Three Ways that Deep Learning Models Allow for Better Contract Management
Contract management is a perfect candidate for machine learning. It's all about data collection, understanding customer behavior patterns, and creating new business opportunities. Deep learning is a form of artificial intelligence used in machine learning, pattern recognition, neural networks, and predictive analytics. Poor contract management is a problem we've all experienced. From the time it's signed by both parties until the time it's enforced, a contract can be subject to several mistakes and issues that cost everyone involved. Support throughout the whole contract life cycle is critical for an organization to stay on top of its business relationships. Details might change, costs might increase, terms and renewal terms need to be taken care of. Working with contracts is not done once they are signed. This often is just the beginning of the more important long-term engagement. In this article, we will explore three ways deep learning can help streamline the contract management process.
It's no secret that deep learning has the potential to make a big difference in many industries. With the recent rise of deep learning, there have been several studies on potential industry impact and complex use cases created for many vertical markets. In our zeal to transform all business processes in digitalization initiatives, the positive impact of AI on contract management should not be overlooked.
New Contract Styles
Machine learning is revolutionizing the way we do business, and contract management is no exception. With deep learning, AI systems can identify patterns in contracts and language that have been previously unnoticed. When dealing with contracts, it's essential to be mindful of the number of things that can go wrong. With more and more enterprise companies seeing the pitfalls of traditional contracts, they break from old formats and switch to a new contract style that outlines formal relational agreements with shared vision and outcomes. AI helps navigate this process by labeling and finding appropriate structures in the often unstructured contract information, seeing patterns in customer behavior, making accurate predictions about it, and generating new outcomes.
One: Capture the facts
Data collection and fact capturing is a significant part of contract management because it helps to extract important patterns. Contracts pile up in large organizations with multiple vendors, suppliers, and customers in various service offerings and departments. It can be tough to make sure content written in clauses and legal definitions are understood and stakeholders are aware of its implications across departments. When important contractual documents and facts can become searchable, it becomes possible to properly inventory all approved language in contracts across an organization and scale this information company-wide. As well, keeping track of renewal dates and renegotiation terms is a lot more manageable.
Two: Understanding Contracts and Implications in Context
The main goal of a contract is to ensure that both parties understand what they agree to and the terms of their agreement. It is critical to have a clear, detailed, and enforceable contract in place.
Having an AI system to help with this process is helpful because it allows rapid access to simple predictions based on the data patterns. They help you focus on the exemplary aspects in context at any time, for example, renewal terms and payment obligations in the case of AI-driven solutions, the ability to find legacy contracts listed under old product or company names, and are hard to find. It also provides automatic quality assurance to verify whether the link between the metadata and the contract documents is correct. Suppose a company stores their relevant master data and contractual facts in e.g. something like a spreadsheet only. In that case, they miss out on a lot of helpful functionality. It is time to explore the improvements that can be made by investing in new technology.
Understanding the context of contracts also improves efficiency and eliminates time-consuming tasks. For example, manually pre-processing and sorting incoming contracts that arrive as electronic documents in e-mail attachments is time-intensive and prone to errors. Putting a robust AI system in place that can extract the correct data at the right time from incoming contracts and make accurate determinations for categorization and prioritization helps companies see the benefits of time savings. For example, it can help with prioritizing incoming communication.
Three: New Opportunities Through Time Savings
When routine tasks are eliminated, new opportunities arise for employees to deliver higher-value output instead of repetitive task fulfillment. Contract reviewers can streamline content more easily throughout an organization. This crosses over into many functional areas, such as legal and compliance workers tasked with contract review.
Let's look at a specific example of the functional area of sales. Suppose a salesperson is trying to upsell a customer with a new offer or get a contract renewal. In that case, they must have all of the client information in front of them at once, so they don't miss anything important before their call. They might have to go to several repositories of information to check if they have any recent complaints and read through past negotiations of prior contracts. Using an Insight Engine would help them have a 360-degree view of all of this data without checking several locations manually.
Also, intelligent pattern recognition built into the AI solution can more quickly improve accuracy. When vendors have multiple contracts with an organization and custom requirements, it becomes much easier to deliver tailored content and language on a case-by-case basis for contracts.
Deep learning is one of the most exciting fields in AI, with advances in the past few years leading to breakthroughs in many areas. Contract management is part of every well-run business's backbone, and these new AI advancements are giving this critical business process a much-needed boost. At some point, every organization will be impacted by deep learning models, causing a shift in how they manage their data. This is your opportunity to decide how you will make sure your business is ready for this change.