Contracting as an activity has been around, ever since the start of the service economy. But despite it being a well-used practice, very few companies have mastered the art of managing contracts efficiently or effectively. According to a KPMG report, inefficient contracting leads to a loss of 5% to 40% of the value of a given deal in some cases.
The main challenge facing companies in the financial services industry is the sheer volume of contracts that they have to keep track of; these contracts often lack uniformity and are hard to organize, maintain and update on a regular basis. Manual maintenance of contracts is not only difficult but also cumbersome and prone to multiple forms of errors. Also, it poses the risk of missing important deadlines or missed scheduled follow-ups, as written in the contract and could potentially lead to expensive repercussions.
Contract management is a way to manage multifarious contracts from vendors, partners, customers, etc. so that data from these contracts can be easily identified, segregated, labeled and extracted to be used in various cases and also updated regularly.
Recent technological advances in Artificial Intelligence (AI) and Machine Learning, are now helping companies resolve many of the contracting challenges by delivering efficient contract management as a seamless automated solution.
Benefits of Using AI in the contract management lifecycle
AI can help in enhancing the searchability of the contracts including clauses, dates, notes, comments and, even metadata associated with it. The AI method used for this purpose is called natural language processing(NLP) and the extraction of metadata is done at a granular level to enable the user to search from the vast repository of contracts in an effective manner.
Example: This search function would be extremely useful for the relationship managers/chat-bots to answer any customer queries pertaining to a particular contract.
Analysis and Diagnostic Search: AI can be used to proactively identify expiry dates, renewal dates, follow-up dates or low KPI compliance, and then can be used to apply suggestive course of action or flag any alerts. Analytics can further be used to study and predict any kind of risks or non-compliance and therefore send a notification to relevant stakeholders for pending payments or negotiations.
Example: This can be effectively utilised for improving customer satisfaction as well as guide negotiations based on accessible information.
Cognitive Support: AI is highly sought for its predictive intelligence. AI’s predictive capabilities can be used to do an analysis of the existing contracts to understand contract terms & clauses. Its pattern recognition algorithms can identify points of parity, differentiations on pricing, geographic, products & services. Based on the predictive analysis, AI can provide suggestions for inclusion/exclusion of clauses, terms & conditions, etc when authoring new contracts.
Example : AI systems may automatically predict and suggest clauses pertaining to NDA (non-disclosure agreement) based on the historical contracts that have been previously processed and the events associated with it.
Dynamic Contracts: Advanced AI can be used to build an adaptive dynamic contract. Based on the past data and by taking into account external factors such as market fluctuations, currency exchange, prices, labor rate, changes in laws and regulations, etc, AI algorithms can create a contract. Such a contract would require auditing by an expert but nonetheless would reduce the effort required to generate the contract.
Example: AI can be used to assess existing contracts for making them GDPR (General Data Protection Regulation) compliant. It will insert the relevant data privacy terms and conditions into the contract and subsequently notify the concerned stakeholder about the changes in the contract, so they can be verified.
Challenges in contract management with AI-ML
The use of AI and Machine Learning for contract management is highly promising but it is also challenged by few limitations.
Machine Learning (ML) is only as effective as the training data that has been used to train the ML algorithms. Therefore, before any AI-ML application is put into practice, an exhaustive dataset of contracts must be developed and then classified, sorted, labeled, and retrieved based on the metadata. This would provide the base, as training data, for AI to build up and therefore put the ‘intelligence’ in the Contract Management process.
For the exhaustive dataset to be developed, all the contract data must be assimilated together. In many organizations, the contracts are still hard copies lying in cabinets. Approximately 10% of written agreements aren’t even traceable. Even when digitized contracts are available, for the AI machine to read these contract’s insights, they must first be in uniform contents. This not only requires scanning of all the documents but also the ability to extract the meaning of the content in the contracts.
Overcoming the challenges
In order to make the contract portfolios AI-ready, the first step is to digitize these contract documents. This can be done using OCR (optical character recognition). OCR reads the physical document as a human eye would read it and converts into digitized text which can easily be searched with ML formulas. While it may be too onerous to scan all historical contracts, this purpose can be accomplished by using a CMS (contract management software), which is capable of converting the documents into machine readable filed, thus making a significant data pool. Then AI, can be used to use this data to gain relevant insights. When AI algorithms access huge pools of data, its ability to decipher patterns and provide insights becomes much stronger. The predictive insights can be achieved by incorporating NLP (natural language processing). NLP allows contact groups to identify when contracts have deviated from defined standards. This makes the approval process, negotiation process much faster when the stakeholder is aware of the current contract version deviation from standards. NLP is also used in reporting risk based on language meaning rather than just string matching. For example, identifying those contracts which are about to expire and starting their renewal process.
Potentially, AI in contract management will change the contract management lifecycle to uplevel the strategic role of the contract managers, which would position them in a superior spot while negotiating terms of contracts. It can also help tremendously in strategic planning, risk management, supplier search, and final selections. Thus enhancing the efficiency and effectiveness of category managers. AI innovation continues to play a vital role when contract managers educate themselves and ensure that their contract processes are fully digitized and AI-ready.
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