How In-House Legal Teams use AI to Review and Manage Contract Work at Scale

When contracts like NDAs or vendor agreements delay critical business processes, legal teams often become the visible bottleneck — even when the real issue is a manual, fragmented contract workflow.

Deprioritizing contract management creates exposure to risk, but managing contracts manually is tedious and time-consuming. Highly trained lawyers spend disproportionate time on routine tasks like reviewing, redlining, and tracking contracts across the organization while trying to manage other crucial tasks.

As AI reshapes the way people work, legal teams have the opportunity to rethink their approach to contract management. Bringing AI into the process doesn’t replace the need for human insights and judgment. Instead, it helps streamline the more routine aspects of contract management so that legal teams can work more efficiently and have more time to spend on tasks that do require their professional judgment. It allows them to deliver the speed, visibility, and consistency companies need without increasing risk.

The Realities of In-House Contract Management

From intake and drafting to negotiation, approvals, review, and post-signature needs, contract management encompasses the entire lifecycle of agreements. But every contract has unique needs and requires careful review to identify risk, manage deviations, and make sure agreements align with company policies.

Legal teams commonly use templates and playbooks to maintain consistency, but complex contracts require more than standard language. Requests come in from across the organization with competing timelines and urgency levels, making coordination as critical as analysis. And as organizations scale, contract volume often grows faster than the size of teams, leaving them needing to balance risk and business goals at a time when expectations are only increasing.

The end result is a high‑stakes environment where contract management is no longer just a legal function, but an ongoing test of a team’s ability to deliver speed and consistency while keeping risk to a minimum.

How Lawyers and Legal Operations Leaders Face Different Problems in Contract Management

While contract management touches everyone in the legal department, lawyers and legal operations leaders experience fundamentally different challenges.

In‑house lawyers often approach contract management one agreement at a time, but want to work more efficiently to improve turnaround time without sacrificing accuracy. That means spotting deviations quickly, applying playbooks without constant cross‑checking, and letting standard contracts move forward with minimal manual effort. Outsourcing routine contract work to outside counsel is rarely an ideal solution. Not only does it add cost and create more back-end review work, it creates even more delays — compounding the same problems they are trying to solve.

For legal operations leaders, success is typically measured by flow and predictability. They’re often not focused on the details of individual agreements, but on whether hundreds of contracts can move through their organization’s review process each quarter without delays. From their perspective, contract management is about designing a system that reliably handles volume and removes friction before it appears, allowing the system to absorb routine variation with minimal intervention. This includes standardizing review processes, creating playbooks lawyers can easily apply, and routing rules that send contracts to the right person without manual triage.

Transforming In-House Contract Management Through AI

AI is reshaping contract management by removing long‑standing pain points across workflows. Platforms like Harvey deliver practical value across roles, improving how contract work gets done without disrupting the ways people work.

For practicing in‑house lawyers, AI reduces drafting and review time by generating drafts from playbooks and clause libraries, flagging deviations in third‑party paper, and producing quick summaries for handoffs and status updates. With routine work easier to manage in‑house, teams can improve turnaround times and stay focused on negotiation strategies that drive the business forward.

For legal operations leaders, AI delivers consistency and scale with standardized review, direct playbook application, and cleaner routing for fewer handoffs. Portfolio‑level analysis across contract repositories adds visibility into clauses, renewals, and exposure, while faster cycle times and a higher throughput rate create clear proof of time savings that can be redirected to high-value strategic work.

What In-House AI Deployment Looks Like in Action

HubSpot’s use of Harvey offers a real-world example of how AI adoption can seamlessly integrate with existing systems and processes.

In late 2023, HubSpot’s Legal Operations team began looking for ways to streamline core legal workflows as the company's growth made their legal needs more complex. The amount of time spent on mundane contract management was significantly limiting their ability to support the deeper needs of a fast-paced company.

HubSpot formed a cross‑functional group of legal and operations stakeholders to evaluate generative AI platforms that could drive efficiency across day‑to‑day legal work. After testing multiple options, the team selected Harvey as the best fit for increasing productivity. Rather than adding new workflows, Harvey seamlessly fit into ways the team already worked.

Sarah Flint, Director of Legal Operations and Technology, explained, “Harvey’s functionality, ease of use, and outstanding user feedback made it our clear choice. We chose Harvey because it saves time, enhances productivity, and provides high‑quality, cited results for legal tasks.”

Learn more about how HubSpot’s Legal team uses AI to save time and increase strategic capacity.

Best Practices for Successful In-House AI Implementation

1. Start with a focused pilot, not a broad transformation promise

In‑house AI adoption works best when it begins with small steps. Rather than making big promises about broad transformations, successful implementation often begins with focused pilot programs aimed at addressing a few high‑friction workflows where inefficiency is already clear, such as initial drafting, routine redlining, and contract summarization. These visible early wins are an effective way to gain momentum and build a foundation for wider adoption based on real, measurable improvements, not speculation.

2. Pick contract-heavy workflows where speed and consistency matter most

When implementing AI in‑house, the most impact comes from contract‑heavy workflows where speed and consistency are critical, but inefficiency is felt the most. High‑volume use cases like first‑pass review, playbook‑based drafting, and contract summarization are repeatable, well‑scoped, and governed by clear standards.

