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19 September 2023 ·

How Artificial Intelligence (AI) is transforming enterprise contracting



We know how contracts are the backbone of all commercial relationships, but, unfortunately, because of the processes that companies have traditionally used to write, negotiate, and store contracts -- the vital data they contain is often difficult to access and leverage effectively. This exposes businesses to risk and deeply cuts into the bottom line -- until Artificial Intelligence (AI) steps into the processes to help erase this dilemma. Could your contract management be in such a position, or getting there little by little?

For example, according to Gartner1 the average in-house lawyer spends 25%-40% of his or her time on work that doesn’t even need to be done by a lawyer — at an average annual cost of $2.7 million.  Artificial intelligence (AI) is changing that, and AI’s proclivities don’t stop there.  Our research also shows that over time, AI will also: 

  • cause some jobs to disappear or change while new opportunities and growth arise; 
  • allow smarter contract management systems to unify critical business objectives within a single platform;
  • empower enterprises with rich data and actionable insights to save time and safeguard business interests;
  • replace many Contract Lifecycle Management (CLM) processes;
  • remind us that those not adopting AI will be left behind, risking total business loss.

Gartner defines contract management as “a solution and process for managing the lifecycle of contracts created and administered by or impacting the company.”

An effective contract management process is the foundation for a strong business. So much so that 25% of a company’s workforce interacts with contracts,2 connecting all parts of the enterprise – legal, sales, finance, procurement teams, risk management, IT, operations, and more.

AI for contract management brings it all together by using artificial intelligence, such as natural language processing (NLP) and machine learning (ML), to rapidly analyze, classify, extract, and author contractual information and language across a large repository of contracts.

Although risks and related considerations exist in using AI to create and manage complex agreements so do the enormous benefits of doing so.

Manual processes of CLM technology – winding road full of pitfalls

In most companies, a legal team is responsible for writing, vetting, and revising contracts based on the needs of many other departments. Although most businesses use some automated practices, they more often use manual processes to perform routine contract negotiation. In large enterprises, driving negotiations manually puts unmanageable strain on the capacity of legal teams. Typically it goes like this:

Step 1: A lawyer drafts an agreement using a previous template and basic information from the requesting stakeholder. There’s typically no built-in check here to ensure the template’s legal concepts align with current regulations or company policy.

Step 2: Multiple rounds of review and redlining arise from internal and external stakeholders. Version control becomes a nightmare.

Step 3: As Legal takes the next review, they often become the bottleneck as the number of pipeline contracts stacks up.

Step 4: The final contract is stored in a paper or siloed digital file. This restricts the visibility and monitoring of terms and obligations.

Digging deeper, challenges with traditional contracting processes become…

  • error-prone – By human nature, more errors happen when manually managing contracts simply due to most contracts' cumbersome nature and complex language.
  • time-consuming With inefficient processes, you waste precious time creating new contracts, extensively red-lining, and status tracking. Time is costly considering the associated manpower and overhead costs.
  • limited collaboration – Contract negotiation is naturally collaborative; it requires significant teamwork with other business functions impacted by the contract. Collaboration is challenging due to the siloed way that enterprises work.
  • Increased risks – Traditional contract management lacks standardized, built-in risk controls to enforce compliance. Using different systems between parties and exchanging multiple versions of documents can result in up to 88% of spreadsheets containing costly mistakes and discrepancies3.
  • Poor visibility and reporting – Post-signature -- plus the limited ability to mine contracts for insight using legacy tools like spreadsheets -- leaves room for human error and unreliable metadata. Searching for and retrieving contracts with legacy systems and offline repositories is difficult.

The AI contract revolution -- fires machine learning into CLM

The first AI models used to automate contract management were rule-based, and many solutions still use those models. Developers had to write rules instructing a bot to take specific actions when encountering a scenario or character set in a contract.

Although this helps reduce some laborious manual work, a rule-based system doesn’t learn from inputs and is only as comprehensive and error-free as the humans who wrote it.

