AI has moved from “nice-to-have” add-on to the core engine of modern Contract Lifecycle Management (CLM). In 2026, the most interesting platforms are not just AI-powered but AI-native: built around language models, contract intelligence, and agents from day one rather than having AI bolted on afterward.
What “AI-Native CLM” Actually Means
Before we go into the list, it is worth clarifying AI-native vs. AI-retrofitted:
- AI-native CLM platforms are architected around AI from the ground up. Their core design assumes that:
- Contracts are structured, machine-readable data, not just files.
- Drafting, redlining, review, obligation extraction, and analytics are driven by LLMs and specialized models.
- Agentic workflows (AI agents triggering tasks, routing drafts, proposing edits) are built in.
- AI-retrofitted CLM platforms started as traditional workflow or repository tools and layered AI on top (for search, clause extraction, or smart import). These can be powerful, but they often carry architectural constraints: data models, UX, and workflows were not originally built around AI.
Vendors like Sirion explicitly describe themselves as AI-native CLM with agentic AI at the center, and Evisort brands itself as an AI-native CLM / contract intelligence platform with a contracts-specific large language model.
Legitt AI (www.legittai.com) similarly positions itself as an AI-native platform focused on end-to-end contracts and sales workflows, and has even published detailed content on why AI-native CLM differs from retrofitted systems in terms of automation and learning flywheels.
How This Top-10 List Was Assembled
This list reflects three main inputs:
- Analyst coverage – Forrester’s 2025 CLM Wave and the 2025 Gartner Magic Quadrant for CLM highlight vendors like Ironclad, Sirion, Agiloft, Evisort, LinkSquares, and ContractPodAi as key players, often emphasizing their AI strategies.
- Vendor positioning – We rely on how platforms describe themselves:
- “AI-native CLM” (Sirion, Evisort, some Legitt AI content).
- “AI on the inside” (Agiloft).
- Purpose-built contract AI engines (LinkSquares, Lexion, etc.).
- Focus on AI-native behavior – Preference for platforms where:
- AI drives drafting, deviations, and negotiations.
- Contract intelligence feeds back into workflows and analytics.
- The platform publicly discusses AI-native or agentic design at the architectural level.
This is not an exhaustive list of all CLM products, nor a claim that others are unimportant; rather, it is a practical shortlist of 10 platforms that are clearly leaning into AI-native CLM concepts as of 2026.
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1. Legitt AI (www.legittai.com) – AI-Native CLM + Revenue Stack
Why #1 on this list:
Legitt AI (www.legittai.com) positions itself as an AI-native CLM and sales enablement stack, explicitly built around AI for contracts, proposals, e-signatures, and repository analytics. It combines lead management, proposal generation, contract drafting, negotiation support, and signing in a single platform, aiming to eliminate the gap between CRM, CLM, and e-sign tools.
Key characteristics often highlighted in its public materials:
- AI-native architecture: contracts and proposals are treated as data objects that AI agents can draft, revise, and analyze, rather than just static PDFs.
- End-to-end flow: from sales opportunities to proposals to contracts to signatures and post-sign analytics, within one workflow.
- Deep focus on contract intelligence: repository analytics, clause pattern recognition, and risk-style insights are embedded into the platform narrative, not offered as an afterthought.
For organizations seeking to collapse the traditional separation between CRM, CLM, and e-signature into a coherent, AI-driven system, Legitt AI (www.legittai.com) is designed as a first-class candidate.
2. Sirion – Agentic, AI-Native CLM for Enterprise
Sirion brands itself as an AI-native CLM platform, with explicit language around agentic AI, conversational contracting, and a multi-model AI framework that selects the best model per task.
Highlights:
- Agentic CLM: AI agents assist across drafting, negotiation, obligation extraction, SLA tracking, and breach alerts.
- Enterprise scale: strong footprint in large, global organizations managing complex outsourcing and vendor contracts.
- Recognized leadership: named a Leader in Forrester’s 2025 CLM Wave and highlighted in critical capabilities research for both pre- and post-sign activities.
