One cannot step into the same river twice. More than 2,000 years ago, the Greek philosopher Heraclitus uttered this phrase and saw change as the only thing that does not change.
Today, with the advent of generative AI, this is more true than ever. Generative AI is having a profound impact on today's businesses, and business leaders are faced with a rapidly changing technology that they need to master to meet changing consumer expectations.
"In all industries, customers are at the core, and tapping into their latent needs is one of the most important factors in sustaining and growing a business," said Akhilesh Ayer, executive vice president and global head of the AI, analytics, data and research practice at WNS Triange, part of business process management firm WNS Global Services. Generative AI is a new way for businesses to meet this need. ”
A strategic imperative
Generative AI can leverage customer data in highly sophisticated ways, which means businesses need to accelerate their plans to invest in and leverage the technology's capabilities.
In a study titled "Enterprise Data and the Future of AI," Corinium Intelligence and WNS Triange surveyed 100 global executives and decision-makers specializing in AI, analytics, and data.
76% of respondents said their organizations are already using or planning to use generative AI.
McKinsey said that while generative AI will impact most business functions, "four of them could account for 75% of its total annual value." "These include marketing, sales, and customer operations.
However, despite the benefits of this technology, many leaders don't know how to take the right approach and aren't sure about the risks of making large investments.
Mapping the path of generative AI
One of the first challenges organizations need to overcome is the alignment of senior leadership. "You need the necessary strategy, you need to have the ability to get the necessary support, you need to make sure that there are the right business use cases and cases," Ayer said. ”
In other words, a well-defined roadmap and precise business goals are just as critical as determining whether a process is a good fit for generative AI.
Implementing a strategy for generative AI can take time. According to Ayer, business leaders should maintain a realistic view of the time it takes to develop a strategy, conduct the necessary training for various teams and functions, and identify areas of added value.
In order for any generative AI deployment to work together seamlessly, the right data ecosystem must be established.
Ayer cites WNS Triange's work with an insurance company on a case of using generative AI to create a claims process.
Thanks to this new technology, insurers can immediately assess the severity of damage caused by an accident and make claims recommendations based on unstructured data provided by customers.
"Because this can be immediately assessed by a human appraiser, they can quickly draw conclusions that will immediately improve the insurer's ability to meet policyholder requirements and shorten claims processing times," Ayer explained. ”
However, none of these efforts would be possible without past claims history, repair costs, transaction data, other necessary data sets, and extracting clear value from the generated AI analysis.
"Be very clear about the adequacy of the data," Ayer said. Don't jump into a project where you realize you don't have the data you need. ”
Benefits of third-party experiences
Businesses are increasingly aware that they have to embrace generative AI, but know that starting with is another story. "You want to make sure you don't make the same mistakes as others from the beginning," Ayer said. ”
Third-party providers can help organizations avoid these mistakes and leverage best practices and frameworks to test and define explainability and benchmarks for ROI.
By using pre-built solutions, external partners can accelerate the time to go-live and increase the value of generative AI programs.
These solutions can leverage pre-built, industry-specific generative AI platforms to accelerate deployment.
"Generative AI programs can be extremely complex, with a lot of infrastructure requirements, customer engagement, and internal regulations to consider," Ayer noted.
Organizations must also consider using pre-built solutions to accelerate time to value. Third-party service providers can apply expertise to all of these elements. ”
Ayer cited the example of WNS Triange to help travel agents use generative AI to handle customer inquiries about airline rescheduling, cancellations, and other complex itineraries.
"Our solution was able to query 1,000 policy documents at once to find relevant policy ......," he saidThen it comes back quickly, and the response is not only fast, but also high-quality, summarizing, and human-friendly. ”
In another example, Ayer shared that his company helped a global retailer create AI-powered generative designs for personalized gift cards. "The customer experience has been greatly improved. He said.
Barriers to generative AI
However, as with any emerging technology, there are organizational, technical, and implementation hurdles to overcome when adopting generative AI technologies.
People are one of the main obstacles that organizations and businesses can face. "Adoption of generative AI tends to be met with immediate resistance because it affects the way people work every day," Ayer said. ”
Therefore, it is essential to ensure internal support from all teams and be aware of skill gaps. In addition, the ability to create a business case for the investment, as well as access to management, will help accelerate the process of technology adoption.
Technological barriers are related to large language models (LLMs) and mechanisms to reduce hallucinations and biases 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 output of the big model," Ayer explains. ”
At the same time, organizations must know whether the generative AI models they are using have been properly trained in the customer's environment or based on the organization's own data and insights.
If not, there is a high chance that an incorrect answer will be generated. Another challenge is bias: if there is some bias in the training data, the final large model may be unfair.
There must be mechanisms to address this issue. Ayer said. Other issues, such as data quality, output authenticity, and explainability, must also be addressed.
The last set of challenges relates to practical implementation. The implementation of the technology can be costly, especially if the company is unable to develop a viable solution, Ayer said.
In addition, it is essential to have proper infrastructure and personnel in place to avoid being constrained by resources. Users must be confident that the output is relevant and of high quality in order to gain their approval for the implementation of the project.
Finally, privacy and ethics issues must be addressed. Surveys by Corinium Intelligence and WNS Triange show that nearly 72% of respondents are concerned about ethical decision-making in AI.
Focus for future investments
The entire ecosystem of generative AI is rapidly evolving. Businesses must practice agile strategies and adapt quickly to change to ensure they meet customer expectations and maintain a competitive edge.
It's nearly impossible to ** what will happen next for such a new and fast-moving technology, but Ayer said organizations looking to harness the potential of generative AI are likely to increase their investment in three areas:
Data modernization, data management, data quality, and governance to ensure the correctness and usability of the underlying training data.
Having enough talent and workforce to meet the demand requires bringing in training, apprenticeships and injecting new talent, or leveraging talent from third-party service providers.
Comply with data privacy solutions and mechanisms. To ensure that privacy is protected, senior leaders must also comply with the relevant laws and regulations of the relevant jurisdictions.
However, the process shouldn't be about trying out all the options and finding the ones that work. Ayer advises organizations to examine ROI in terms of the effectiveness of the services or products provided to customers.
Business leaders must use generative AI-based interventions to clearly demonstrate and measure significant improvements in customer satisfaction.
"In addition to a clear generative AI strategy, companies need to understand how to apply and build use cases, how to bring them to market at sufficient scale and speed, and how to measure their success," Ayer said. ”
Leveraging generative AI to engage with customers is often a multi-stage approach, and successful partnerships can help at every stage.
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