Future of Contracting and AI Readiness – The Ultimate Playbook

RazorSign

8 minutes read

The legal department was often seen as the final stop. A contract would come through, and legal would step in to review, revise, and eventually approve. Necessary, but rarely seen as strategic.

That perception is changing.

Across industries, in-house legal teams are redefining their role. They are moving beyond execution and becoming more closely involved in shaping business strategy, managing risk proactively, and influencing commercial outcomes.

AI-assisted tools can help legal teams reduce time spent on manual work by surfacing patterns, highlighting potential deviations from standard language or expected patterns, and organizing large volumes of contract data. These tools are helpful, but they are not a substitute for legal judgment.

That is where RazorSign fits in. RazorSign includes AI-assisted capabilities designed to support legal workflows, not automate decisions. These capabilities can help surface common contract risk indicators and organize due diligence inputs, while keeping review and decision-making with legal teams. All AI-assisted activity is guardrail-first, with auditability and control remaining with your team. RazorSign takes a different approach. It is designed specifically for in-house legal teams operating in complex, multi-department contract environments. The focus is on enhancing your capabilities with configurable and policy-driven AI support, structured workflows, and practical safeguards that match the way legal teams think and work.

Request a demo and see how RazorSign supports responsible AI adoption through structured workflows, oversight, and governance.

How to Build AI Fluency Across Legal Operations

Here’s how legal teams can start using AI with confidence by understanding its strengths, limits, and where human input still matters.

Understand AI Capabilities Without Technical Overload You don’t need to become a tech expert to start using AI in legal operations. What matters is understanding what AI tools are available and how they can actually help with your daily work. AI is already being used to:

Search and summarize contracts
Highlight changes between versions
Assist with importing and structuring data from legacy systems
Support approval workflows by surfacing key terms for review

The goal is to know what AI can do, where it fits, and how it saves you time. You’re not expected to build or train these tools. Just be familiar enough to use them with confidence.

Apply Human Oversight To Ensure Contextual Accuracy

AI can process large amounts of information quickly, but it still lacks context. That’s why human oversight is important. For example, AI might suggest a contract clause that seems fine at first glance. But you might know something it doesn’t, like a specific vendor preference or an internal policy.

That’s where your judgment comes in. Review what AI produces, make adjustments when needed, and make sure the results align with your business context. Think of AI as an assistant that operates within defined workflows and always requires final human review.

Recognize The Boundaries Of AI Support In Contracts

AI is helpful for repetitive, low-risk tasks. But it’s not ready to handle everything.

Use it for tasks such as assisting with standard agreements, supporting high-volume NDA workflows, or summarizing contract terms for review. These are high-volume tasks that follow clear patterns.

For more complex scenarios, like renegotiating terms with a key supplier during a crisis, human involvement is still essential. AI can help with research or analysis, but it won’t replace strategic thinking or relationship management.

Ask yourself:

Is this task simple or complex?
How often does it happen?
What could go wrong if it’s handled incorrectly?

Use those answers to decide whether AI should assist or if it needs a human lead.

Steps to Identify Contracting Tasks Suitable For AI Assistance

Here’s a simple way to figure out which contract tasks are right for AI and which ones still need human oversight.

AI-assistance for Routine, Low-Risk Activities

Start with tasks that are repetitive and don’t carry much business risk. These are the best candidates for AI. Examples include:

Extracting metadata from contracts
Flagging missing clauses
Summarizing standard provisions
Comparing versions of common agreements

These activities happen often, follow clear patterns, and don’t usually need complex decision-making. Using AI assistance for these tasks can save time and frees up your team for more strategic work.

Maintain Control Over High-Stakes Negotiations

Not everything should be handed over to AI. Contracts that involve large financial value, sensitive terms, or complex relationships still need human judgment. For example:

Supplier renegotiations during disruptions
Custom terms in a strategic partnership
Contracts with significant legal or compliance implication

AI can assist by providing analysis or drafting suggestions, but the final decisions and accountability should remain with experienced legal professionals.

Balance Speed With Judgment In Decision-Making

AI helps move fast, but that speed shouldn’t come at the cost of good judgment.

When using AI for contract work, make sure you:

Review AI outputs for accuracy and relevance
Consider the broader business context
Validate decisions against risk tolerance and company goals

Use AI to accelerate your work, not to replace critical thinking. A good balance ensures efficiency without compromising on quality or accountability.

Four-Factor Framework To Guide AI Implementation

Assess Complexity And Frequency Of Legal Tasks

Look at how complex a task is and how often it occurs. AI works best on tasks that are simple and happen frequently. For example:

Reviewing standard NDAs
Pulling key dates from contracts
Updating templates

On the other hand, one-off or highly complex tasks, like crisis-related renegotiations, are better handled by humans. Use complexity and frequency as your first filter.