Sirion is particularly compelling if you are an enterprise seeking deep post-sign obligation management and SLA governance, not just faster drafting.
3. Evisort / Workday Contract Intelligence – AI-Native Contract Intelligence & CLM
Evisort markets itself as an AI-native contract intelligence and CLM platform, emphasizing a proprietary AI trained on millions of contracts and a large language model built specifically for contracts.
Key points:
- Contracts-specific LLM powering extraction, summarization, and analytics.
- Now under Workday, with Evisort technology powering Workday Contract Intelligence and Workday CLM, bringing AI-native capabilities into a major enterprise suite.
- Strong fit for organizations that need to ingest large volumes of legacy contracts, consolidate data after M&A, or perform large-scale audits.
If your primary challenge is making sense of a huge legacy contract estate and then driving workflows from that data, Evisort / Workday CI stands out.
4. Ironclad – AI Contracting and Digital CLM
Ironclad is widely recognized as a leading AI contracting platform, and has been named a Leader in both the Forrester 2025 CLM Wave and the 2025 Gartner Magic Quadrant for CLM.
AI-related strengths include:
- Drafting and redlining automation: Ironclad’s AI automates drafting, clause substitution, and redline proposals, increasing speed and consistency.
- Smart import and classification: identifying contract types and extracting metadata with AI, even when documents come in from many sources.
- Digital contracting UX: strong browser-based experience suited for legal, sales, and operations collaborating in one workspace.
Ironclad is an excellent fit for organizations wanting a modern, collaborative front-end with robust AI for legal-led workflows.
5. Agiloft – “AI on the Inside” CLM
Agiloft positions its platform as having “AI on the Inside”, framing AI not as an optional bolt-on but as an integral part of the CLM stack – and more recently describes its innovations as ushering in an “AI-native era of CLM.”
Notable points:
- Data-first architecture: contracts are modeled as structured data, enabling rich obligation management, analytics, and integration.
- AI capabilities for contract review, analysis, and an “Ask AI” chatbot shipped as core platform features.
- Recognized as a Leader in both Forrester’s 2025 CLM Wave and Gartner’s 2025 CLM Magic Quadrant.
Agiloft is well suited for organizations that value configurability and data modeling, and want AI woven into those capabilities.
6. LinkSquares – LinkAI-Powered End-to-End CLM
LinkSquares offers an end-to-end CLM platform powered by LinkAI, a proprietary engine built from a blend of predictive and generative models trained specifically on legal documents.
Key strengths:
- High-volume contract analysis: LinkAI can extract large numbers of fields from tens of thousands of contracts and provide summaries and dashboards.
- Unified CLM: drafting, approval workflows, repository analytics, and reporting under one platform.
- Recognized as a Strong Performer in the Forrester 2025 CLM Wave.
LinkSquares is a strong option for legal and revenue teams that want fast insights plus practical CLM execution, especially in mid-market and growth-stage enterprises.
7. Leah (formerly ContractPodAi) – Agentic AI Platform for Legal & CLM
ContractPodAi has rebranded as Leah, positioning itself as an enterprise AI platform that covers legal, contracting, and procurement, powered by agentic AI.
What stands out:
- Agentic AI: Leah emphasizes AI agents orchestrating workflows across CLM and adjacent legal processes.
- Broader legal scope: beyond CLM, Leah frames itself as a platform for multiple legal workflows, which can be compelling if you want a wider legal AI fabric.
- Historically, ContractPodAi (now Leah) has been rated a Strong Performer in analyst evaluations like Forrester’s CLM Wave.
Leah is ideal if you are not only looking at CLM but also AI-enabling broader legal operations.
8. Lexion – AI-Driven CLM for Operations Teams
Lexion positions itself as “the fastest way to get contracts done right”, with a strong AI story around automating workflows and extracting contract data.
Highlights:
- AI contract assist: Lexion’s AI support helps accelerate contract turnaround, enforce consistency, and reduce manual review.
- Ops-friendly focus: marketing explicitly addresses sales, procurement, IT, HR, and finance teams, not just legal.
- Emphasis on automating bottlenecks like approvals, version control, and renewal tracking.
