Optimizing contract management in procurement with AI

Procurement refers to how companies acquire the goods and services they need from external sources to operate efficiently, including sourcing and managing providers across the broader supply chain. As procurement processes become more complex and data-intensive, AI is increasingly being used to improve how contracts are created and managed.

Procurement contracts define the terms that govern buyer-supplier relationships and support long-term partnerships, including pricing, delivery obligations, performance expectations and dispute resolution. Because these agreements directly influence costs and services, effective contract management plays a central role in helping ensure that procurement activities deliver the value that was intended.

Contract management extends across the full procurement contract lifecycle. It includes activities such as negotiating terms, executing agreements, monitoring performance and ongoing contract administration, including renewals and amendments. These steps form the broader contract management process and generate large volumes of contract data, which can be difficult to manage without the right tools. AI is increasingly used to help structure and analyze this information, making it easier for procurement teams to maintain control and visibility.

Despite its importance, contract management can be challenging to scale. Many organizations still rely on manual document handling, email-based approvals and spreadsheets to track agreements. These fragmented and time-consuming processes make it difficult to streamline contract management, which can lead to delays, inconsistent language and missed milestones. These challenges increase the risk of disputes with suppliers and a loss of value from the negotiated agreements.

AI can help procurement teams address these issues, often as part of contract lifecycle management (CLM) platforms, contract management software and other advanced contract management tools. AI technologies can analyze contract documents, extract key clauses and organize large contract repositories into a centralized repository of structured and searchable data. These abilities enable procurement professionals to quickly locate critical information, effectively monitor contract performance and identify potential risks or opportunities that might otherwise be overlooked.

Procurement expert Daniel Barnes explained the value of using AI in contract management on a recent episode of IBM’s AI in Action: “Getting really good data can be a challenge…(I’ve used) AI to extract as much data from documentation—mainly contracts, because my view is that (good) contracts really contain all the information you need. Data about the supplier, the relationship, the commercials, the SLAs, KPIs. Just about everything you need you can find in a contract.”¹

By improving visibility and reducing manual workload, AI enables teams to focus more on strategic decision-making and supplier performance. It does not replace human judgement but strengthens the ability of procurement professionals to oversee supplier agreements with greater insight and control.

Contracts sit at the center of how organizations control cost and support risk management efforts while helping ensure supplier performance. These agreements define what is expected from suppliers and what the organization must deliver in return, making them a critical component of an overall procurement strategy. When contract management is inefficient or lacks visibility, it becomes difficult to ensure that these expectations are consistently met.

Traditional approaches to contract management often struggle to keep pace with the scale and complexity of modern procurement. Organizations can manage hundreds or thousands of contracts across different suppliers, regions and categories. When information is stored in disconnected systems or handled manually, it becomes harder to monitor performance and respond to changes. As a result, procurement teams can spend more time searching for information and resolving issues than proactively managing supplier relationships.

AI makes contract data more accessible, structured and usable. Instead of treating contracts as static documents, AI helps transform them into a source of actionable insight. This transformation allows procurement teams to better understand what is happening across their contract portfolio and improve supplier relationship management by quickly responding to obligations and opportunities.

As procurement becomes more strategic, it’s increasingly important to manage contracts with clarity and consistency. AI enables a more informed and proactive approach to contract oversight. It strengthens the overall contract management strategy by helping organizations move away from reactive, manual processes toward a more controlled and data-driven way of managing supplier agreements.

Contract management in procurement typically spans several key stages, including drafting, contract negotiation, execution, ongoing monitoring and renewal or amendment. Each stage involves multiple workflows that require coordination and careful review. AI enhances these stages by improving how specific processes are carried out, making them more efficient, consistent and easier to manage at scale.

• Creating drafts: Generative AI, which is designed to produce new content based on existing data, can create initial contract drafts by using templates, prior agreements and predefined rules. A procurement team can generate a supplier agreement tailored to a specific category or region without starting from scratch, reducing drafting time and improving consistency.
• Standardizing language: Natural language processing (NLP), a type of AI that understands and analyzes human language, can compare contract text against approved clause playbooks. This helps identify deviations from approved terms.
• Reviewing clauses and identifying risks: NLP can also analyze contract language to flag missing clauses, ambiguous wording or potentially risky terms. For instance, it can detect the absence of a termination clause or highlight unusually broad indemnification language.
  Generative AI can then suggest clearer or more compliant language. A recent IBM study found that executives rank risk management and resilience as the top sustainability use case for generative AI, and 64% say that gen AI will be important for their sustainability agenda overall.²
• Interacting with contracts: Conversational AI, which allows users to interact with systems by using natural language, enables procurement professionals to ask questions about contracts and receive clear answers. For example, a user might ask, “What are the key risks in this agreement?” or “Summarize the payment terms,” making contract review faster and more accessible.

• Orchestrating workflows: Agentic AI can autonomously take actions and manage tasks based on defined goals. Gartner predicts that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024.³
  In procurement, agentic AI helps automate the routing of contracts through workflows and helps ensure that each step is completed. An AI agent can automatically send a contract to legal, finance and procurement stakeholders in the correct sequence without manual coordination.
  
