3.2 hours. That's the average time legal professionals spend reviewing a single contract. For teams processing 500 agreements a year, that adds up to nearly 200 working days dedicated to contract review alone.
is the average time legal teams spend reviewing a single contract. At 500 agreements per year, that's ~200 working days lost to review alone.
Much of that review time is spent on repetitive tasks: identifying key obligations, reviewing liability clauses, simplifying legal terminologies, and flagging provisions that require escalation.
Faced with these challenges, many teams are beginning to incorporate AI into their contract review workflows. In fact, 78% of legal departments and law firms are either using, evaluating, or exploring AI for contract review.
of legal departments and law firms are using, evaluating, or exploring AI for contract review.
The need for faster contract analysis shows up in different ways across organizations:
For many organizations exploring AI-assisted contract review, ChatGPT is often the starting point. It helps summarize lengthy agreements, explain complex legal language, and even extract key terms and obligations.
However, the focus is largely on efficiency rather than automation. It means most legal teams are not looking to replace legal judgment. They want a faster way to complete a contract's first pass review so they can spend more time assessing risks, negotiating terms, and focusing on issues that require human oversight.
This article explains more about this space, explaining how ChatGPT fits into the contract review process, its limitations, and how Aline's comprehensive CLM offerings help address the gaps that AI-powered review alone cannot fill.
Yes. ChatGPT can analyze contract text, answer questions about specific provisions, identify potential issues, and suggest alternative wording. But its output should be treated as guidance and not as a final legal assessment.
Some contract risks depend on context, company policy, and legal interpretation. These situations typically require human expertise or specialized AI contract review platforms:
In these scenarios, the challenge is often determining whether the language aligns with organizational policies, regulatory requirements, and acceptable levels of risk (more than understanding what a clause says).
Aline can better support situations that general-purpose AI tools don't address. Beyond reviewing individual contracts, it helps teams manage approvals, maintain version control, and track obligations. It also keeps contract information centralized throughout the lifecycle, helping teams free up 50-75% of the time typically spent on routine legal reviews.
Looking for an AI solution built specifically for legal teams? Aline lets you draft, redline, and analyze contracts with legal-first tools. Give it a try today!
ChatGPT works when the goal is to understand, organize, or analyze information within a contract. The following are the ideal use cases:
If you're reviewing a single agreement and need a quick overview before a meeting or negotiation, ChatGPT can help identify important business terms, obligations, deadlines, and rights buried within the document.
It is particularly useful for:
Reviewing one contract is different from monitoring obligations, renewal deadlines, and commercial terms across an entire contract portfolio. The actual challenge is tracking and analyzing hundreds of agreements.
An Aline customer uploaded 700 leases and extracted every insurance provision across the portfolio in 10 minutes. But a generative AI will typically require separate prompts and reviews for each agreement.
Leases uploaded in one pass
Aline customer
To extract every insurance clause
Across the portfolio
Saved annually
Aline customer
Aline also centralizes contract data, making it easier to search, track, and manage contract information at scale without reviewing each agreement individually.
ChatGPT can help identify unusual language, broad obligations, one-sided provisions, or clauses that differ from commonly used contract language.
For example, it can flag:
These observations can give you a useful starting point before a more detailed review.
Whether a clause is acceptable often depends on internal legal standards rather than market norms. By reducing the need to manually review, search, and analyze contracts, Aline has helped teams save up to $30,000 annually. The platform reviews agreements against approved language, preferred fallback positions, and organizational requirements, helping legal and business teams apply consistent review standards across every contract.
ChatGPT can help identify provisions that need closer scrutiny and suggest follow-up questions for legal review. This is particularly useful when reviewing unfamiliar agreements or preparing for negotiations.
It can help identify questions like:
Identifying a question is only the first step. Aline helps teams route contracts through the appropriate review and approval process, ensuring legal, procurement, finance, and business stakeholders can collaborate on issues requiring input before an agreement moves forward.