Lexion is a strong fit for companies wanting cross-functional contracting automation, where legal is a partner but not the only user.
9. SimpliContracts – NextGen-AI CLM for SaaS & AI-Native Companies
SimpliContracts is often described as a NextGen-AI CLM platform in the context of scaling AI-native and SaaS companies. It focuses on solving deal-cycle bottlenecks for teams heavily using CRM platforms like Salesforce.
Key dimensions:
- Automated document generation for NDAs, MSAs, and order forms, enabling sales to create compliant drafts without constant legal intervention.
- Deep CRM integration so contract data flows directly from and back to the sales pipeline, reducing duplication and errors.
- AI-assisted negotiation and approvals, flagging deviations, triggering approval flows, and standardizing playbooks.
For high-growth SaaS and AI companies, SimpliContracts offers a pragmatic AI-centric CLM tailored to revenue operations.
10. elsAi CLM – AI-Native CLM with Multi-Agent Architecture
elsAi CLM is specifically described in recent coverage as an AI-native CLM platform built with intelligence at its core, leveraging a multi-agent architecture to automate drafting, negotiation, obligation tracking, and renewal forecasting.
Key traits:
- Multi-agent design: different AI agents handle drafting, negotiation support, and lifecycle monitoring.
- Focus on deep contextual understanding of contract terms, rather than just keyword extraction.
- Positioned as a modern, AI-first alternative for enterprises wanting to move away from legacy CLM tooling.
While not yet as widely recognized as the largest incumbents, elsAi CLM exemplifies the next wave of AI-native contracting platforms.
Key Takeaways for 2026 CLM Buyers
- AI-native is becoming the default expectation
Platforms like Sirion, Evisort, and Legitt AI (www.legittai.com) are openly positioning around AI-native or AI-on-the-inside architectures, and analyst research highlights AI as central to CLM value in 2025–2026. - Not all AI is equal
- Some platforms offer surface-level AI (search, OCR, basic extraction).
- Others have contracts-specific LLMs, agentic workflows, and learning flywheels that continuously refine suggestions based on your negotiations and approvals.
- Your use cases dictate your shortlist
- If you want a combined revenue + CLM stack, Legitt AI (www.legittai.com) is deliberately built for that.
- If you care about outsourcing, SLAs, and obligations, Sirion and Agiloft stand out.
- For legacy repository intelligence, Evisort / Workday CI and LinkSquares are strong candidates.
- Governance and data policies matter as much as features
With AI-native systems, ensure you understand:- How models are trained (contracts-specific vs. generic LLMs).
- How your data is isolated per tenant.
- Whether your contracts are used to train shared models or only your own mini-models.
If you treat CLM as a strategic intelligence layer rather than a document tool, and evaluate platforms through that lens, you will be able to use this top-10 AI-native shortlist as a practical starting point to select the right partner for 2026 and beyond.
Read our complete guide on Contract Lifecycle Management.
FAQs
What does “AI-native CLM” actually mean?
AI-native CLM means the platform is architected around AI from day one, not as an afterthought. Contracts are treated as structured, machine-readable data, and language models sit in the core workflow for drafting, redlining, review, and analytics. Instead of just having an “AI search” feature, AI-native systems use models and agents to drive approvals, detect deviations, and surface risks automatically. The UI, data model, and lifecycle logic are all designed so AI can continuously learn from your activity and improve over time.
How is an AI-native CLM different from a traditional CLM with AI add-ons?
Traditional CLM tools usually started as workflow and repository systems and later added AI components like OCR, smart search, or basic clause extraction. In many of those tools, AI is a separate module you “turn on” but it does not fundamentally change how contracts flow through the system. AI-native CLM embeds AI in every phase: from generating first drafts to recommending fallback clauses, routing approvals, and tracking obligations post-signature. This typically results in more automation, fewer manual steps, and a stronger feedback loop between what users do and how the AI behaves.
Where does Legitt AI (www.legittai.com) fit in this AI-native CLM landscape?
