Six ways in which Artificial Intelligence brings value to Contract Lifecycle Management
As highlighted in the The Forrester Wave™: Contract Lifecycle Management For Source-To-Contract Suites, Q1 2019, the CLM market is facing incredible growth right now. First-generation CLM technology is becoming increasingly outdated and less effective, so procurement officer and organisations are seeking innovative new CLM products in order to improve business intelligence, contract fulfilment, vendor management and negotiation. This is where AI is becoming more prevalent across the industry.
The report notes that the biggest innovations in CLM stem from the implementation of AO function to adress gaps in contract creation and management processes. Here, Forrester Wave explores six key ways in which AI can drive further value in CLM products:
- Bots for voice or IM user interaction with systems
Through the use of virtual agent AI solutions, Forrester believes that CLM vendors can create a more conversational way for users to interact with the system to initiate a contract, or review and approve a draft
- AI – enabled wizards to guide users to the right contract template and clauses
Implementing machine learning platforms and decision management tools will allows CLM vendors to create A-enabled wizards that provide a more intuitive and responsive contract guidance experience. Traditional wizards will often provide a rigid guidance process, AI will address multiple specific pain points to ensure a seamless development.
- Automated capture and metadata clause tagging of legacy or third-party contracts
The traditional manual process of importing existing contracts, or contracts from a counterparty, and applying the specific metadata tags to the relevant clauses within those contracts, can be elevated through the use of AI. AI tools will allow the CLM system to display imported contracts alongside existing clause library, allowing a more seamless process through drag and drop tagging. AI assisted natural language processing (NLP) and machine learning will also automate the metadata tagging.
- Semantic analysis AI to identify new issues in contracts and apply new metadata tags
Metatags in most CLM systems, set up in the repository, are often the only metatags that can be used. With unpredictable changes, such as regulatory or tax-law, the report notes that “the only way to find contracts that might be affected is through inefficient multivariable searches.” With the implementation of NLP and semantic analysis, CLM vendors can trace related words that are relevant to the specific issues and being able to seamlessly apply new metadata tags.
- Robotic process automation for approval of changes to standard contract language
Creators and initiators of new contracts use standard approved terms and conditions, its often the case that new or different language is needed in order to revise contract drafts in order to meet the other party’s demands or requirements throughout negotiation. With various levels of approval required, commercial terms and language will often go through various change processes. “Robotic process automation (RPA) can help the CLM system streamline this approval process by learning from prior decisions, changing the workflow based on these learnings, and providing recommendations to approvers.”
- Advanced analytics to identify new risks, opportunities, and obligations
The report explains that in the average contract, 10% of language used describes the business relationship and 90% of language assigns responsibility if things go wrong. Most language will describe risks, obligations, and opportunities that relate to the business relationship. NLP, semantic analysis and machine are but a few examples of AI tools that can find and surface contract language to address unanticipated risks create unintended obligations or suggest new ways to benefit from existing contracts.