Map Interconnectivity Across Legal And Business Functions

Contracting rarely happens in isolation. Legal, procurement, finance, and business teams are often involved.
Before applying AI, ask:

Who else needs to review or approve this?
How many systems and people are part of this workflow?

If a task is deeply connected to multiple teams, make sure your AI tools can integrate with existing processes. Otherwise, you risk delays or confusion.

Evaluate Risk And Cost Of Failure Before Delegating To AI

AI can assist, but tasks with a high cost of failure should always include human review. Make sure you have clear escalation paths for anything that looks uncertain or risky. This evaluation should also include data sensitivity, confidentiality obligations, and regulatory exposure associated with the task.

Key Contractual Risks Introduced By Agentic AI

Revisit Data Ownership, IP, And Privacy Clauses
So-called ‘agentic’ AI systems—where software initiates actions with limited human intervention—rely on business data and configured workflows to operate, which raises important questions about ownership. Contracts should clearly state who owns the input data and the AI-generated output. These considerations should be explicitly reflected in contractual data-use, confidentiality, and audit clauses.

It’s also important to address how the data is used. Can the provider use your data to improve its models? Will that data be exposed to other clients? Privacy, IP rights, and data use policies all need to be updated to reflect these new dynamics.

Define Accountability In AI-Driven Workflows

As AI takes on more responsibility, the line between human and machine accountability starts to blur. If something goes wrong, who’s liable? Contracts should clarify who is responsible for each part of the workflow. This includes defining roles for the provider, internal teams, and any third parties involved. Exception handling and issue resolution paths also need to be clearly outlined.

Rethink Commercial Constructs And Outcome-Based Models

As AI supports execution and analysis, contracts may increasingly focus on outcomes, not just activities or inputs. That means defining what success looks like in measurable terms.

Traditional SLAs might not be enough. Contracts should cover AI-specific metrics like system uptime, accuracy, and performance. Also, watch out for lock-in clauses and ensure there’s a plan for what happens after the deal ends.

SLAs And Performance Metrics For AI Systems

Introduce AI-Specific KPIs For Output Quality
AI systems need a new kind of measurement. Traditional SLAs focused on response time or uptime aren’t enough when the system is generating, analyzing, or automating key tasks. You need KPIs that look at how relevant, consistent, and review-ready the AI’s outputs are. Whether it’s drafting clauses, reviewing contracts, or pulling data, you should be able to measure the quality of those results.

Track Proportion Of AI-assisted Steps And AI Coverage

To understand how much of the process is being handled by AI, where appropriate, track metrics such as the proportion of AI-assisted steps versus human-reviewed steps or how much of the overall workflow is automated.

AI coverage tells you where automation is working well and where human involvement is still essential. These metrics help balance efficiency with oversight and can reveal where you’re getting the most value from AI.

Monitor FTE Productivity Gains Over Contract Terms

One potential indicator of effective AI support is improved team productivity over time, alongside unchanged or improved risk outcomes. Over the course of the contract, track how AI contributes to reducing manual effort and boosting full-time equivalent (FTE) efficiency. This helps tie AI performance directly to business outcomes and makes it easier to evaluate ROI during renewals or expansions.

Conclusion

By supporting routine tasks such as redlining assistance, clause tracking, and data extraction, AI frees up legal professionals to focus on high-value work. Instead of being stuck in the weeds, teams can now advise the business, manage risk early, and support more timely, well-informed decisions.. AI-assisted insights also give legal a sharper edge. From surfacing risky terms to suggesting clause changes, legal teams can act faster and with more confidence, without compromising accuracy.

It’s about aligning legal more closely with business goals, improving governance, and becoming a true strategic partner across departments. Request a Demo to see how RazorSign helps in-house legal teams strengthen their role as strategic partners through better visibility, governance, and decision support.

What Are avthe Key Benefits of Using AI-Powered CLM Tools in 2025?

AI works best on repetitive, low-risk tasks like metadata extraction, clause comparison, and contract summarization. These tasks follow patterns and don’t require complex judgment.

How Can Legal Teams Maintain Control Over AI Outputs?

Always review AI-generated outputs with human oversight to ensure relevance and accuracy. Set clear workflows where final decisions stay with legal professionals.

What Legal Risks Should Be Considered When Using Agentic AI?

Key risks include data privacy, IP ownership, accountability, and compliance with regulatory frameworks. Contracts should be updated to address these areas clearly

How Can Organizations Prepare Their Legal Teams For AI Adoption?

Start by building AI fluency through basic training and pilot projects. Encourage a mindset shift from execution to strategic enablement supported by AI.

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