Published on December 18, 2023, 4:11 pm

Change is the only constant, as Greek philosopher Heraclitus taught more than 2,000 years ago. This timeless wisdom holds true today, especially with the rise of generative AI. The emergence of generative AI is revolutionizing enterprises and forcing business leaders to adapt to evolving consumer expectations.

Akhilesh Ayer, Executive Vice President and Global Head of AI, Analytics, Data, and Research Practice at WNS Triange, explains that understanding customer needs is essential for sustaining and growing a business. Generative AI offers companies a new way to tap into these latent needs.

Generative AI’s ability to utilize customer data in a sophisticated manner has prompted enterprises to invest in its capabilities. A study conducted by Corinium Intelligence and WNS Triange surveyed 100 global C-suite leaders and decision-makers specializing in AI, analytics, and data. The study revealed that 76% of respondents are either already using or planning to use generative AI.

According to McKinsey, marketing and sales, as well as customer operations, will likely benefit the most from generative AI—accounting for 75% of its total annual value. Despite these benefits, many business leaders are uncertain about the best approach to adopt due to potential risks associated with large investments.

To successfully implement generative AI strategies within organizations, senior leadership alignment is crucial. Clear roadmaps outlining specific business objectives must be established. Additionally, it is important for businesses to assess whether a particular process can benefit from generative AI.

Implementing a generative AI strategy takes time. Ayer advises business leaders to maintain a realistic perspective on the duration required for strategy formulation, provide necessary training across various teams and functions, identify areas for value addition, and establish adequate data ecosystems.

Ayer cites an example of WNS Triange collaborating with an insurer to leverage generative AI in claims processing. Thanks to this technology, insurers can quickly assess vehicle damage severity based on unstructured client data. Consequently, this improves customer satisfaction and reduces claims processing time.

However, the success of generative AI applications relies on the availability of relevant data sets. Ayer emphasizes the importance of ensuring sufficient data before embarking on any generative AI program.

Enterprises recognize the need to embrace generative AI but may struggle with determining where to begin. Ayer suggests seeking external assistance to avoid repeating mistakes made by others and leveraging best practices for testing, defining explainability, and establishing benchmarks for return on investment (ROI).

Partnering with third-party providers can expedite time to market and enhance the value of generative AI programs. These providers offer industry-specific platforms that accelerate deployment by utilizing pre-built solutions. They possess integrated approaches that encompass infrastructure requirements, customer touchpoints, and compliance with internal regulations.

Another challenge in adopting generative AI relates to organizational, technical, and implementation barriers. Resistance from employees due to changes in their daily work routines is a hurdle that businesses must overcome. Additionally, organizations must ensure data quality and navigate potential biases within large language models (LLMs). The cost of implementation and resource constraints may also pose challenges.

Despite these hurdles, enterprises should focus their future investments on three areas: data modernization, talent development and workforce capabilities, and privacy solutions. Adapting to customer expectations requires agility and quick adaptation.

To measure success in harnessing the potential of generative AI, organizations need clear strategies, effective use cases, scalability, speed to market execution, and measurable improvements in customer satisfaction levels.

Ayer concludes by emphasizing the importance of forming successful partnerships throughout each stage of leveraging generative AI for customer engagement. With a well-defined strategy and effective execution, businesses can unlock the full potential of generative AI.

(Disclaimer: This content was produced by Insights, MIT Technology Review’s custom content arm.)

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