What to Look for in a CLM Based On Your Contract Management Maturity

CLM maturity reflects how consistently and effectively contract management runs end-to-end. Knowing your current maturity stage gives you the context you need to make meaningful improvements.

A CLM platform is essential for managing contracts at scale, but the platform alone doesn’t determine maturity. Two organizations using the same software can see vastly different outcomes depending on their processes, governance, data quality, and adoption.

If you measure your performance against teams that are far more advanced, everything can look broken, even when you’re making real progress. Compare yourself to teams much earlier in their journey, and you risk missing opportunities to improve or delaying changes you actually need to make.

While no two organizations manage contracts the same way, clear patterns emerge across three broad phases — crawl, walk, run — each with its own priorities, opportunities, and buying considerations. AI also fits differently depending on where you are, and layering it in too early can create more complexity than value. Whether you’re evaluating a CLM or optimizing the one you have, knowing your stage helps you focus on finding the right capabilities at the right time.

Stage one: Crawl

Your contract management program is hard-working, but with a few changes, you could ease the burden of scattered contracts and approvals. You need a dependable contracting foundation so you can put AI to work to accelerate cycles, automate busywork, and measure what is and isn’t working.

Right now, you’re probably wrestling with shared drives and legacy tools, which lead to inconsistent decisions and friction as contract volume goes up. By the end of the year, you’ll have a contracting system that runs smoothly to reduce risk and give your team time back. With clearer visibility into your contracts, you’ll be ready for the next level of AI CLM maturity.

Signs you’re in this stage

• Manual, email-based contracting processes
• Limited or no automation
• Contracts stored in shared drives or email
• Minimal visibility into contract status or data
• Legal involved in most or all contracts
• Inconsistent use of templates

Your transformation roadmap

Phase One: Organize your current contracting workflow

Timeline: Months 1–3

This phase is all about creating order and building clear processes before automation can help. You’re establishing the foundation that makes AI useful.

Look for CLMs that:

• Centralize existing contracts regardless of where they currently live
• Support bulk import and digitization, including OCR for legacy or scanned agreements
• Offer pre-built workflow templates for high-volume contract types like NDAs, MSAs, and SOWs
• Provide a customizable template library that doesn’t require engineering support
• Allow approval routing to be configured based on contract value and risk level

Phase Two: Use AI for cleanup and visibility

Timeline: Months 4–9

Your contracts are centralized and it’s time to let AI handle the tedious work of organizing, extracting, and surfacing what matters. At this stage, AI delivers the fastest wins when you focus on cleanup and visibility. Save complex automation for later once your data is cleaner and your workflows are tested.

Look for CLMs that:

• Extract key terms from contracts automatically, including parties, dates, values, renewal terms, and obligations
• Standardize file names, merge duplicates, and align metadata across contracts
• Send automated notifications for upcoming renewals and expirations
• Integrate with your CRM so contract data flows into the tools your teams already use

Phase Three: Automate simple workflows and measure progress

Timeline: Months 10–12

Once AI is handling cleanup, your focus turns to automating high-volume, low-risk work and proving value through metrics that matter.

Look for CLMs that:

• Route pre-approved contract types automatically based on predefined criteria
• Enable self-service for standardized agreements like NDAs
• Track contract cycle time from request to signature and surface bottlenecks
• Measure template adoption rates by contract type
• Support regular workflow reviews so you can adjust based on what’s working

Stage two: Walk

You’ve centralized contracts, cleaned up core workflows, and started using some AI. Now it’s time to refine what’s in place and expand your impact.

You need to connect contracting performance to the outcomes leadership cares about, like faster cycle times, lower costs, tighter compliance, and reduced value leakage. Teams at this stage usually have automated workflows in place, but efficiency gains can flatten at this point.

Teams that keep moving forward use this phase to prove impact, earn trust, and secure the support needed to scale AI and contracting excellence across the business. By year’s end, your team will be the one others turn to for advice on what works.

Signs you’re in this stage

• Automated workflows for standard contract types
• Self-service enabled for routine agreements
• Integrations with key systems (CRM, procurement, etc.)
• Business users can initiate and manage simple contracts
• Legal focuses on complex negotiations and exceptions
• Contract data captured and reportable

Your transformation roadmap

Phase One: Expand contract AI into negotiation and risk management

Timeline: Months 1–4

You’ve proven AI works for cleanup and routing. Now it’s time to use it where friction still exists: redlining, clause negotiation, and compliance tracking. At this stage, AI is most powerful when applied to repetitive, high-volume work. Focus on removing friction from everyday negotiations.

Look for CLMs that:

• Suggest alternative clause language during redlining based on your playbook and past negotiations
• Automate pre-approvals for contracts that meet predefined risk and value thresholds
• Track obligations and flag compliance deadlines before they’re missed
• Support a clause library so business users can assemble low-risk contracts without legal involvement
• Handle a broad range of contract types with standardized workflows

Phase Two: Position contracting as a strategic driver

Timeline: Months 5–8

This phase is about proving value through reporting and insights — demonstrating how contract AI accelerates work, strengthens compliance, and drives real business outcomes.

Look for CLMs that:

• Provide dashboards that track cycle time reduction, cost savings, and legal no-touch rates
• Make it easy to document and share specific wins tied to AI performance
• Support quarterly business reviews with reporting on how contracting improvements affect revenue, risk, and efficiency
• Offer playbook functionality for high-volume contract types
• Identify usage patterns that help internal champions build the case for expanded AI adoption

Phase Three: Expand what’s working

Timeline: Months 9–12

With measurable success behind you, it’s time to grow your AI ecosystem, expand integrations, and start piloting advanced use cases.