A rule-based model is nonexhaustive, because a human will likely not write a rule for every scenario. Exceptions will arise at the end of the line, and managing those exceptions is a tedious, time-consuming, manual process.

Contract AI 2.0 – using Large Language Models (LLMs) to manage any size portfolio

When using rapid advances in generative AI, smart CLM platforms employ Large Language Models (LLMs) to transform what’s possible in managing a contract portfolio of any size.

A machine learning model works much like a human brain. For every rule it learns, there is a neural pathway. As you feed it more rules and training data, those “neurons” start forming connections. Even if you don't write a particular set of logic into the system, the system can learn and infer rules and patterns by itself over time.

When the training data is a corpus of contracts, the ideal CLM software can enable:

  • first-draft contract authoring;
  • automated legal review and initial redlining;
  • automated risk assessment;
  • seamless workflows and auto-reminders; and
  • post-signature obligation monitoring.

Real-world rewards of AI for CLM – transform contract data

The new generation of contract management software lets you seamlessly manage the enterprise contracting process. AI-driven contract extraction is the absolute core of most sophisticated CLM platforms.

With AI-enabled extraction platforms you can rapidly transform unstructured contract data into meaningful information organized according to logical taxonomies and stored in a central repository. This AI-based auto extraction can reduce the contract extraction time by 80% compared to traditional extraction models.

Extraction platforms can pull data directly from enterprise-wide contracts by integrating with file servers, Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), document management systems, and more. They then leverage Natural Language Processing (NLP) and Machine Learning (ML) to pull critical data (metadata, clauses, obligations) from the contracts. Extraction converts passive contract elements into active objects you can track throughout their lifecycle.

Other top benefits:

  • Speed up legal review  By scanning documents at scale, AI assists in legal review by highlighting changes made to a draft in minutes. This lets reviewers focus on more strategic goals.
  • Automate risk discovery – AI autonomously highlights missing or problematic clauses in both internal and external drafts and offers prescriptive clause suggestions to ensure effective risk control in negotiated agreements.
  • Streamline contract creation – Standardize and set up pre-approved templates and clauses. AI then reviews the draft contract to ensure its language matches company-preferred positions. A CLM’s AI can also leverage historical contract data to recommend more solid agreements that offer better business outcomes.
  • Digitize and centralize storage Securely and sustainably store your contracts using a centralized, cloud-based contract repository. The repository also acts as a single source of truth for all contracting data and related communication.
  • Deep analytical insights Get insights based on past contract performance to help you develop strong negotiation tactics, present appealing counteroffers, and find the best alternatives to unfavorable contract terms.

AI adoption – discover potential risks and considerations

The rapid evolution of AI technology demonstrates the enormous potential for gains in productivity and discovery across countless fields. But this also comes with potential threats. As a result, AI solutions can’t be plug-and-play — but they need to be carefully implemented, with certain conditions met -- to operate successfully and ethically.

Potential drawbacks include:

  • poor data quality – AI relies on data to function effectively. But the quality and accuracy of AI and large language models (LLMs) depend on the type of data it’s trained on. If you don’t feed the AI with complete and industry-relevant contract data, it might miss crucial legal nuances or give misinformed recommendations.
  • lengthy setup time AI solutions in complex domains like contract management require a significant initial time commitment to ensure accuracy and reliability. This involves training, testing, and optimizing your LLMs using historical contract data.
  • Ethical and privacy concerns – As with any technology that collects data, you should always consider ethical and privacy policies when working with an AI contract management system. If it is not managed properly, you risk of data breaches or misuse.
  • Missing human oversight Despite how fast technology and AI advance, humans aren’t going anywhere anytime soon. AI still requires human oversight. To maintain the agreement's integrity, ensure that your team is still closely reviewing contracts generated or processed by AI.

Considering data sources – should you always trust off-the-shelf versus proprietary data?

As stated, AI will produce a more reliable output when trained on data specific to your use case. However, it’s important to consider the purpose of the output and how that relates to the data going into the system.