Some review scenarios require legal expertise, company-specific context, or data security controls that general-purpose artificial intelligence tools cannot provide. The following are the typical limitations:
Uploading contracts to a public AI tool may create data governance concerns. This is especially applicable when agreements contain confidential business information, customer data, intellectual property, or regulated information.
These concerns can become significant, creating compliance issues in industries such as healthcare, insurance, and life sciences.
Aline is SOC 2 Type II-certified and encrypts data at rest and in transit. It also supports enterprise security features such as SSO and undergoes regular penetration testing. These controls help strengthen data handling practices and provide a more secure environment for organizations managing confidential contracts and sensitive commercial information.
Some contract risks cannot be evaluated by analyzing the text alone.
For example, check:
Aline reviews contracts against your organization's approved language and negotiation playbooks rather than relying solely on generic contract patterns.
This helps teams evaluate whether a liability cap complies with internal policy, or an exception requires escalation, or whether a proposed concession falls outside approved positions. Embedding these standards directly into the contract workflow helps teams accelerate contract cycles by 30-50%.
faster contract cycles when reviews are embedded in playbook-driven workflows. — Aline customer deployments
ChatGPT can analyze contract language in seconds, but everything comes down to the quality of your prompt. The most effective reviews start with a clear objective, targeted prompts, and a process for verifying the output. The following is the structured approach:
Before submitting a contract for analysis, define your review objective. Different review goals require different prompts. For example:
A focused objective helps ChatGPT prioritize relevant, specific clauses instead of generating a generic contract summary.
ChatGPT cannot infer the commercial context behind an agreement unless you provide it. Before requesting a review, include information that may affect how contractual terms should be interpreted and evaluated. For example:
For example: We are a B2B SaaS company reviewing a vendor MSA. Our preferred position limits liability to direct damages and requires a 30-day termination notice. Review the agreement for provisions that deviate from these requirements.
The most effective prompts tell ChatGPT how to conduct the review, what issues to evaluate, and how findings should be assessed. Without this guidance, ChatGPT lacks the context needed to apply consistent review criteria, often resulting in broad observations rather than actionable findings.
For example: Instead of prompting "Is there any risk?", say it as: Act as in-house counsel for a B2B SaaS company reviewing a vendor Master Services Agreement.
Review the contract for:
For each issue identified:
This is much stronger and more specific as it tells ChatGPT who it represents, what to review, and how to evaluate it.
Long narrative responses are difficult to validate and compare. Request information in a structured format, like the following:
| Clause | Summary | Risk Level | Reason | Recommended Action |
|---|
Structured outputs also make it easier to compare findings across multiple contracts, share results with stakeholders, and verify that key points or risks have not been overlooked.
One of the most practical uses of ChatGPT is reviewing revised drafts. Instead of manually comparing multiple versions of a contract, you can ask the model to identify changes and explain their implications.
Example prompt: Compare Version A and Version B of the limitation of liability clause. Identify every material change, explain how the allocation of risk has changed between the parties, and highlight any modifications that increase financial exposure.
ChatGPT can accelerate contract analysis, but it should not be treated as an authoritative source. Large language models can misinterpret clauses, overlook exceptions, or draw conclusions that are not fully supported by the contract text.
Before escalating findings, approving language, or making negotiation decisions, verify the output against the original agreement.
Reviewers should confirm:
Think of ChatGPT as a contract review triage tool rather than a final reviewer. It can help identify potential risk areas for further examination, but contractual decisions should always be based on the agreement itself and the organization's commercial and legal requirements.
The following prompts will help sales, procurement, and finance teams identify potential risks and review contract terms more efficiently using ChatGPT. Each example also shows the general-purpose AI's limitations and how Aline closes the gaps.
Scenario: You are reviewing a vendor MSA, customer agreement, or partner contract against your organization's preferred positions on key clauses, including indemnification, liability, termination, and data protection.
