Cost and accuracy of AI models still evolving: TCS – Digital Transformation News

Although the implementation of generative AI, and AI more broadly, offers many benefits in terms of cost savings and revenue growth in the long term, many organizations remain hesitant to adopt these technologies, according to Tata Consultancy Services (TCS).

Key concerns include the accuracy of AI predictions, high implementation costs, and the complexity of integrating these systems with existing business processes.

Companies are particularly cautious about making large investments in this evolving technology. The expected return on investment may not be realized, especially as GenAI has only just come out of its experimental phase and may disrupt existing workflows without providing sufficient benefits, says Krishna Mohan, vice president and deputy head of TCS' AI.Cloud unit.he told FE.

“Cost is certainly one aspect of it, but when you integrate and implement it at enterprise scale, you have to consider performance, security, accuracy, removal of bias, and whether it will work when integrated in a real-world environment. “Production systems? So I think that's an area that will continue to evolve,” Mohan said.

Increased revenue and cost efficiency

According to the company's recently released report, “TCS AI for Business Study,” only 19% of approximately 1,300 CEOs surveyed had a “sufficient” evaluation criteria for the current stage of AI adoption.

But an overwhelming majority, 86%, of senior business leaders believe AI can significantly improve their revenue streams. According to Mohan, “The vast majority of clients we spoke to – more than 1,300 CEOs and others – definitely recognize the real role that AI can play in reimagining their business models and driving business efficiencies. So many projects are primarily about increasing revenue and improving cash flow.”

Areas where AI has proven particularly effective include predictive analysis of customer behavior, supply chain optimization and even complex tasks like drug discovery, he added. In areas such as customer service, AI-powered chatbots and virtual assistants are already making a difference by improving response times and customer satisfaction.

In manufacturing, AI is used for predictive maintenance of equipment, helping to avoid downtime and reduce repair costs.

However, this means that the impact of AI and GenAI will go beyond just financial metrics and will impact current employment structures and sector-specific roles, resulting in some job losses. There will be replacements, but new roles will also emerge, especially in areas related to AI, he added.

Accelerating Cloud Adoption

GenAI adoption is driving companies to the cloud, Mohan said, where the ease of accessing and processing vast data sets makes it a must-have technology for AI.

“Generative AI definitely needs data. The best thing about generative AI is that it doesn't need any data structures. It can read unstructured data as well. That's why the cloud is becoming more and more prevalent and most of these models are interconnected,” he said.