Generative Artificial Intelligence

Gen AI Adoption: Steering Clear of Unrealistic Expectations

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A WNS Perspective

Key Points

Findings from a recent survey by HFS Research, in partnership with WNS, indicate that 25 percent of executives view Generative AI as overhyped.
The challenge in effective Generative AI adoption arises from unrealistic expectations, often established by boardroom executives who may not fully grasp the technology's capabilities.
Organizations must recognize that Generative AI is a collaborative tool, not a panacea. Success lies in setting realistic expectations, acknowledging limitations and embracing a resilient mindset.

In recent months, the buzz surrounding Generative Artificial Intelligence (Gen AI) has reached unprecedented levels. However, amid this widespread excitement, businesses across industries face a common challenge: Establishing realistic expectations regarding Gen AI's capabilities and constraints, as underscored in a recent survey conducted by HFS Research in collaboration with WNS. This survey, involving executive leaders across seven industries, examined the strategic considerations organizations contend with during Gen AI implementation.

The research findings presented in Democratizing Gen AI: A Reality Check for Business Transformation reveal that 25 percent of respondents perceive Gen AI as overhyped. They attribute this sentiment to the unrealistic expectations set by boardroom executives and leaders who may not fully grasp the technology’s capabilities.

The report unveils that excessively exaggerated expectations of Gen AI could lead to disillusionment if the technology fails to meet benchmarks. This, in turn, might impede the seamless integration of Gen AI into business operations, hindering the realization of its true potential. Furthermore, the dissonance between expectations and reality may breed skepticism and reluctance among decision-makers.

Data Quality & Domain Specificity: The Cornerstone of Gen AI Success

As one of the survey respondents puts it, “The quality of data that you use defines the quality of the outcome that you get out of a Gen AI project." Data quality emerges as a paramount concern across sectors. Ensuring accurate, reliable and integral Gen AI-generated outputs mandates rigorous controls and a commitment to upholding data quality.

Moreover, a nuanced comprehension of industry domains coupled with a profound understanding of AI's core capabilities is indispensable. Domain-specific Gen AI adopts a specialized approach to data analysis and algorithmic computation, culminating in elevated accuracy and reliability tailored to the intricacies of those specific domains.

For instance, in regulated sectors like pharma and finance, compliance is non-negotiable. Clear communication with regulators and a deep understanding of AI models become imperative. Conversely, industries with less stringent regulations, like information services and automotive, must guard against the allure of hype-driven, rushed adoption. Internal direction and effective handling of technical complexities become pivotal.

Gen AI is a Collaborator, Not a Magic Wand

“People believe that Gen AI can solve everything. It cannot... it's not going to solve all of our problems. It can be a helping hand,” summed up a survey participant. Enterprises must set realistic expectations around Gen AI adoption, understanding that failures are opportunities to learn and grow. Navigating the intricate landscape requires a delicate balance between acknowledging limitations and harnessing the immense potential that Gen AI offers. As industries chart their course through the challenges, the key lies in cultivating a resilient mindset, staying informed and embracing the transformative power of Gen AI with open eyes and a clear vision.