Businesses are keenly exploring the capabilities of Generative Artificial Intelligence (Gen AI). Powered by Large Language Models (LLMs), Gen AI can simulate human-like interactions, proving useful in many sectors. For instance, Morgan Stanley has tapped into Gen AI to extract insights from more than 100,000 research reports for its financial advisers. Salesforce has integrated this technology into its Customer Relationship Management (CRM) platform.
However, while LLMs are versatile, they often lack the depth and nuance required for industry-specific outcomes. Beyond this, there are concerns surrounding data security, potential biases and misinformation.
During a LinkedIn Live session involving prominent WNS Leaders and guest speaker Mike Gualtieri, VP & Principal Analyst at Forrester Research, we discussed how the answer could lie in domain-specific language models. These models, tailored to individual industries, address unique challenges more effectively.
The Emergence of Domain-specific Models
Take the insurance industry. Imagine a bustling cityscape – New York or Paris. Accidents? Pretty common. When they occur, the subsequent insurance claims can be a tedious process. Traditionally, determining fault has often been reduced to manual sampling, where only a tiny percentage of claims is examined. The problem? A staggering 70 percent of these could be false positives, leading to wasted resources.
Enter Gen AI. Through advanced models tailored to the insurance landscape, the process can undergo a seismic shift. Gen AI can process every claim, analyzing photos, notes, e-mails and descriptions to determine if subrogation (enabling insurers to recover the costs of a claim by initiating legal action against the third-party responsible for the loss) is possible. Additionally, it assesses damage levels, potential repair options and even the financial metrics surrounding the claim. The result? Swift claims resolutions, enhanced accuracy and, ultimately, higher profitability for insurance companies.
In healthcare, it can be cumbersome for professionals to navigate heaps of medical claims. Nurses and medical coders sift through stacks of documents, diagnosing, identifying treatments and assigning medical codes – a daunting and time-consuming task.
The ordeal is overcome by integrating custom-curated Gen AI with proprietary AI models. The AI combs through extensive medical documentation, distilling it into a concise summary. Imagine the time saved, errors reduced and the resulting increase in patient satisfaction.
For most of us, travel planning involves juggling itineraries, hotels and flight bookings, often with changes along the way. Traditionally, making modifications required a service representative to sift through a mountain of policy documents to determine eligibility, potential refunds or penalties – a process ripe for errors and biases.
However, with Gen AI's capability, these decisions are made rapidly and accurately. The AI presents a clear, contextualized answer by analyzing vast policy documents across airlines and properties, making the entire experience seamless for the traveler.
Conclusion
These are just three instances from a plethora of use cases that showcase the transformative power of Gen AI across industries. We're talking about impact that goes beyond mere productivity – enhanced accuracy, reduced bias and significant financial implications. It's an exciting era, and as Gen AI continues to evolve, its ability to re-shape our world will only grow stronger.
Dive into Gen AI’s transformative impact on industries. Watch our insightful discussion now.