For example, experts applaud off-the-shelf generative AI, such as ChatGPT, for producing nuanced, well-written copy using only brief prompts. But ChatGPT’s greatest success is also its biggest pitfall. If used for narrative writing, ChatGPT sometimes produces new text that sounds authentic and accurate but it is fictional content based on learned patterns – also known as a “hallucination.”

We’ve already seen the pitfalls come to life. In mid-2023, a judge sanctioned two lawyers4 for using ChatGPT to author a legal brief. The problem wasn’t getting AI help to generate copy more quickly — instead, the LLM created two fictional cases to argue precedent. Faced with a knowledge gap, the AI made something up.

Ultimately, companies can’t afford to base contract language or negotiation arguments on falsehoods or solely open data – AI is only as good as the data you train it on. Yet, 99% of organizations5 lack the data and technology needed to improve their contracting process.   

Here’s how you can mitigate risks:

  • Use your internal contract repository as the key source of proprietary data that helps standardize your AI’s output.
  • Add publicly-available data to your model to improve the tech’s consistency and quality.
  • Leverage multiple LLMs. Let them work together to orchestrate the best possible outcomes based on specifically-trained use cases.
  • Adopt a balanced approach, combining AI capabilities with human expertise and oversight.
  • Ensure transparency, ethics, and continuous monitoring of AI systems.

AI’s future in contracting -- how teams and humans will evolve in adapting

AI enables a form of automation, and history tells us that some jobs will disappear or radically change with automation. But as with any technological leap, new possibilities lead to new opportunities and growth. As examples:

  1. Erase the grunt work -- With traditional contract management, highly paid and trained lawyers end up doing the contracting grunt work – crunching page after page, piling up billable hours while doing very little of the specialty they were trained for. By eliminating manual CLM’s laborious, time-consuming work, legal teams can return to the strategic work that only humans with deep expertise can do.
  2. Manage the exception - With sophisticated AI and machine learning driving the management and monitoring along a contract’s lifecycle, a critical human function in contracting will be exception management. If something has happened 50 times before, the AI can learn from those 50 times and find and apply a solution to the 51st instance. But if something has happened only five times previously, a procedure to respond to it doesn’t exist.

This is where humans will thrive in the new AI-enabled future of enterprise contracts.

Why support smarter contract management?

Smarter contract management systems help unify critical business objectives within a single platform. When AI sits behind these systems, it empowers enterprises with rich data and actionable insights while saving time and safeguarding business interests.

While the world is still learning about how generative AI works,  it’s important to make sure you…

  • emphasize safety, reliability, and cost-efficiency;
  • take a multi-model LLM approach tailored for contract lifecycle management;
  • continuously test, validate, and implement new foundational models as available; and
  • use AI backed by domain-specific training and cross-verified by human experts.

But why think about all this now – what’s the point here?

It’s simple. Ultimately, those not adopting AI effectively will be left behind.

The McKinsey Global Institue6 even predicts that 30% of hours worked today will likely be automated by 2030 with generative AI.

That’s why aligning with a trustworthy CLM provider ensures that your business, procurement, and legal department remain at the forefront of contract AI capabilities.


  1. Gartner
  2. Ernst & Young
  3. MarketWatch
  4. Reuters
  5. Ernst & Young
  6. McKinsey Global Institute


Bethany is the Senior Content Marketing Manager at Sirion. She has over 10 years of experience partnering with tech companies and senior-level executives to drive business results through creative content, marketing, and PR. She specializes in B2B content strategy, SEO, and project management.


Sirion is known for bringing together category-leading innovations, unrivalled Contract Lifecycle Management expertise and a deep commitment to customer success, strives to help the world’s leading businesses contract smarter. Powered by intelligence uniquely connected across the complete contract lifecycle, Sirion’s easy-to-use, highly configurable Smarter Contracting Platform brings all enterprise teams together to author stronger agreements, manage risk and strengthen counterparty relationships.

Bethany Mullinix
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