Technology’s development has accelerated to almost supersonic speed, creating opportunities for businesses to streamline processes, enhance efficiencies, and focus their resources on the highest-value tasks. Recent advances in generative AI, in particular, are enabling organizations to build upon their existing investments in traditional AI and machine learning (ML) to add new use cases and drive better business outcomes.
Across industries ranging from professional and financial services to agriculture and manufacturing, it’s becoming easier and easier for end users to interact with computer systems, so that stakeholders with limited technical proficiency can gain ever-greater value from technology. Jensen Huang, the CEO of Nvidia, argues that programming is no longer a future-forward skill, since AI is making it possible to use natural human language to get computers to do everything we need them to do.
That future isn’t here just yet. In fact, the number of job openings in software development has grown six percent since ChatGPT was released to the public. Expertise in building AI applications remains very much in demand, and organizations continue to need guidance in identifying how and where to apply AI to realize as much value as possible. Many are also looking for help building out the data infrastructures that make it possible to leverage generative AI effectively and securely.
In this blog, we’ll take a closer look at a few of the most exciting use cases for generative AI in the legal and financial sectors, with special attention to leveraging pre-built models and services available from public cloud providers like AWS. These tools make it easier to build and scale generative AI applications customized for an organization’s data, unique use cases, and business model.
Law firms have long been required to manage enormous volumes of paperwork. Many contracts, forms, and legal documents are still executed via hard copies, and stakeholders ranging from courts and notaries to local government agencies still send notifications by postal mail.
Because of this, most attorneys, paralegals, and administrative professionals who support them still find themselves sorting through lots and lots of paper. Sometimes the task at hand is simply locating relevant information in an archive, while other times there’s a need to take action to follow up on the contents of a recently received notice or memorandum. In practice, though, most firms find that they’ve been wasting hours of what would otherwise be billable time on paperwork-related tasks.
Here at Netrix, one of our clients, a legal services company, faced exactly this challenge. The firm was receiving more than 4,000 letters per month. Each of these mailings would need to be archived, or would require processing (and a professional to take action upon its receipt). Manually opening, reading, and figuring what to do with these documents was taking up significant amounts of time.
With a cloud-native intelligent document processing solution built on AWS, the law firm became able to harness the power of AI to sort through all this paperwork. The solution is able to classify incoming documents into two main categories—those that can be read and archived, and those that require significant professional activity. Some outliers require manual review. During this review process, the system can be further trained, so that fewer and fewer documents will require such review as the training proceeds.
Such intelligent document processing solutions can extract key information, identify relevant clauses, and even redact sensitive data automatically. This saves many hours of tedious manual work, so that the practice can be smarter and more efficient. It’s possible to build and train a custom model to minimize error rates and ensure consistent performance. If it’s running on the public cloud, that model can be continuously updated as regulations and business requirements change. Public cloud providers like AWS adhere to industry-leading standards for security (including robust encryption), and support rigorous governance to protect the confidentiality and integrity of the customer’s data.
Consumer banking is a highly competitive industry, especially since customer expectations for convenient, high-performing digital services continue to climb. Research from McKinsey shows that banks with higher customer satisfaction rates see higher shareholder returns, faster growth, and lower costs. Banks are using generative AI to create hyper-personalized content for individual customers, and summarize customer interactions for inclusion in a knowledge repository. Other types of AI, including machine learning, can perform sentiment analyses to help customer service teams improve their performance over time.
Financial services companies are also turning to AI to improve their credit decisions and optimize fraud detection. Increasingly, too, banks are building cloud-native digital services for their customers. With this approach, new capabilities can be delivered quickly, so that banks can keep pace with rapidly-evolving trends and consumer preferences. Financial institutions are also turning to AI to improve financial planning, analysis processes, data reporting, and observability (enhancing audit-readiness while protecting sensitive data).
In today’s world, a company’s most valuable asset is its data. But unlike other forms of wealth, data is only valuable when it’s put to work. Stakeholders are understandably eager to take advantage of AI’s recent evolution, but it is important to begin by identifying the business problems where AI can provide the most value. From there, you’ll need to ensure your organization has the right data pipelines, infrastructure, and processing capabilities—all of which are available in the public cloud.
Successfully embedding AI into your organization’s value chain has the potential to transform your business. Nearly every company has untapped potential when it comes to implementing new technologies.
Want to realize yours? Contact us today. We’ll help you turn your vision into a project that can be implemented within your real-world budget and time frame.
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