Prompt:
Act as senior commercial counsel for [Company Name], a [industry] company. Review the indemnification clause below against typical B2B SaaS contracting standards.
For each issue identified:
What it does well: Provides paralegals and operations teams with a structured first-pass review of indemnification risks. It highlights potential issues and alternative language before the agreement is escalated to legal counsel.
The limitation: ChatGPT works from market-standard language, not your organization's preferred language. That means it can recommend different revisions for similar clauses across reviews. This creates extra work for legal teams, who must compare them against real-world organizational and industry standards before sending them to the counterparty.
The Aline advantage: AI Playbooks capture your approved positions and fallback language once, then apply them consistently across every inbound review. Instead of generating generic alternatives, Aline automatically identifies deviations and recommends your preferred redlines.
Scenario: You are reviewing a vendor-drafted contract against predefined positions on liability, indemnification, termination rights, data processing, and governing law.
Prompt:
Act as in-house counsel for [Company Name], reviewing a vendor's standard MSA.
Our non-negotiable positions are:
Review the sections below and, for each deviation:
What it does well: By providing your preferred positions upfront, ChatGPT can generate a clause-by-clause deviation analysis that works as a practical first-pass review. It is considerably faster than reviewing a counterparty agreement from scratch.
The limitation: Your legal positions must be repeated in every prompt for every contract. A missed requirement, outdated position, or simple copy-paste mistake can change the outcome of the review. As contract volumes increase, maintaining and updating prompts becomes a process in itself.
The Aline advantage: AI Playbooks store your approved positions once and apply them automatically across every review. This ensures contracts are evaluated against the same standards every time, without manually restating requirements.
Scenario: You are conducting an initial review of an NDA, DPA, or MSA to confirm that all required protections are included before the agreement is escalated for legal review.
Prompt:
Act as a senior commercial lawyer reviewing this [NDA/DPA/MSA] against the required clause checklist for a mutual B2B SaaS agreement.
For each item:
Review the agreement against the checklist below.
What it does well: Helps identify missing or incomplete boilerplate provisions that can be overlooked during an initial review. The checklist format also generates structured findings that are easy to validate, share, and escalate.
The limitation: The checklist is only as good as the instructions provided. ChatGPT can assess whether common clauses are present, but it cannot identify requirements that are unique to your organization. If your legal team mandates specific language regarding data residency, disclosure restrictions, or survival periods, you should explicitly mention those requirements. Otherwise, the review is limited to general market expectations.
The Aline advantage: AI Playbooks define the required provisions for each agreement type, aligned with your organization's standards. Reviews are then performed against those requirements, automatically flagging missing or inadequate clauses rather than relying on a generic checklist.
Scenario: You are reviewing vendor contracts at quarter-end or year-end to identify upcoming renewals, auto-renewal obligations, notice deadlines, and potential pricing increases.
Prompt:
Extract renewal and termination data from this vendor contract and present the findings in the following table:
| Field | Value |
|---|---|
| Contract start date | |
| Initial term end date | |
| Renewal type (Auto-renewal / Manual renewal / Notice required) | |
| Notice period required to prevent renewal | |
| Deadline to act | |
| Price escalation on renewal (% or mechanism) | |
| Termination for convenience (Yes/No and notice period) | |
| Risk level (High / Medium / Low) | |
| Notes |
Assign risk levels using the following criteria:
If any information is ambiguous or not explicitly stated, mark it as "Unclear, legal review required."
What it does well: Converts lengthy renewal and termination provisions into a concise risk summary that procurement, revops, and business teams can quickly review. The predefined risk criteria also create a more consistent assessment process without requiring an immediate legal review.
The limitation: The analysis is limited to the contract being reviewed. For teams managing dozens or hundreds of vendor agreements, each contract must be reviewed separately. There is also no portfolio-wide visibility, automated tracking of notice deadlines, or a simple way to identify contracts with similar renewal risks across the repository.
