Artificial intelligence in contract lifecycle management: Why contract intelligence is becoming a business essential

Artificial intelligence (AI) has become one of the most discussed technologies in commercial real estate, yet many conversations remain focused on automation. The narrative is often framed around speed: faster lease abstraction, faster contract review, faster reporting. Speed matters, but it is not the problem most organizations are trying to solve.

The bigger challenge is confidence.

Lease portfolios contain thousands of obligations, financial commitments, renewal rights, and amendment histories. Real estate teams are expected to support portfolio strategy, finance teams rely on lease data for reporting and compliance, and more executives expect immediate answers to questions that once took days or weeks to investigate.

Contract data sits at the center of those decisions, yet many organizations still struggle to determine whether the information they’re working from is complete, accurate or current.

This helps explain a striking contradiction in the market. MRI Software research found that 95% of organizations use AI to some degree. However, only 5% have successfully scaled those initiatives across their respective portfolios. The adoption of AI property management tools was the first hurdle, but turning it into a reliable business feature is the next, more difficult one.

As a result, AI in contract lifecycle management (CLM) is entering a new phase. Organizations are moving away from viewing AI as a productivity tool and toward seeing it as a foundation for better governance, stronger compliance, and more informed decision-making. Trust has replaced automation as a key focus.

Contracts are creating more data than organizations can realistically manage

Lease portfolios have always generated large volumes of information, but the intricacy of that information has increased substantially.

Modern commercial leases contain layers of amendments, options, operating expense provisions, audit rights, landlord obligations and compliance requirements. Many agreements remain active for a decade or longer. Every acquisition, portfolio restructuring, and occupancy change adds another layer of complexity.

This challenge extends beyond real estate. World Commerce & Contracting found that 42% of organizations have already adopted AI in their contracting activities, while Gartner predicts that 50% of procurement contract management activities will be AI-enabled by 2027. Organizations clearly recognize that traditional approaches are struggling to keep pace with growing contract volumes and complexity.

Contract lifecycle management has traditionally focused on storing documents, managing workflows, and tracking approvals. Those capabilities remain important, but they do little to address the underlying problem: vast amounts of critical information remain trapped within contracts that are difficult to search, validate or use effectively.

The result is a paradox. Organizations often possess the data they need to make better decisions, yet extracting value from that information requires time-intensive manual effort.

Why extracting data is no longer enough

The first generation of AI in contract management focused on extraction.

The objective was simple: reduce the effort required to read contracts and capture key information. This is an important use case. AI can identify lease dates, rent schedules, operating expense clauses, termination rights, and other critical terms significantly faster than manual processes.

Yet many organizations are discovering that extraction alone solves only part of the problem.

A lease abstract is useful. A lease abstract that cannot be traced back to source documents, validated against amendments or relied upon for compliance reporting creates a different set of risks. The conversation is changing because contract data now supports activities that carry material financial and operational implications.

Lease accounting under ASC 842 provides a good example. Data quality issues are no longer simply administrative inconveniences. Errors in lease information can become reporting issues that affect financial statements, audits and compliance activities.

This is why leading organizations are placing greater emphasis on validation, transparency, and governance. The question now is whether stakeholders can trust the information that has been extracted.

The new challenge: trust

For years, organizations evaluated contract technology by asking how much time it could save.

Today, many are asking different questions.

Can the source of an extracted field be verified? Can lease audit teams understand how information was produced? Can lease administrators see which amendments override earlier provisions? Can finance teams explain the origin of a value used in reporting?

These questions all point toward the same challenge: trust.

Trust is becoming a defining characteristic of effective contract lifecycle management because contract data now influences decisions throughout the organization. Portfolio planning, lease accounting, operating expense audits, compliance activities and executive reporting all depend on information contained within contracts.

A useful contract intelligence platform does more than store documents and generate summaries. It provides visibility into how information was derived, how it was reviewed and whether it remains current.

The market is rapidly moving toward this model because organizations have learned that faster decisions only create value when the underlying information can withstand scrutiny.

