Rethinking Contracting Technology: Why GenAI and CLM Must Be Layered, Not Substituted

Generative AI (GenAI) is rapidly reshaping legal technology, bringing both opportunity and disruption to traditional contract operations. Recent developments, such as market reaction to new GenAI plugins and rising investment in AI-driven legal tools underscore the urgency for legal and procurement leaders to define and refine their technology strategies. Yet, the rush to adopt AI frequently overlooks a fundamental truth: GenAI isn’t a wholesale replacement for contract lifecycle management (CLM); it is a powerful supplemental layer when intelligently integrated into a broader contracting ecosystem.

This article explores where GenAI fits within contracting technology, why CLM remains vital, fit-for-purpose paradigms, and how leading organizations can design modular, outcome-driven stacks that balance innovation with governance and operational predictability.

The Market Narrative: “CLM Is Dead” vs. Evolving Reality

Over the past 18–24 months, proclamations that “CLM is dead” have proliferated across legal ops discussions and industry media. Some analysts assert that GenAI and emerging autonomous agents will render traditional lifecycle systems obsolete; others argue that CLM’s functions are being unbundled into discrete, best-of-breed tools.

A more accurate interpretation, borne out by conversations with practitioners and buyers alike is that CLM is evolving, not expiring. What is changing is how CLM fits into a broader, AI-enabled contracting ecosystem. Rather than a single monolithic suite, organizations are adopting modular architectures that integrate CLM with specialized capabilities such as negotiation phase accelerators, mass contract data analytics, repository intelligence and searchability, and third-party risk and obligation management capabilities.

Leading legal technology commentary aligns with this view: the CLM market is indeed impacted, but not obliterated. Vendors are rearchitecting for an AI-first world and buyers increasingly prefer fit-for-purpose components over “one platform to rule them all.” The platforms that most seamlessly integrate the full suite become the most enterprise-viable. The organizations that stack tech most efficiently maximize the value of the component parts while preventing undue clutter and cost.

Why GenAI Isn’t a Drop-In Replacement for CLM

GenAI has compelling use cases: it can summarize complex clauses, surface deviations from approved playbooks, and accelerate negotiation cycles through suggested redlines. These capabilities represent meaningful efficiency gains at the document level.

However, drawing from industry reports and client engagements, two key limitations stand out:

1. Scalability and Cost: Token-based pricing models common among large language models (LLMs) can restrict GenAI’s effectiveness at enterprise scale—especially when analyzing large legacy portfolios of executed contracts. What works well for negotiation or draft review may be cost-prohibitive or technically constrained for broad repository analytics across thousands of agreements. This may change in the future, but is a current reality. Solve for the business outcome with effective organization and deployment of fit-for-purpose tech – e.g. avoid hammers chasing nails.
2. Governance and Workflow: CLM platforms serve as systems of record with embedded workflow, audit trails, compliance guardrails, and integration with adjacent business systems. GenAI tools, particularly standalone ones, lack these core governance features and functional controls. Process is still paramount, and within that the people, empowered and enabled. The distinction between tool and utility is key. The tool is part of the utility as leveraged by people, in the correct process or workflow. As tools advance, there’s more acceleration gained though the process and workflow, but caveat emptor on anything presented as a standalone “easy button.”

As noted in recent Gartner research, although GenAI spending continues to rise, many proof-of-concepts underdeliver—driving enterprise leaders toward AI capabilities embedded within existing software rather than standalone AI models. In other words: GenAI augments CLM, but does not supplant its foundational role. Foundational preparation work is mission-critical, as are the internal support and buy-in elements, inevitably change management is at play and successful outcomes are dependent on thoughtful enablement, adoption, and training programs. Extensibility and integration aspects further necessitate a holistic approach to the connective tissue (people, process, and tech) to guarantee success. A data lake is only as deep, wide, and accurate as the commoditized documents and data flowing in through the collection streams. The output and business acceleration gained can only be realized by layering the latest and greatest tech on top of the foundational inputs.

Where GenAI Adds Value Across the Contract Lifecycle

The value of GenAI becomes most meaningful when embedded into discrete stages of the contract lifecycle—not treated as a separate application:

• Intake & Triage – GenAI can classify incoming contract requests, identify contract types, risks, requirements, extract data, provide a summary, and automate initial routing.
• Drafting – GenAI can generate clause-level recommendations aligned to internal playbooks, suggest remediation content, and link back to CLM clause libraries.
• Negotiation – GenAI can suggest surgical and/or heavy redlines, highlight and score risks, deviations from standard content, and accelerates review cycles within end-to-end CLM workflows (origination through execution and beyond.)
• Review & Analytics – Extractive AI surfaces contract data including dates, financials, obligations, risk triggers, provision-level content, and other “metadata.”
• Post-Signature Management – GenAI and intelligent agents can identify milestones, renewals, and compliance risks, layered on top of CLM repositories that create the foundational data lake.

