Unlocking generative AI’s greatest growth opportunities

Over the last year, generative AI—a form of artificial intelligence that can compose original text, images, computer code, and other content—has gone from experimental curiosity to a tech revolution that could be one of the biggest business disruptors of our generation. Along the way, business leaders in every industry have been scrambling to develop their generative AI strategies, address potential risks, and figure out the best next action while trying to stay one step ahead of the competition.

The challenge now, of course, is separating the hype from reality to move forward on a path that makes sense for real businesses.  Fortunately, while the spotlight on generative AI may be new, the underlying technology has been around for quite some time, and many pioneering companies have already begun paving the way for successful commercial-grade implementations. In fact, our team has been working with generative and conversational AI in complex professional services applications like insurance, banking, and healthcare for the better part of a decade, and we’ve learned some important lessons along the way.

Where will the biggest transformation occur first?

First, it is clear that generative AI will transform business. The rate at which the technology continues to grow, and its amazing ability to keep getting better, is nothing short of remarkable. Any job function that involves working through large amounts of information to synthesize insights will be touched by generative AI. Importantly, however, that does not mean humans will be removed from the loop. It just means the way we do our jobs will change.

Second, the leaders in the generative AI arms race will be those who are able to get their data organized and accessible quickly. While off-the-shelf generative AI technology from the likes of OpenAI, Google, Amazon, and others is incredibly powerful, the key to successful commercial generative AI initiatives is having authoritative, comprehensive reference data to train the system for a specific use case.

For example, some of the specific use cases where we are currently seeing the fastest and most impactful commercial applications of generative AI are in the following four areas:

1. Agent Assist in Customer Experience: These are AI-powered applications that leverage previous customer activity, payment histories, transactions, and investment records to equip live customer service agents with effective response scripts. Examples include insurance prior authorizations to check eligibility for a prescription and robo-enhanced investment guidance and advice in financial services.

2. Contract Analysis/Drafting: Generative AI solutions in finance, insurance, and legal sectors that extract key information from contracts and legal documents, address inconsistencies, identify risks, and even draft legal text.

3. Audit: In finance and accounting, businesses are deploying generative AI to analyze 100% of compliance documents, replacing the outdated sample-based compliance approach.

4. Code Generation: Across all sectors, we are seeing clients utilizing generative AI to write code, detect bugs and streamline product development.

Strong data governance and human expertise: Essential ingredients

Across all these examples, reliable data and human expertise are critical differentiators between genuine, commercial-grade solutions and mere technological novelties. In complex, highly regulated industries like insurance and financial services simply applying an off-the-shelf AI chatbot to a front-end interface is not an option. To provide real value, and mitigate risk, these applications must comprehend the entire customer data landscape, be trained on specific use case nuances, and produce accurate results that are easily verifiable.

Early leaders are already recognizing that rock-solid data governance and cloud migration capabilities are the critical prerequisites for a strong generative AI strategy. Likewise, they realize that human talent will be central to success. From programming and training large language models to integrating the technology into existing workflows to interpreting nuances in specialized subject matter, effective generative AI will continue to require human intermediation. After all, generative AI’s true potential lies in augmenting our strengths and mitigating our weaknesses, not rendering us redundant.

As more commercial-grade generative AI solutions come to market, the distinction between those that systematically improve workflows and drive better customer engagement and those who claim to automate everything effortlessly with the push of a button will become abundantly clear. While we are currently at the beginning of a massive surge in new innovation involving generative AI, it is crucial for companies integrating and building these technologies to see beyond the hype and embrace this exceptional opportunity to strengthen their products, services, and workforce.

Learn more about how EXL can put generative AI to work for your business here.

About the Author:

Rohit Kapoor is the vice chairman and CEO at EXL, a multinational data analytics and digital operations and solutions company.