Platforms like Harvey are designed for this kind of work. Harvey enables lawyers to apply playbooks during review, draft from standard language, and redline faster all without leaving their Word document. For larger‑scale needs, leverage review tables for bulk analysis across contract portfolios to surface risk and assess clause consistency across agreements. Focusing on these workflows allows legal teams to quickly demonstrate value early in these areas.

3. Adoption comes from fitting into lawyers’ existing workflows

Legal teams don’t want new systems to learn or separate destinations for their work — they want tools that meet them where they already are. Whether a lawyer is drafting in Word, negotiating over email, or reviewing documents in their contract systems, making sure AI solutions align with existing processes helps make adoption feel seamless rather than disruptive.

Harvey is designed around how lawyers work. When Repsol began integrating Harvey into their workflows in 2024, it proved to be a natural fit, with their team noting, “Lawyers have Harvey open on one screen and Word on the other. It’s part of how we work.” That tight integration is what drives faster contract review and sustained adoption. Today, Repsol has achieved 96% Harvey adoption across the legal department.

4. Change management matters more than most legal teams expect

For effective adoption of AI in legal teams, strong change management is essential. A platform that impresses in a demo isn't enough on its own. Lawyers need to see clear, concrete examples tied directly to their daily work, along with guidance on how to use it. Without that context and support, even the most impressive AI can feel like a one-time experiment or something that’s easy to ignore.

Harvey supports this transition through guided onboarding, practical training, and Harvey Academy, which gives legal teams hands-on examples, best practices, and ongoing education grounded in real legal workflows. This support helps teams confidently apply AI in daily legal work. At HubSpot, adoption was driven by a structured rollout and tailored training aligned to the team’s workflows and practice areas, enabling lawyers to quickly use AI for drafting, review, and research. Machado Meyer took a similarly intentional approach, pairing formal training with weekly question-and-answer sessions and live support to reinforce learning and address questions as they arose.

5. Bring IT, compliance, and privacy stakeholders in early

Introducing AI into legal workflows should be a cross‑functional initiative. Bringing IT, compliance, and privacy stakeholders into the process early allows them to assess security requirements, integration readiness, access controls, and data handling policies to create a program teams feel confident supporting and fosters a smoother path to secure, enterprise‑ready adoption.

Harvey is designed to support these priorities. The platform does not train on customer data by default and offers role-based access controls, giving customers control over data retention and sharing.

6. Measure implementation by business impact, not by usage alone

If you’re trying to measure the success of bringing AI into legal workflows, usage alone doesn’t give you the full picture. Instead, the most meaningful metrics to focus on are operational, such as:

• Faster turnaround times
• Less time spent on first-pass reviews
• More time spent on strategic work
• Adoption among teams handling the highest contract volume

Harvey customers save more than 25 hours per month on average, with a 92% monthly adoption rate. These results highlight what’s possible when AI is implemented and evaluated against outcomes that matter to the business.

Five Things to Look for in a Contract Management Tool

1. Workflow Fit

The tool should integrate directly into where lawyers already work rather than forcing a parallel workflow.

2. Playbook Execution

Can the tool apply your specific playbooks during drafting and review, or does it rely on generic market standards that don’t reflect your unique needs?

3. Use of Internal Materials

Look for the ability to work against your own templates, forms, clause libraries, and prior agreements so outputs reflect institutional knowledge, not just abstract rules.

4. Grounded, Verifiable Outputs

Lawyers need to be able to trace answers and suggestions back to source text, verify accuracy quickly, and edit results without extra manual cross‑checking.

5. Data Ownership and Governance

Data usage, retention, access controls, and training policies should be clearly defined and configurable to meet IT, privacy, and compliance requirements.

Learn more about how to choose the best legal AI platform.

How to Make the Business Case Internally

For in-house legal teams, a key challenge to AI integration isn’t winning over the lawyers who will use the tools — it’s winning over the CFOs who have the final say in the investment. Finance leaders aren’t concerned about sophisticated features or technical capabilities, they need evidence of real operational impact, such as higher throughput, lower costs, and shorter cycle times.

Start by baselining current performance. Track contract volume, average turnaround time by agreement type, outside counsel spend tied to routine work, and how lawyer time is split between tactical and strategic tasks. These metrics establish where inefficiencies currently exist and create a clear benchmark for improvement.

After deployment, measure what has changed. Look for things like faster contract cycles, increased throughput without added headcount, and time saved on first‑pass review. Frame these wins in terms of what they mean for a company’s bottom line and try to deliver a tangible ROI. When AI is tied to real outcomes, it becomes a scalable business investment rather than a technology experiment.

Get six practical steps designed to help in-house legal teams make the case for AI adoption.

Legal as a Strategic Partner, Not a Bottleneck

Integrating AI into contract management is about more than just helping contracts move faster. The larger opportunity is helping legal operate with the speed, consistency, and visibility the business needs — without compromising the judgment and risk oversight that legal provides.

When routine contract work depends on manual review, scattered intake, and fragmented tracking, legal teams are often seen as the reason business slows down. But with AI supporting high-volume tasks like summarization, first-pass review, redlining, and contract analysis, attorneys can spend less time managing repetitive work and more time advising the business on the decisions that matter most.

That shift changes how legal is perceived across the organization. Instead of acting as a reactive function brought in at the end of a process, legal becomes a more proactive partner to sales, procurement, finance, and leadership. With the right AI platform, legal teams can accelerate commercial execution, improve consistency across contract workflows, and manage risk with greater confidence.

Ready to help your legal team move faster? See Harvey in action: