A strategic imperative

Generative AI’s ability to harness customer data in a highly sophisticated manner means enterprises are accelerating plans to take a position in and leverage the technology’s capabilities. In a study titled “The Way forward for Enterprise Data & AI,” Corinium Intelligence and WNS Triange surveyed 100 global C-suite leaders and decision-makers specializing in AI, analytics, and data. Seventy-six percent of the respondents said that their organizations are already using or planning to make use of generative AI.

In keeping with McKinsey, while generative AI will affect most business functions, “4 of them will likely account for 75% of the full annual value it may deliver.” Amongst these are marketing and sales and customer operations. Yet, despite the technology’s advantages, many leaders are unsure about the precise approach to take and mindful of the risks related to large investments.

Mapping out a generative AI pathway

One in every of the primary challenges organizations must overcome is senior leadership alignment. “You would like the vital strategy; you wish the power to have the vital buy-in of individuals,” says Ayer. “You must be certain that that you’ve the precise use case and business case for every one in every of them.” In other words, a clearly defined roadmap and precise business objectives are as crucial as understanding whether a process is amenable to using generative AI.

The implementation of a generative AI strategy can take time. In keeping with Ayer, business leaders should maintain a sensible perspective on the duration required for formulating a method, conduct vital training across various teams and functions, and discover the areas of value addition. And for any generative AI deployment to work seamlessly, the precise data ecosystems have to be in place.

Ayer cites WNS Triange’s collaboration with an insurer to create a claims process by leveraging generative AI. Because of the recent technology, the insurer can immediately assess the severity of a vehicle’s damage from an accident and make a claims suggestion based on the unstructured data provided by the client. “Because this may be immediately assessed by a surveyor and so they can reach a suggestion quickly, this immediately improves the insurer’s ability to satisfy their policyholders and reduce the claims processing time,” Ayer explains.

All that, nonetheless, wouldn’t be possible without data on past claims history, repair costs, transaction data, and other vital data sets to extract clear value from generative AI evaluation. “Be very clear about data sufficiency. Don’t jump right into a program where eventually you realize you haven’t got the vital data,” Ayer says.

The advantages of third-party experience

Enterprises are increasingly aware that they need to embrace generative AI, but knowing where to start is one other thing. “You begin off wanting to be certain that you do not repeat mistakes other people have made,” says Ayer. An external provider may help organizations avoid those mistakes and leverage best practices and frameworks for testing and defining explainability and benchmarks for return on investment (ROI).

Using pre-built solutions by external partners can expedite time to market and increase a generative AI program’s value. These solutions can harness pre-built industry-specific generative AI platforms to speed up deployment. “Generative AI programs may be extremely complicated,” Ayer points out. “There are plenty of infrastructure requirements, touch points with customers, and internal regulations. Organizations may also have to think about using pre-built solutions to speed up speed to value. Third-party service providers bring the expertise of getting an integrated approach to all these elements.”