Contract Lifecycle Management in 2026: AI, Automation, and What has Changed

Contract lifecycle management (CLM) is still the same eight-stage process: request, drafting, negotiation, execution, obligation management, amendments, renewals, and reporting. What has changed in 2026 is the intelligence behind every stage.

Picture a revenue operations leader walking into a board review. The first question is simple: How many contracts are due for renewal this quarter, and which ones are at risk? A few years ago, answering that meant digging through spreadsheets, shared drives, and manually updated trackers, hoping every contract had been captured correctly. Today, an AI agent has already surfaced the answer. It has been reading every contract, extracting key terms, monitoring obligations, and flagging renewal risks continuously since the agreement was signed.

That is the real shift in CLM. The lifecycle itself has not changed. The way organizations execute it has.

Three capabilities separate modern CLM platforms from the systems companies relied on just a few years ago. AI can now extract structured data from thousands of legacy contracts in days instead of months. Autonomous agents monitor obligations, trigger workflows, and surface risks without someone maintaining spreadsheets. And migrations that once stretched across six to twelve months can now be completed in weeks.

This guide is for legal operations leaders, procurement executives, revenue operations teams, and IT leaders evaluating their next CLM platform or planning a migration. It explains where CLM stands in 2026, how AI is changing contract management today, what’s new in Conga CLM, and the framework you can use to evaluate any platform before committing a budget.

On that note, let’s get started.

Contract Lifecycle Management (CLM) is the end-to-end process of creating, negotiating, executing, storing, and renewing contracts, supported by software that automates each stage, reduces risk, and gives organizations visibility into their contract portfolio.

None of these eight stages are new, but each one looks different once AI sits underneath it.

Takeaway: The eight-stage lifecycle has not changed. What changed in 2026 is that AI now runs stages 1,2,6, and 8 on its own in most modern deployments.

The stages are stable. What is not stable is the cost of running them manually, and that cost is where the urgency around CLM in 2026 actually comes from.

Before getting into what AI does, it is worth sitting with what manual contracting still costs, because the numbers are larger than most legal or procurement leaders expect. They explain why boards are suddenly paying attention to a process that used to live quietly inside legal.

Three numbers make the scale of the problem hard to ignore:

The waste traces back to how contract data is scattered across the organization:

What makes 2026 different is how fast AI adoption has caught up to that fragmentation:

Organizations that have already closed the gap are seeing it show up in hard numbers:

The key takeaway: The cost of manual contracting is now measured in trillions, and for the first time, the tools to close that gap are mature, proven, and being adopted at scale rather than piloted in isolation.

The cost of the old way is clear. What is less obvious is exactly what changed to make the new way possible, and that is worth breaking down stage by stage.

The CLM framework has not moved forward, but the intelligence sitting on top of it, and this distinction matters more.

The earliest stages of the lifecycle changed first:

Further down the lifecycle, ownership and monitoring changed just as much.

Takeaway: The CLM platform you evaluated in 2022 is a categorically different product in 2026. The contracts have not changed. The intelligence operating on them has.

That intelligence is already running inside live deployments, handling specific, repeatable jobs.

It helps to move past the idea that AI is coming to CLM and look at what it is actually doing today.

Picture an enterprise with 8,000 vendor contracts sitting in SharePoint as PDFs, all of which need to become structured data (parties, effective dates, payment terms, SLAs, renewal options, termination rights) inside Conga CLM before go-live.

AI ingests all 8,000 documents, extracts 50-plus fields per contract with a confidence score on each field, flags anything low-confidence for human review, and exports directly into CLM records. That replaces what used to be five paralegals working four to six months.

LexiShift customers have seen an 80% reduction in manual review time and a 45% improvement in data accuracy.

AI compares incoming redlines against an approved clause library and flags deviations before legal ever opens the document. KPMG found this can cut standard contract review time by up to 98%, from roughly 90 minutes down to about 2.