Look for CLMs that:

• Integrate with existing business systems so contract data informs decisions across the organization
• Support AI-assisted contract drafting for routine agreements
• Offer predictive analytics that use past negotiations to estimate cycle times, discount ranges, or risk patterns
• Surface and help resolve remaining friction points in your workflows
• Provide a clear path for advanced AI investments tied to measurable business outcomes

Stage three: Run

You’re ahead of the contract AI curve, with strong CLM adoption, measurable impact, and clear AI-supported use cases in place. At this stage, contracts are reliable inputs into how the business manages risk, spend, and growth. High-performing teams have low legal involvement through effective self-service, fast cycle times through AI-driven automation, and strong template control through better negotiating position and quality.

Your goal is to turn contract AI into a sustained strategic advantage. That means focusing on continuous optimization and measurement, while experimenting with advanced analytics. Historical agreements help forecast outcomes, surface emerging risks earlier, and highlight portfolio-level trends that would be difficult to see manually. By the end of this stage, contract AI is a core part of how the business operates — supporting better decisions, protecting negotiated value, and helping position the organization for continued innovation and leadership.

Signs you’re in this stage

• AI automation across all major contract types
• Predictive analytics identifying risks and opportunities
• Organization-wide adoption across departments
• CLM integrated into broader business strategy
• Continuous optimization based on data insights
• Contracts viewed as strategic assets, not just legal documents

Your transformation roadmap

Phase One: Strengthen and scale your foundation

Timeline: Months 1–3

Tighten the system you already have. Close lingering gaps, fine-tune accuracy, and make AI an everyday part of how contracts move through the business.

Look for CLMs that:

• Provide adoption and accuracy reporting across all contract types to surface where teams are still defaulting to manual processes
• Support automated handoffs between departments at scale
• Establish bi-directional data flows with CRM, ERP, and finance tools so contract changes trigger updates automatically
• Enforce data governance standards to keep contract metadata clean and reliable as volume grows

Phase Two: Deploy predictive and generative AI

Timeline: Months 4–8

Once the basics are in place, push into predictive and generative AI — forecasting outcomes, guiding negotiations, and drafting contracts faster. Focus on AI that helps you make better decisions before contracts are signed.

Look for CLMs that:

• Use past negotiations to forecast typical discounts, average cycle times, and risk patterns
• Surface early risk signals across the contract portfolio
• Support scenario planning that models how renegotiations or term adjustments would affect revenue, spend, or risk
• Automate contract creation and compliance checks end-to-end
• Provide a path to test emerging capabilities like blockchain or IoT integrations

Phase Three: Lead the field

Timeline: Months 9–12

By this stage, you’re ready to innovate on purpose. Build the structure that keeps progress going by documenting what works, testing what’s next, and setting the pace for contract AI leadership in your industry.

Look for CLMs that:

• Support living playbooks that capture repeatable patterns and institutionalize what works
• Offer early access to new AI capabilities before broader market release
• Enable experimentation with emerging tools like AI reasoning engines or advanced analytics
• Partner with your organization to share insights, present findings, and shape industry standards

Moving between stages

Crawl → Walk

Typical timeline: 6–12 months

At this stage, teams move past cleanup work and focus on making contracting run the same way every time, even as volume grows.

How teams keep moving forward:

• Turn judgment into policy. Clear rules and expectations maintain consistency and prevent value leakage across similar deals.
• Shift routine work out of legal queues. Self-service frees up legal capacity without giving up control.
• Build workflows that match how the business actually works. Contracting runs better when it mirrors your specific organization’s sales, procurement, and finance workflows.

Signs you’re on the right track:

• 20–30% reduction in legal involvement
• More predictable cycle times
• Confident self-service for standard contracts with >50% business user adoption

Don’t skip this step: Teams that rush automation without defining templates, thresholds, and ownership can stall later. AI magnifies the structure you do (or don’t) have in place.

Walk → Run

Typical timeline: 12–24 months

Here, the challenge changes. Automation is in place, but progress slows unless teams start using contract data differently. The focus shifts from moving work faster to learning from it.

How teams keep moving forward:

• Standardize decisions to reduce variance and value leakage. Consistency matters more as deal volume grows.
• Use automation to create insight, not just speed. Reporting, forecasting, and scenario analysis become differentiators.
• Align contracting metrics to business outcomes. Contract data starts informing pricing, vendor strategy, and risk posture.

Signs you’re on the right track:

• Legal involvement below 25%
• Sub-30-day cycle times for standard agreements
• Actively using contract data in planning and decision-making

Don’t stall here: Teams can plateau after quick automation wins. The next gains require analytics, AI-driven insights, and tighter alignment with leadership priorities.

What matters at every stage

Across all maturity levels, the same fundamentals separate teams that advance from those that stay where they are:

• Executive sponsorship and clear vision
• Dedicated resources (people and budget)
• Regular measurement and optimization
• Appropriate technology for your stage (don’t over-engineer)
• Change management and training

Progress isn’t linear, and maturity looks different by industry and size. But the benchmarks are clear: teams that treat contracting as an operating system — not a one-time implementation — are the ones that keep improving.

For additional benchmarks for metrics across the entire contract lifecycle, download the 2026 Contracting Benchmark Report.

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Ironclad is not a law firm, and this post does not constitute or contain legal advice. To evaluate the accuracy, sufficiency, or reliability of the ideas and guidance reflected here, or the applicability of these materials to your business, you should consult with a licensed attorney.