value.

Step 4: Setting Clear AI Objectives

To ensure AI delivers measurable value, procurement teams must define clear objectives aligned with business priorities. Without specific goals, AI implementation risks becoming unfocused and difficult to track.

Key objectives for AI in procurement: (table)

• Efficiency Gains – Reduce manual workload in contract reviews, supplier evaluations, or risk assessments.
• Risk Reduction – Improve supplier risk scoring and compliance monitoring.
• Process Automation – Streamline repetitive tasks such as purchase order approvals.
• Cost Savings – Identify pricing trends, optimize supplier negotiations, and reduce procurement costs.

Step 5: Determining Data Requirements and Quality

AI in procurement is only as effective as the data it relies on. Poor-quality or fragmented data can lead to inaccurate insights and unreliable decision-making, making data preparation a critical step before implementation.

To assess data readiness, procurement teams should evaluate:

• Availability – Is procurement data fully digitized, or is it stored in scattered spreadsheets and documents?
• Structure – Is data standardized, or does it require cleaning and formatting before AI integration?
• Accuracy – Is historical procurement data free of inconsistencies and errors?
• Completeness – Are key procurement datasets (supplier records, contracts, transaction histories) fully captured?

Without a structured and centralized procurement database, AI struggles to generate reliable insights. If data gaps exist, organizations may need to prioritize digitization and data cleansing efforts before deploying AI.

Step 6: Upgrading Technological Infrastructure

Once you confirm data readiness, the next step involves ensuring that existing procurement systems can support AI integration. AI models require computing power, storage, and seamless connectivity with procurement tools to function effectively.

For organizations using pre-trained AI models, a cloud-based procurement platform with API connectivity may be sufficient. However, transfer learning and custom AI models demand additional infrastructure, such as:

• Scalable cloud storage for processing procurement datasets.
• AI-compatible ERP systems to integrate AI-driven insights into procurement workflows.
• Data security measures to comply with regulatory and supplier confidentiality requirements.

A lack of AI-ready infrastructure leads to slow processing speeds, security concerns, and system integration challenges. Organizations should evaluate their tech stack to ensure AI implementation runs smoothly and efficiently.

Step 7: Defining Clear KPIs for AI Performance

To measure AI's success, we need clear Key Performance Indicators (KPIs). These KPIs should track how AI affects procurement efficiency, cost savings, and risk management. Without clear benchmarks, assessing AI’s performance becomes difficult.

Some key AI performance metrics include:

• Process Efficiency: Reduction in manual processing time for supplier evaluations and contract processing.
• Cost Optimization: Percentage decrease in procurement costs after AI-driven pricing recommendations.
• Risk Mitigation: Increase in flagged compliance risks before contract approval.
• Adoption & Usage: Number of procurement tasks completed using AI vs. manual intervention.

By setting clear pre- and post-implementation KPIs, organizations can measure how AI helps their procurement strategy. This allows them to make data-driven changes for ongoing improvement.

Step 8: Training Employees and Ensuring Adoption

Even the most advanced AI system is ineffective without proper adoption. Procurement teams need training and change management to integrate AI into daily workflows and decision-making.

A structured AI adoption plan includes:

• Hands-on Training – Interactive workshops and real-case demonstrations to build confidence in AI tools.
• Process Alignment – Defining how AI integrates into existing procurement operations to avoid workflow disruptions.
• Stakeholder Engagement – Involving key decision-makers early to address concerns and secure buy-in.
• Internal AI Champions – Identifying employees who can advocate for AI adoption and assist colleagues in using AI effectively.

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Resistance to AI often comes from uncertainty or lack of familiarity. By making AI literacy a priority, procurement teams can reduce resistance and accelerate adoption.

Watch our webinar to learn more about how to successfully implement AI in procurement.

Step 9: Implementing AI in Procurement Workflows

With trained employees and an AI-ready infrastructure, it’s time to integrate AI into real-world procurement processes. A phased approach ensures minimal disruption and maximum efficiency.

Key implementation steps include:

1. Pilot Programs: Start small—apply AI to a single procurement function such as supplier risk analysis before full-scale deployment.
2. Performance Monitoring: Track AI’s initial outputs, ensuring alignment with expectations and business goals.
3. Iterative Improvements: AI models learn over time—refine workflows based on early insights and feedback.

By introducing AI in stages, procurement teams can change their strategies. They can improve model performance and solve unexpected problems before using it in other departments.

Step 10: Continuous Monitoring and AI Optimization

AI in procurement isn’t a one-time implementation—it requires ongoing monitoring and refinement to remain effective. Researchers need to regularly evaluate and fine-tune AI models to maintain accuracy and relevance.

Best practices for AI performance monitoring:

• KPI Tracking: Continuously measure AI’s impact on procurement speed, cost savings, and accuracy.
• Feedback Loops: Gather insights from procurement teams using AI daily—identify usability improvements.
• Algorithm Updates: AI models improve with more data and fine-tuning—adjust parameters based on procurement trends and supplier behaviors.
• Security and Compliance Checks: Ensure AI systems remain compliant with industry regulations and internal procurement policies.

A well-monitored AI system ensures sustained efficiency improvements and maximizes AI’s long-term business value in procurement.

Step 11: Scaling AI and Long-Term AI Strategy

Once AI has proven effective in procurement, the final step is scaling AI across additional procurement functions and business units. A strategic expansion plan ensures long-term ROI and competitive advantage.

Considerations for scaling AI in procurement:

• Cross-Department Expansion: AI can extend beyond procurement into finance, supply chain, and risk management.
• Advanced AI Capabilities: As teams become comfortable with AI, explore predictive analytics, autonomous procurement, or AI-driven negotiations.
• Investment in AI Talent: Hiring or upskilling procurement professionals with AI expertise ensures continuous innovation.
• AI Governance & Ethics: Establish internal AI policies to ensure responsible AI use and compliance with evolving regulations.

If you want to learn more about how 

AI in procurement is an ongoing journey, not a one-time project. By continuously expanding, refining, and optimizing AI applications, organizations can drive sustained cost savings, efficiency, and innovation in procurement.

If you want to learn more about the future capabilities of AI in procurement, including vertical AI agents and their impact on the industry, check out our Webinar on the topic.

Conclusion: The Future of AI in Procurement

AI is transforming procurement, helping businesses cut costs, improve efficiency, and make smarter decisions. Companies that adopt AI now will stay ahead, while those that delay risk falling behind.

However, AI success depends on clear goals, quality data, and strong execution. Businesses that follow a structured approach and ensure team adoption will see real, measurable benefits.

By applying these 11 steps, procurement teams can move beyond AI experimentation and start seeing Tangible results.

What’s Next?

AI adoption in procurement is no longer a question of if, but how quickly organizations can implement it effectively.

Companies that act now will shape the future of procurement – those that hesitate risk being left behind. Learn more about how to get AI-ready in procurement by watching our Mercanis AI Readiness Webinar

Now is the time to take the necessary steps to successfully implement AI and transform procurement for the future.

Frequently Asked Questions