AI in the Chain

GenAI Contract Management & Compliance: Unlocking Efficiency and Reducing Risk

Context: Contract Lifecycle Complexity

Modern supply chains hinge on a complex web of contracts that govern supplier relationships, pricing terms, service‑level agreements and compliance obligations. Unfortunately, most organisations still rely on manual contract management. Legal teams and procurement professionals sift through hundreds of pages, often across thousands of agreements, to identify obligations and risks. This process is time‑consuming and prone to errors. According to a 2025 EY survey of chief procurement officers, 70 % of CPOs list digital transformation as their top priority, yet only 31 % have adopted artificial intelligence in contract management, leaving 69 % of organisations without a clear AI strategy. Those that embrace digital tools are already seeing game‑changing benefits: generative AI can extract key terms, highlight deviations and even generate draft clauses based on previous negotiations.

Why It Matters

Poorly managed contracts create hidden liabilities. Without clear visibility into obligations and pricing, companies risk missed deadlines, overpayments and non‑compliance fines. Manual review cycles also slow down negotiations, which increases lead times and leaves value on the table. An AI‑powered contract lifecycle management (CLM) system can surface risks earlier, ensure adherence to regulatory requirements and enable faster negotiations. With generative AI, supply‑chain leaders can free up legal and procurement teams to focus on strategy rather than chasing clauses.

Immediate Impacts and Challenges

Despite the promise of digital CLM platforms, companies still face obstacles. Contract data is often scattered across departments, stored in different formats or locked in scanned PDFs. Extracting and normalising this information requires significant data‑engineering effort. In addition, AI systems need context‑specific training: a generative model trained on generic contracts might not understand specialised terminology or local regulations. Finally, governance and ethics remain critical: misuse of generative AI could inadvertently insert biased clauses or reveal confidential data. EY found that 77 % of executives lack foundational data and AI security practices, underscoring the need to build secure digital cores.

Traditional Approaches and Their Limitations

Most organisations rely on static CLM software and human review to manage contracts. These tools provide repositories and basic search functionality, but they do not understand the meaning of the text. Searching for a term like “force majeure” may return dozens of documents that still require manual reading to understand the scope and limitations. Standard CLM systems also struggle to track compliance with regulatory changes across multiple jurisdictions. As a result, contract review cycles remain long and expensive, and organisations often discover compliance gaps only after an audit.

How Generative AI Reimagines Contract Management

Generative AI (GenAI) changes the game by enabling natural‑language processing and synthesis. It can ingest vast libraries of contracts, learn patterns and context and generate human‑readable outputs. Here’s how GenAI transforms contract management:

1. Automated Extraction and Summarisation: GenAI systems parse unstructured contract text, identify key clauses (e.g., payment terms, termination rights, liability caps) and summarise them in plain language. This makes it easier for stakeholders to understand obligations without reading every page.
2. Clause Recommendation and Drafting: By analysing historical negotiations, GenAI suggests alternative clauses or language that align with a company’s risk appetite. For instance, if a supplier repeatedly pushes for a shorter warranty period, the system can recommend compromise language based on past agreements.
3. Risk Scoring and Compliance Checks: GenAI models flag deviations from standard templates and assign risk scores. They can cross‑reference clauses against regulatory databases to ensure compliance. When new laws (like new data‑protection rules) emerge, the model highlights impacted agreements.
4. Interactive Q&A: Legal and procurement teams can ask questions like “Which suppliers have unilateral price‑increase rights?” or “Which contracts expire in the next quarter?” The AI responds with structured lists and links to the relevant documents.
5. Scenario Simulation: GenAI can run “what‑if” scenarios, such as modelling the financial impact of renegotiating payment terms or adding new clauses. This helps teams choose the best negotiation strategy.

Case Example: A Global Electronics Manufacturer

A global electronics manufacturer sources components from hundreds of suppliers across Asia and Europe. The company faced mounting compliance risks due to varying environmental regulations. By deploying a GenAI‑powered CLM system, the company ingested over 10,000 supplier contracts. The system automatically tagged sustainability clauses, identified missing compliance certifications and highlighted suppliers with outdated environmental commitments. It generated summary reports for the legal team and suggested updated language aligned with new EU directives. As a result, the manufacturer reduced contract review time by 60 % and renegotiated 30 % of its supply agreements to include stronger sustainability commitments.

Hands‑On Adoption Roadmap

Implementing a GenAI CLM solution requires careful planning. Here is a roadmap to get started:

1. Digitise and Centralise Contracts: Scan and convert all paper contracts into machine‑readable formats. Store them in a central repository with metadata such as contract type, supplier name and expiration date.
2. Clean and Structure Data: Use OCR and data‑transformation tools to extract clauses and index key fields. Normalise terminology (e.g., unify references to “delivery date” vs “shipment date”).
3. Select a GenAI Tool: Evaluate providers specialising in contract intelligence. Ensure the tool supports industry‑specific language and integrates with existing CLM and ERP systems.
4. Train the Model: Feed the GenAI system with historical contracts and negotiation outcomes. Collaborate with legal teams to label desired clauses and risk categories. Iterative training improves accuracy.
5. Pilot with a Subset: Start with a high‑value category (e.g., logistics agreements). Use the model to summarise key terms, generate draft clauses and identify risks. Compare results with manual reviews to calibrate.
6. Establish Governance: Define approval workflows and ethical guidelines. Ensure that the AI’s suggestions are reviewed by humans before finalising. Build in audit trails to track how clauses were generated.
7. Scale and Integrate: Once the pilot proves successful, extend the system across all contract categories. Integrate with procurement workflows so that GenAI insights appear during supplier onboarding and negotiation.
8. Monitor and Improve: Continuously monitor model performance, update training data and adjust risk thresholds. Collect feedback from users and refine the system accordingly.

Conclusion

Contract management sits at the heart of supply‑chain performance. Manual processes are no longer sufficient to cope with the volume and complexity of modern agreements. Generative AI offers a step change: it reads and understands text, summarises obligations, proposes better clauses and flags risks. By following a structured adoption roadmap, supply‑chain leaders can harness GenAI to accelerate negotiations, ensure compliance and unlock value. The technology will not replace human judgement, but it will empower professionals to spend their time on higher‑level strategies. As digital transformation accelerates, companies that embrace GenAI for contract management will gain a competitive edge.

References

• EY, Transforming contract management with generative AI – The report notes that generative AI streamlines contract management by summarising complex documents, suggesting alternative language based on historical negotiations and improving collaboration.
• Accenture, State of Cybersecurity Resilience 2025 – Highlights that only 36 % of tech leaders believe their security capabilities can keep pace with AI adoption and 77 % lack foundational data and AI security practices.