Published on July 3, 2024, 9:12 am

Unveiling The Truth: Revenue Realities Of Generative Ai Integration In It Services

ShareIT services companies have been at the forefront of incorporating generative AI, launching GenAI services alongside hyperscalers and SaaS companies. The burning question remains: how much revenue are these IT firms truly deriving from generative AI alone? Accenture has disclosed billions in revenue from this technology, while TCS has unveiled a $900 million generative AI deal pipeline. However, Tanvir Khan from NTT DATA reveals that a significant portion of reported generative AI revenue stems from its integration into existing business operations rather than spawning new revenue streams.

Khan identifies three categories within the realm of generative AI where “AI washing” occurs. Firstly, there are genuine use cases solely powered by generative AI. Secondly, traditional processes like call centers leveraging chatbots are now perceived as generative AI applications, although they contribute only partially to overall revenue. Lastly, scenarios like cloud infrastructure management witness productivity boosts through generative AI automation, which might lead to the entire revenue being classified as generative AI-driven, despite the core business model remaining unchanged.

Enterprises have observed tangible benefits from generative AI adoption with claims of up to 30-35% increased efficiency in software development projects. Khan also hints at forthcoming categories like mature AI-based products which may require a few more years to come to fruition.

Venkata Malapaka echoes similar sentiments emphasizing that while consumer-focused initiatives like ChatGPT have thrived, enterprise-level implementation of generative AI remains limited. At NTT DATA, where 50% of projects heavily rely on generative AI and 90% involve some form of its application, substantial growth is seen in this domain.

Moreover, NTT DATA’s focus on developing foundational models such as Tsuzumi underscores their commitment towards sustainable innovation in the AI landscape. Tsuzumi’s lightweight design caters to both Japanese and English languages and is projected to make significant strides in enterprise use cases over the coming quarters.

NTT DATA’s strategic approach towards foundational model development serves as an insurance policy for future advancements rather than a competitive move within the market. As Khan aptly puts it – “AI is a marathon, not a sprint,” indicating that the true potential of generative AI will unfold progressively over years rather than months.

In essence, as IT companies navigate through the evolving landscape of artificial intelligence and Generative AI technologies like GenAI shape new horizons for businesses across sectors, it becomes increasingly apparent that embracing innovation coupled with strategic planning is crucial for sustained success in this transformative era.

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