An AI agent watches every active contract for renewal windows at 90, 60, and 30 days out, tracks SLA milestones and payment obligations, and triggers notifications, tasks, or auto-renewals on its own. This used to be a contract manager’s spreadsheet, maintained by hand and prone to the exact kind of human error that leads to missed renewals.

An acquirer needs to review 15,000 target contracts inside a 48-hour data room window. AI processes the full set, surfaces risk clauses like change-of-control, termination, and IP assignment, and produces a structured risk report.

LexiShift processed 15,000 contracts in 48 hours for M&A due diligence. McKinsey’s research on AI-guided negotiation points to similar gains, with 10 to 15% in portfolio savings when AI is involved.

The question in 2026 is not whether AI can manage contract data, but whether your organization has a plan to migrate legacy contract data into a system where AI can actually do it.

Every one of those use cases depends on one thing going right first: getting legacy contract data out of its current mess and into a system AI can read. That is where most projects actually stall.

Here is the stat worth sitting with: the average enterprise has its contract data scattered across 24 separate systems. That single fact explains why most CLM projects stall during migration rather than during licensing or platform selection.

The stall usually comes down to three things:

AI extraction addresses all the three problems at once:

This is LexiShift’s territory. It is Forsys’s proprietary AI contract data extraction platform, built specifically for CLM migration, and it comes with a free contract analysis so buyers can upload a sample set of their own contracts and see exactly what AI extracts before committing to anything.

Takeaway: The CLM migration problem will be solved in 2026, but only with AI extraction in the loop. Manual migration is no longer a realistic path for any organization sitting on more than 500 legacy contracts.

Once the migration question is solved, the platform itself becomes the deciding factor, and Conga CLM has changed considerably in what it can do natively.

Five of the key capabilities of Conga stands out in the current release:

Takeaway: Conga CLM in 2026 operates as a revenue operations platform. It connects contracts to quotes, billing, and Agentforce agents in a way earlier versions simply could not.

Knowing what the platform can do is one thing. Knowing how to evaluate it against your own contract volume and timeline is the harder, more useful question.

If you are in the middle of a platform evaluation, these are the questions that actually separate a viable CLM implementation from one that will stall six months in.

Start with what the platform itself needs to be able to do:

Then look past the platform to the partner and the volume you are actually planning for:

Takeaway: Questions 1 and 7, AI extraction and migration support, are the two that eliminate the most CLM implementations before they even get started.

Running through that checklist usually surfaces a gap most vendors don’t volunteer: few partners can actually cover both migration and platform deployment under one roof.

LexiShift handles AI-powered contract data extraction. It processes legacy contracts at scale, extracts 50-plus fields with confidence scoring, and integrates directly with Conga CLM. A healthcare client migrated 8,000 vendor contracts in three weeks using LexiShift, compared to an estimated six months manually. In an M&A context, the same platform processed 15,000 contracts in 48 hours for due diligence. Buyers can request a free contract data sample before committing to anything.

Conga CLM implementation covers the full engagement: data migration, AI capability configuration, Salesforce integration, and obligation-workflow setup. We configure the AI features themselves rather than stopping at the forms sitting on top of them.

What separates this from a typical Conga partner engagement is that few partners handle both the migration side and the platform deployment under one roof. That single detail removes the handoff risk where legacy data and the new system fail to connect cleanly at go-live. This handoff gap is exactly where a lot of CLM projects quietly lose momentum, months after everyone assumed the hard part was already behind them.

CLM in 2026 is not a new category. It is the same eight-stage process it was in 2015. What changed is the intelligence sitting on top of it. AI extraction makes migration possible at scale. Agentic workflows make obligation tracking autonomous. Platforms like Conga CLM connect contract data to the rest of the revenue stack in a way no earlier version managed to do.

Go back to that revenue operations leader heading into her board review. The difference between having that renewal-risk answer ready before the meeting and scrambling for it during the meeting is not a matter of effort or headcount anymore. It is a matter of whether the underlying contract data was ever made readable to a system that can act on it. WorldCC puts the cost of skipping that step at 8.6% of contract value, and that number shows up in missed renewals, obligations nobody tracked, and deals that took longer to close than they should have.