The Aline advantage: Aline automatically extracts and tracks renewal data across the contract portfolio. This enables teams to monitor notice periods, identify renewal risks, and receive alerts before key deadlines without running separate reviews for every agreement.
Both ChatGPT and Aline can support contract review, but they solve different problems. Here's how they fit against each core contract review workflow.
| Feature | ChatGPT | Aline |
|---|---|---|
| Primary use case | Best for ad hoc reviews | Built for high-volume contract operations |
| Review standards | Requires positions to be manually included in prompts | AI Playbooks automatically apply approved standards |
| Clause deviations | Flags deviations based on prompt instructions | Automatically identifies deviations from approved positions |
| Missing clauses | Checks against user-provided checklists | Detects missing clauses against organization requirements |
| Data privacy and security | Privacy depends on the deployment model and workspace settings | Enterprise security controls, permissions, and governance |
| Redlining | Suggests alternative language | Recommends approved redlines and fallback language |
| Portfolio-wide visibility | Limited to contracts included in a review session | Search and analyze contract data across the portfolio |
| Renewal tracking | Extracts renewal terms from individual contracts | Tracks renewals, obligations, and notice periods automatically |
| Approval workflows | No built-in review or approval routing | Supports legal, procurement, finance, and business approvals |
| Contract repository | No centralized contract repository | Centralizes contracts from SharePoint, Dropbox, Box, Google Drive, and other repositories with AI-powered search. |
| Auditability | Limited review traceability | Complete review, approval, audit trails, and version history |
Aline's approach to contract review is built around legal governance, not autonomous decision-making. Contract recommendations are based on approved legal positions and negotiation playbooks defined by your organization, ensuring reviews remain aligned with established legal standards.
Legal teams review, approve, and control every contract decision, while the platform helps flag risks, deviations, and opportunities for faster resolution.
ChatGPT and purpose-built legal AI platforms leverage the same underlying AI models, including GPT, Claude, and Gemini. The difference is in how those models are applied during contract review.
ChatGPT can help identify clauses, summarize provisions, and suggest revisions based on your prompt. However, it does not have built-in knowledge of your organization's approved positions, review policies, or approval requirements.
Aline AI works within the framework established by your legal team. Reviews are guided by approved playbooks, negotiation standards, and fallback clauses specific to your organization. The platform can flag deviations, identify potential risks, recommend appropriate responses, and maintain an auditable record of reviews, approvals, and contract activity.
For legal teams, the practical difference comes down to consistency. Instead of assessing contracts against general legal knowledge, you can review them against your own standards and approval processes.
Aline maintains a 97% customer retention rate in a category where lengthy implementations, low adoption, and platform abandonment are common. It reflects a product built around the day-to-day realities of legal work, from contract review and approvals to negotiation and post-signature management.
Customer retention in a category where lengthy implementations, low adoption, and platform abandonment are common.
Aline customer base, 2026
Try Aline for free or book a 30-min demo to see how AI-driven contract review can operate within the standards, governance requirements, and workflows your team already follows.
Yes, you can use ChatGPT to review a contract by summarizing sections, spotting unclear terms, and rewording language for clarity. Just remember, it doesn't understand context the same way a human does, so it's best used alongside a legal review, especially for complex clauses like termination conditions or indemnity terms.
If you're using the free or public version of ChatGPT, avoid uploading sensitive information, client confidentiality clauses, or customers' personal data. For secure contract reviews, use platforms that offer private AI environments with legal-grade security.
Yes, it can help you understand lease terms like rent increases, the effective date, and the responsibilities of each party. However, leases often include local legal terms, so you should double-check anything important to get legal advice.
Not exactly. It can flag vague or inconsistent wording, but it doesn't fully grasp intent or spot fallback positions the way a lawyer might. It's useful for non-legal teams to get a basic sense of risk, but final reviews should still be handled by professionals.
