Navigating a shifting customer-engagement landscape with generative AI

One can’t step into the same river twice. This simple representation of change as the only constant was taught by the Greek philosopher Heraclitus more than 2000 years ago. Today, it rings truer than ever with the advent of generative AI. The emergence of generative AI is having a profound effect on today’s enterprises—business leaders face a rapidly changing technology that they need to grasp to meet evolving consumer expectations.

“Across all industries, customers are at the core, and tapping into their latent needs is one of the most important elements to sustain and grow a business,” says Akhilesh Ayer, executive vice president and global head of AI, analytics, data, and research practice at WNS Triange, a unit of WNS Global Services, a leading business process management company. “Generative AI is a new way for companies to realize this need.”

A strategic imperative

Generative AI’s ability to harness customer data in a highly sophisticated manner means enterprises are accelerating plans to invest in and leverage the technology’s capabilities. In a study titled “The Future of 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 use generative AI.

According to McKinsey, while generative AI will affect most business functions, “four of them will likely account for 75% of the total annual value it can deliver.” Among these are marketing and sales and customer operations. Yet, despite the technology’s benefits, many leaders are unsure about the right approach to take and mindful of the risks associated with large investments.

Mapping out a generative AI pathway

One of the first challenges organizations need to overcome is senior leadership alignment. “You need the necessary strategy; you need the ability to have the necessary buy-in of people,” says Ayer. “You need to make sure that you’ve got the right use case and business case for each one of them.” In other words, a clearly defined roadmap and precise business objectives are as crucial as understanding whether a process is amenable to the use of generative AI.

The implementation of a generative AI strategy can take time. According to Ayer, business leaders should maintain a realistic perspective on the duration required for formulating a strategy, conduct necessary training across various teams and functions, and identify the areas of value addition. And for any generative AI deployment to work seamlessly, the right data ecosystems must be in place.

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

All that, however, would not be possible without data on past claims history, repair costs, transaction data, and other necessary data sets to extract clear value from generative AI analysis. “Be very clear about data sufficiency. Don’t jump into a program where eventually you realize you don’t have the necessary data,” Ayer says.

The benefits of third-party experience

Enterprises are increasingly aware that they must embrace generative AI, but knowing where to begin is another thing. “You start off wanting to make sure you don’t repeat mistakes other people have made,” says Ayer. An external provider can 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 accelerate deployment. “Generative AI programs can be extremely complicated,” Ayer points out. “There are a lot of infrastructure requirements, touch points with customers, and internal regulations. Organizations will also have to consider using pre-built solutions to accelerate speed to value. Third-party service providers bring the expertise of having an integrated approach to all these elements.”

Ayer offers the example of WNS Triange helping a travel intermediary use generative AI to deal with customer inquiries about airline rescheduling, cancellations, and other itinerary complications. “Our solution is immediately able to go into a thousand policy documents, pick out the policy parameters relevant to the query… and then come back quickly not only with the response but with a nice, summarized, human-like response,” he says.

In another example, Ayer shares that his company helped a global retailer create generative AI–driven designs for personalized gift cards. “The customer experience goes up tremendously,” he says.

Hurdles in the generative AI journey

As with any emerging technology, however, there are organizational, technical, and implementation barriers to overcome when adopting generative AI.

Organizational:  One of the major hurdles businesses can face is people. “There is often immediate resistance to the adoption of generative AI because it affects the way people work on a daily basis,” says Ayer.

As a result, securing internal buy-in from all teams and being mindful of a skills gap is a must. Additionally, the ability to create a business case for investment—and getting buy-in from the C-suite—will help expedite the adoption of generative AI tools.

Technical: The second set of obstacles relates to large language models (LLMs) and mechanisms to safeguard against hallucinations and bias and ensure data quality. “Companies need to figure out if generative AI can solve the whole problem or if they still need human input to validate the outputs from LLM models,” Ayer explains. At the same time, organizations must ask whether the generative AI models being used have been appropriately trained within the customer context or with the enterprise’s own data and insights. If not, there is a high chance that the response will be incorrect. Another related challenge is bias: If the underlying data has certain biases, the modeling of the LLM could be unfair. “There have to be mechanisms to address that,” says Ayer. Other issues, such as data quality, output authenticity, and explainability, also must be addressed.

Implementation: The final set of challenges relates to actual implementation. The cost of implementation can be significant, especially if companies cannot orchestrate a viable solution, says Ayer. In addition, the right infrastructure and people must be in place to avoid resource constraints. And users must be convinced that the output will be relevant and of high quality, so as to gain their acceptance for the program’s implementation.

Lastly, privacy and ethical issues must be addressed. The Corinium Intelligence and WNS Triange survey showed that almost 72% of respondents were concerned about ethical AI decision-making.

The focus of future investment

The entire ecosystem of generative AI is moving quickly. Enterprises must be agile and adapt quickly to change to ensure customer expectations are met and maintain a competitive edge. While it is almost impossible to anticipate what’s next with such a new and fast-developing technology, Ayer says that organizations that want to harness the potential of generative AI are likely to increase investment in three areas:

Data modernization, data management, data quality, and governance: To ensure underlying data is correct and can be leveraged.

Talent and workforce: To meet demand, training, apprenticeships, and injection of fresh talent or leveraging market-ready talent from service providers will be required.

Data privacy solutions and mechanisms: To ensure privacy is maintained, C-suite leaders must also keep pace with relevant laws and regulations across relevant jurisdictions.

However, it shouldn’t be a case of throwing everything at the wall and seeing what sticks. Ayer advises that organizations examine ROI from the effectiveness of services or products provided to customers. Business leaders must clearly demonstrate and measure a marked improvement in customer satisfaction levels using generative AI–based interventions.

“Along with a defined generative AI strategy, companies need to understand how to apply and build use cases, how to execute them at scale and speed to market, and how to measure their success,” says Ayer. Leveraging generative AI for customer engagement is typically a multi-pronged approach, and a successful partnership can help with every stage.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

Leave a comment