AI adoption has outpaced AI maturity

The widespread adoption of AI often creates the impression that organizations have solved the implementation challenge.

The data tells a different story.

MRI Software research shows that while 95% of organizations use AI, only 5% have scaled it throughout their operations. This gap highlights an important reality: deploying AI and operationalizing AI are two very different things.

Pilots often succeed because they focus on narrow use cases with defined objectives. Scaling requires governance, training, validation processes and integration with existing workflows.

Agentic AI illustrates this challenge clearly. The technology creates significant opportunities by coordinating tasks, identifying issues and initiating actions automatically.

MRI Software has explored how agentic AI could support real estate operations through activities such as renewal management and workflow orchestration. At the same time, Gartner predicts that more than 40% of agentic AI projects may be cancelled by the end of 2027 because of escalating costs, inadequate controls or unclear business value.

Organizations are learning that successful AI programs require more than technical capability. They require operating models that define ownership, accountability and acceptable levels of risk.

Why contract data still fails to drive decisions

Many organizations have already completed lease abstraction projects. They have repositories containing thousands of contracts and millions of data points.

Despite this, decision-making often remains slower than expected.

One reason is that information frequently resides in a different location from the people responsible for acting on it. Real estate teams may manage contracts on one platform. Finance teams may rely on another. Facilities management and portfolio planning may operate elsewhere.

Every handoff introduces risk.

Information can be duplicated, manually re-entered or interpreted differently across systems. The result is a familiar problem: organizations spend time validating data rather than using it.

This issue helps explain why connected contract intelligence is attracting greater attention. Contract data creates the most value when it can support operational processes, accounting activities and strategic planning without requiring teams to continually search for information or reconcile competing records.

To solve this, the objective must be to ensure contract intelligence can actively support decisions wherever those decisions are made.

From lease abstraction to contract intelligence

Lease abstraction remains one of the most valuable applications of AI in real estate. The difference is that its role is changing.

Historically, lease abstraction was treated as a one-time administrative exercise. Today, organizations expect contract data to support reporting, compliance, auditing, and portfolio management throughout the life of a lease.

That expectation requires a different approach.

Modern contract intelligence combines AI-powered contract abstraction with validation processes, centralized repositories, AI contract review, and ongoing visibility into obligations and changes. MRI Software’s lease abstraction software reports a 75% reduction in manual data entry, 80% fewer data errors and 90% faster access to reporting, drawing on experience from more than two million extracted documents processed across more than 25 languages.

Organizations need reliable information that can support accounting requirements, identify portfolio risks, surface opportunities, and improve operational execution.

Contract intelligence represents the evolution of lease abstraction from a documentation exercise to a decision-support capability.

Governance will define the next phase of AI in CLM

The future of contract lifecycle management will not be determined solely by model performance.

Governance is becoming just as important as automation.

Frameworks such as the NIST AI Risk Management Framework and ISO/IEC 42001 are gaining attention because organizations need structures that define how AI is used, how outputs are validated, and where accountability sits.

This requirement is particularly relevant in real estate, where contract data influences legal obligations, financial reporting, and portfolio strategy.

Strong governance includes:

• Defined review thresholds for high-impact decisions.
• Clear audit trails.
• Validation standards for extracted information.
• Appropriate security controls.
• User training and accountability.

The organizations creating long-term value from AI are not necessarily those deploying the newest technology but the ones developing operating disciplines that allow technology to be trusted across the business.

The future of CLM is trusted contract intelligence

Most organizations already have an abundance of information spread across leases, amendments, repositories, and operational systems. Instead of additional data, what they really need is confidence that the information supporting decisions is accurate, current, and defensible.

That reality is reshaping the contract lifecycle management market.

The next generation of AI-powered CLM will focus less on document processing and more on creating trusted contract intelligence. Successful platforms will help teams understand not only what a contract says, but also where the information came from, how it was validated, and how it can be used across the organization.

Artificial intelligence in contract lifecycle management is no longer just a discussion about efficiency. It has become a conversation about governance, trust and decision quality. Organizations that recognize that distinction will be far better positioned to turn AI adoption into measurable business value.