• Tracking approvals: AI systems provide real-time visibility into approval status, showing where contracts are in the process and who needs to act. This visibility helps align approvals with related documents such as purchase orders, reduces the need for manual follow-ups and helps prevent delays.
  
• Identifying bottlenecks: Machine learning, which identifies patterns in data over time, can analyze past approval cycles to detect recurring delays. For instance, it can reveal that contracts consistently stall at a particular approval step, allowing organizations to adjust workflows or resources in accordance.

• Extracting obligations: NLP can extract key obligations and deliverables from contract text and convert them into structured data, including key performance indicators (KPIs), performance metrics and service level agreement (SLA) metrics. For example, it can identify SLAs or reporting requirements and turn them into trackable items for performance monitoring.
  
• Tracking milestones: Agentic AI can continuously monitor contract timelines and trigger alerts and notifications when important dates are approaching. For instance, it can notify a procurement manager 30 days before a key delivery milestone or renewal deadline.
  
• Monitoring compliance: Machine learning models can analyze contract and performance data to identify patterns that can indicate non-compliance. Repeated delays in supplier deliveries, for example, might signal a risk of breaching service levels agreed to in the contract.
  
• Improving contract visibility: AI systems organize contract data into searchable formats and visible dashboards, making it easier to locate and understand key information. A procurement team can quickly find all contracts with specific pricing terms or identify agreements nearing their expiration.

• Tracking renewals: AI systems can automatically identify upcoming contract expirations and initiate review workflows. To avoid lapses, for example, it can trigger a renewal review process 60 or 90 days before a contract ends.
  
• Evaluating performance: AI and machine learning can analyze historical supplier performance to identify patterns that might indicate non-compliance. For example, repeated delays in supplier deliveries might signal a risk of breaching contractual service levels.
  
• Drafting amendments: Generative AI can assist in drafting updated contract terms based on prior agreements and new requirements. It can suggest revised pricing clauses or updated service levels during renegotiation.
  
• Identifying opportunities: AI can surface insights from past contracts to highlight opportunities for improvement. It can identify patterns where better terms were achieved with similar suppliers, for example, helping procurement teams negotiate more effectively.

AI enables procurement teams to manage agreements more effectively at scale, which drives cost savings and supports more cost-effective operations. Other benefits include:

• Better decision-making: AI provides structured data and insights that support more informed contract and supplier decisions.
• Enhanced risk identification: AI can quickly analyze contract language and performance data to surface potential risks that might otherwise be missed, supporting risk mitigation.
• Faster contract creation and execution: By automating drafting, review and approval workflows, AI reduces the time required to move contracts from creation to signature.
• Fewer delays and bottlenecks: AI improves workflow coordination and visibility, helping reduce approval delays and process inefficiencies.
• Improved visibility into contract data: AI makes it easier to access and understand key contract terms and timelines across large portfolios.
• Reduced manual workload: Automating repetitive tasks allows procurement and legal teams to focus on more strategic work.
• Scalability across contract portfolios: AI allows organizations to manage a growing number of contracts without a proportional increase in manual effort.
• Stronger compliance and obligation tracking: AI enables more reliable tracking of contractual obligations, helping organizations maintain strong contract compliance and stay aligned with agreed terms and other compliance requirements.

Implementing AI requires alignment with established contract management best practices. Without careful planning and oversight, organizations can face risks that limit the effectiveness of AI or create new concerns.

Change management and user adoption: Procurement and legal teams might be hesitant to adopt AI tools, especially if they disrupt established workflows. Training and resources such as internal upskilling sessions or webinars are essential to help ensure that AI is successfully adopted.

Data quality and availability issues: AI systems rely on accurate and well-structured data to perform effectively. If contract data is incomplete, inconsistent or stored in fragmented systems, AI outputs might be unreliable. To address this issue, organizations should invest in data cleansing, standardization and centralized data management before scaling AI initiatives.

Data security and confidentiality concerns: Contracts often contain sensitive commercial and legal information. Using AI systems, particularly systems that rely on external models or cloud services, raises concerns about data protection and access control. Organizations should implement strong data governance policies, encryption and access controls and carefully evaluate vendors for compliance with security standards.

Integration with existing systems: Incorporating AI into existing procurement and contract management systems can be complex. Poor integration can lead to workflow disruptions or duplicated efforts. A phased implementation and the use of APIs or integration platforms can help ensure smoother adoption.

Lack of standardization in contracts: Variations in contract language and formats can make it difficult for AI models to accurately interpret and analyze documents, especially in organizations without standardized templates. Establish standardized contract templates and clause libraries to improve consistency and enhance AI performance.

Overreliance on automation: Relying too heavily on AI can lead to reduced human oversight. Contract management still requires professional judgment, especially for negotiations and risk assessment. Maintain human oversight with clear review checkpoints to help ensure that critical decisions are properly evaluated.