Every single pound in and out of an enterprise has a contract behind its transaction. Whether it’s a single supplier contract, or a multi-billion pound acquisition outlining the ownership of intellectual property rights, there is value to be derived from each contract before, during, and after its signature. Contract Lifecycle Management (CLM) aids in this value creation by automating contract processes and surfacing unique insights during each key stage of a contract’s lifecycle, including post-signature when agreements can be utilized to identify cost savings and bolster compliance.
A decade ago, traditional CLM was little more than a repository of scanned documents. But in today’s world, true contract intelligence – CLM powered by AI – is enabling automation, efficiency, and unmatched visibility into every business relationship to support more strategic decision-making. Intelligent contracting plays a critical role in the next evolution of enterprise technology, empowering businesses to dynamically analyse contracts to ensure the intent of every agreement is realised and unlock value around revenue, savings, and risk. As businesses become more agile and tightened margins call for savvy financial pivots, the shift from traditional CLM to AI-driven contract intelligence seeks to maximise the use of every available data point within a contract.
The Shift Away from Traditional CLM
Today, increased workloads for lawyers and procurement personnel have led to increasingly negative effects on employee wellbeing, with 92 percent of legal professionals experiencing stress or burnout, according to a study by Legatics. As most businesses look to do more with less in today’s environment, this may be experienced by employees across multiple departments – including sales and finance professionals interacting with contracts. In parallel, World Commerce & Contracting data shows that contract mismanagement can cost companies around 9 percent of their annual revenue, which equates to billions in lost profits for Fortune 500 enterprises. Contract intelligence presents a unique opportunity to mitigate revenue leakage while alleviating employee workloads through greater efficiencies, ultimately safeguarding employee well-being and financial resiliency.
Although traditional CLM has been a satisfactory tool for businesses looking to speed up contract review processes, the pre-execution stage only represents a fraction of the value derived from commercial agreements. Once a contract has been signed, the relationship must be tracked to ensure all parties follow its intent. This means ensuring the right penalties were enacted, payments were made on time, and services were delivered as promised so enterprises aren’t leaving revenue on the table. In other words – was the intent of the contract when it was negotiated fully realized in the real world?
Businesses can also use contract data to gain deeper insights into whether they focus on the correct geographies, suppliers or products, equipping their c-suite – along with legal, finance, and procurement teams – with the right information to drive strategic outcomes.
The implementation of AI is making it easier for large enterprises to not only obtain contract data, but to structure and integrate that data and use it to significantly benefit their bottom-line performance. As contract intelligence analyses what has happened to contracts in the past and helps draft better contracts in the present, it also works to predict what may happen in the future.
Generative AI in Contract Management
While Large Language Models (LLMs) have ignited innovation by tapping into publicly available data, non-public information in critical systems of record, such as a business’s ERP or SCM solution, is the next frontier for generative AI innovation in enterprise use cases. Systems of record represent substantial bodies of reliable data with the power to transform entire sectors and ways of working. Combined with the data in a CLM system, which is the new system of record, they are creating an opportunity to turn commercial agreements into interactive assets that enable a step-change in efficiency and decision-making. However, there are key considerations in enterprise use cases for LLMs to ensure reliability: The AI must be coupled with the right data and safeguards to ensure it is resistant to hallucinations and inaccurate conclusions. And, security is paramount, warranting more advanced underlying platforms like Microsoft Azure OpenAI to ensure data is safeguarded and compliance is not compromised.
We’re seeing this in practice with the application of LLMs in contract management, which enables users to query agreements in a way that removes the guesswork from business decisions by cutting through legal-ese to comprehend and summarize contracts in conversational language. This creates unprecedented insight to help leaders deepen their understanding of contracting’s impact on the enterprise and ultimately distinguish mutually beneficial, revenue-driving business relationships from those that create revenue leakage and present underlying risk. Contract intelligence that goes beyond generative capabilities to incorporate LLMs is ushering in a new era of possibilities, and forward-looking organizations are turning to these solutions to multiply the value of their commercial agreements and accelerate the pace of business in the face of macro-economic challenges like inflation, supply chain disruptions, and evolving regulations.
As LLMs evolve and humanity continues on its AI journey, it is safe to assume that we will see a host of different enterprise use cases with the potential to change the world we live in. Factors like global regulation and the effects of innovation versus accountability will inevitably impact our next AI milestones. Still, generative AI’s applicability to transform business relationships through contracts, and ultimately impact the foundation of commerce, remains undeniable.
