A contract is never one document. It is a collection: the main agreement, exhibits, amendments, terms, clauses and obligations, often negotiated over months by several departments. Contracts also sit at the center of the source-to-pay process, linking sourcing, onboarding, purchasing and payment. Much of a company's commercial value is written into them.
That is why contract management touches more teams than almost any other process: legal, finance, sales, IT, HR and procurement all depend on it. This article, based on an Aavenir session on AI in contract lifecycle management and refreshed for 2026, looks at the challenges contract managers raise most often and five ways AI helps solve them.
What contract management challenges do teams face?
Contract managers describe a consistent set of problems:
- Global coordination: stakeholders in different regions and time zones slow every review.
- Back and forth: each stage, from request to renewal, involves repeated emails and lost time.
- No end to end view: systems that do not connect leave nobody with the full picture.
- Manual steps: copying data and chasing approvals cause delays, mistakes and missed opportunities.
- Inflexible tools: rigid systems limit transparency and control.
- Unclear ownership: without notifications, nobody knows who owns a term, right or obligation.
Underneath these are questions many contract managers struggle with day to day: how to assess risk in a draft, what to do with a counterparty's redlines, why templates matter and how best to assemble a contract. Most of the trouble traces back to three causes: contract volume, poor connections between systems and too much manual work.
How does AI change contract management?
AI keeps contracts working in the business rather than sitting unattended in a repository. It reads contract language, finds the risks and opportunities buried in it and points people to the parts that need attention. Users can compare dates, terms and clauses across contracts to find similarities or deviations, without reading every page.
How much value AI delivers depends on how far an organization has come. A simple three stage model helps: extract data from contracts, use it to guide authoring and review, then manage obligations and compliance after signature. See AI in contract lifecycle management for the full model.
What are five practical uses of AI in contracts?
1. Terms
AI extracts key metadata such as parties, dates, values, renewal and termination terms from contracts in many formats, including scanned images and PDFs. Legacy contracts and third party paper become searchable data, review time falls and renewals stop slipping.
2. Clauses
AI suggests clauses from your library and your best negotiated agreements while a contract is drafted, and flags language likely to create risk with an approved alternative. Search goes beyond keywords to intent, so users find clauses by what they mean.
3. Obligations
AI extracts obligations and, once approved, assigns them to owners with notifications, so commitments are met and compliance is not missed. See Obligationflow.
4. Deviations
Terms, clauses and obligations can be extracted, checked and compared side by side with your standards, so reviewers see at once where a contract departs from policy or is missing a clause.
5. Visualizations
Portfolio views show the most negotiated clauses, the terms used most often, common deviations and missing clauses. Legal and procurement leaders see where their playbook is under pressure and where templates need to change.
What does asking a contract a question look like?
The most popular capability in the session was the ability to ask a contract, or the whole repository, a question. Instead of opening dozens of files to check whether agreements include a force majeure clause, a user asks, and the system lists the contracts that do, highlights the clause and notes any required action.
This capability was introduced as iAsk and is now part of Avy. For questions across the full portfolio, such as which supplier agreements allow price increases this year, teams use Aavenir Atlas. Answers link to the source text so a person can verify them.
How do these uses map to the challenges?
| Challenge | AI capability that helps |
|---|---|
| Too many contracts to read | Term extraction and portfolio questions |
| Back and forth on drafts | Clause suggestions and deviation flags |
| Missed obligations | Obligation extraction with owners and alerts |
| No end to end view | Portfolio visualizations across contracts |
| Unclear risk in third party paper | Side by side comparison with your playbook |
AI does not fix process on its own. Teams still need clear templates, a written playbook and agreed owners. What AI does is remove the reading and searching that keep people from applying them.
Where should you start?
Assess where you are today, both before award and after it. Which contracts are in a system, which are in folders, whether you have a clause library and whether obligations have owners. That tells you whether to start with extraction, guided review or obligations. Aavenir runs contract and obligation maturity assessments with customers as part of a demo. See Contractflow.
The bottom line
Contract management challenges come from volume, disconnected systems and manual work. AI helps with each one, by extracting terms, suggesting clauses, tracking obligations, flagging deviations and answering questions across the portfolio, while people keep the decisions.