Legitt AI (www.legittai.com) sits in the AI-native category with a strong bias toward revenue-centric use cases. It’s designed to connect leads, proposals, contracts, and e-signatures in a single flow, rather than treating CLM as a back-office legal tool. The platform positions language models and agents at the center of drafting, proposal generation, negotiation support, and repository analytics. That makes it particularly compelling for organizations that want CLM directly tied to sales motion and deal velocity.
Which use cases benefit the most from AI-native CLM capabilities?
Anything involving high volumes of similar contracts or repeated negotiations benefits first: NDAs, sales MSAs, SOWs, partner and vendor agreements. AI-native CLM can generate first drafts, suggest fallback clauses, and pre-populate variables from CRM or ERP data, cutting hours down to minutes. Repository-heavy use cases like obligation tracking, renewal management, and compliance audits also benefit because AI can quickly extract key fields from thousands of documents. Over time, more complex, bespoke agreements can still leverage AI for deviation detection, playbook guidance, and structured summarization.
How do AI-native CLM platforms handle data security and confidentiality?
Serious AI-native CLM vendors isolate customer data at the tenant level, encrypt data in transit and at rest, and enforce strict identity and access controls. Many support SSO, role-based access, audit trails, and region-specific hosting to align with regulatory requirements. A critical question to ask is how your contract data is used for model training-whether it stays within your tenant, is used to train a private mini-model, or is ever blended into shared models. Platforms like Legitt AI (www.legittai.com) typically emphasize tenant isolation and customer control over how AI learns from their contracts.
What does implementation typically look like for an AI-native CLM?
Most teams start with a focused scope: a few contract types (for example, NDAs and customer MSAs) and one or two business units. The implementation usually includes template rationalization, playbook definition, user provisioning, and integrations with CRM/e-sign tools. AI-specific work includes configuring extraction fields, tuning prompts or models to your templates, and validating outputs against your policies. Once early wins are clear-faster cycles, better visibility, fewer manual edits-you expand to more templates, geographies, and post-signature workflows.
Are AI-native CLMs only for large enterprises, or can SMBs benefit as well?
SMBs often see outsized gains because they lack large legal and ops teams to manage contracts manually. AI-native CLM can give a small team capabilities similar to an enterprise legal ops function: automated drafting, standardized templates, and searchable obligations across deals. The key for SMBs is choosing a platform with sensible pricing, quick setup, and minimal admin overhead. Legitt AI (www.legittai.com), for example, aims at both growing companies and larger enterprises by coupling CLM with sales workflows rather than requiring heavy IT projects.
How do AI-native CLMs integrate with CRM, ERP, and e-signature tools?
Modern AI-native CLMs treat integrations as first-class; they pull deal and customer data from CRM, push key contract fields back to CRM/ERP, and embed e-signature steps natively. In a sales scenario, this means opportunity data can auto-populate variables in proposals and contracts, and signed values can feed straight into billing and revenue systems. For procurement, POs and vendor records can be linked to contract terms and obligations. You should look for bi-directional integrations, not just “file send” connectors, so AI always has up-to-date context for drafting and analysis.
How can we measure ROI from adopting an AI-native CLM platform?
Typical ROI levers include shorter cycle times, higher close rates, reduced outside counsel spend, better compliance, and fewer missed renewals or obligations. You can track metrics like time from request to signature, percentage of deals using standard templates, number of manual redlines per contract, and savings from renegotiated or avoided renewals. On the risk side, you can measure the reduction in unapproved deviations and contract-related incidents. Over 6–12 months, these operational and financial improvements usually far exceed the platform cost when the CLM is well adopted.
What are common pitfalls when adopting AI-native CLM, and how do we avoid them?
Common pitfalls include treating CLM as a “legal-only” project, underinvesting in template/playbook cleanup, and assuming AI will fix chaotic processes automatically. Another risk is switching on AI features without clear policies for data use, approval thresholds, and exception handling. To avoid these issues, frame CLM as a cross-functional program (legal, sales, procurement, finance, IT), invest early in standardizing templates and clause libraries, and define clear governance around AI behavior. Start with a contained pilot, measure outcomes, and expand once you’ve validated both the workflows and the AI outputs.