In this model, CLM remains the workflow engine and repository, while AI accelerates specific touchpoints around it.

A Modular and Layered Ecosystem Wins

Trying to force all contracting capabilities into a single platform can lead to complexity, rigidity, and slower adoption. Legal and procurement organizations increasingly favor modular, articulated stacks that:

• Align tools to specific contract lifecycle outcomes rather than one-size-fits-all feature sets
• Offer flexibility in procurement and upgrade pathways – extensibility and market movement must allow for growth and enhancement
• Enable tighter integration with finance, procurement, and sales systems, with articulable ROI and business case validation (measure now, deliver, measure again, value proved – make it concrete, not hypothetical)
• Support incremental modernization without wholesale replacement. Avoid the need to rip & replace, lift & shift, etc.

In practice, this means selecting best-of-breed tools across categories—repository/search, extraction/analytics, authoring/negotiation assist, obligation management—and integrating them under a coherent governance and workflow strategy. Consolidate the stack where tools deliver true value across multiple categories. Layer efficiently where gaps need to be filled and only layer where integrations and foundational work sets up success.

Governance and Expertise: Why They Matter Most

Access to AI models and contract tools is only the first step. Successful adoption depends on the often-underestimated (and too often understated) work of:

• Data hygiene: Normalizing and structuring contract data so that AI has reliable context. (Data hygiene necessitates data centralization – how deep, wide, and accurate is the data lake?)
• Model and tool selection: Choosing the right tool or AI model for classification, extraction, or retrieval tasks rather than defaulting to a single LLM. Some tools are best suited to mass-data extraction, while others are best suited to single-document analysis. Consider ecosystems, enterprise-platforms, and the mapping of tools to business outcomes to ensure success.
• Human-in-the-loop controls: Embedding subject matter expertise in architecture, design, exception handling, strategic decisions, nuanced interpretations, and process and workflow frameworks has never been more important. The power of GenAI is obvious, in the right hands, for the right purposes.
• Governance and compliance: Aligning AI usage with privacy, auditability, and enterprise risk controls. Repeatability and reliability of AI outputs is critical. Hallucinations and model drift are real concerns and need to programmatically and thoughtfully offset and de-risked.

Forrester has emphasized that while GenAI adoption is near-universal among firms, trust and governance are prerequisites for scaling beyond experimentation into production use. This rings particularly true in contracting, where risk and liability directly attach to outcomes, good or bad.

Integrating the Right Tools: A Strategy-First Approach

At its best, a contracting platform orchestration strategy connects CLM, GenAI, data governance, and upstream and downstream systems into one measurable operating model. The goal is not to layer technology arbitrarily, but to align tools to business outcomes such as:

• Faster contract cycle times
• Reduced revenue leakage
• Higher accuracy in obligation capture
• Better risk mitigation at both the document-level and portfolio level
• Scalable post-execution compliance

Where once technology procurement revolved around feature checklists, today’s legal leaders are focused on outcome architecture, designing modular ecosystems that scale with business needs, satisfy regulatory requirements, and foster growth and acceleration without incurring unnecessary risk.

The Future: Autonomous AI with Human Oversight

Agentic AI, software capable of planning and acting with limited supervision is gaining traction in enterprise workflows, including contracting. Pilot programs are underway across negotiation, obligation tracking, and compliance monitoring.

Yet the durable pattern emerging across organizations that succeed with AI is not full autonomy. It is supervised autonomy—where intelligent agents handle routine tasks, but human experts remain at the helm for exceptions, policy interpretation, and strategic judgment.

This hybrid model ensures that automation accelerates work without compromising legal oversight, risk management, or corporate governance.

Practical Steps for Legal and Procurement Leaders

If your organization is wrestling with questions about CLM’s relevance in an GenAI era, start by reframing the discussion around outcomes—not technology:

1. Define measurable objectives. Identify key performance indicators tied to contracting outcomes (cycle time, error rates, revenue impact).
2. Assess your current state. Map repositories, data quality, search times, process ownership, and integration points.
3. Adopt a modular architecture. Select tools that map to specific capabilities and integrate under a governance framework. Consolidate software where tools truly cover multiple functions.
4. Prioritize governance and expertise. Bring in domain experts to design workflows, enforce controls, and operationalize insights.
5. Pilot, measure, and scale. Start with controlled deployments, track performance, and expand where ROI is demonstrable.

Conclusion: CLM Has Evolved—Not Disappeared

Contract lifecycle management continues to be a foundational system of record and workflow engine. Generative AI is not replacing CLM, it is enhancing specific activities across the lifecycle when deployed with data discipline, governance, and expert orchestration.

The organizations generating real value are not chasing hype or adopting monolithic solutions. They are designing modular, outcome-driven ecosystems that integrate CLM, GenAI and analytics, and governance, into coherent contracting programs.

Smart contracting in 2026 and beyond will be less about platform wars and plugins and more about architecting outcomes with the right people, tools, and tactical and strategic expertise.