IBM is actively using artificial intelligence (AI) to improve efficiency across its operations. Viswanath Ramaswamy, vice-president of technology at IBM India & South Asia, told FE that by implementing generative AI, IBM tests many of its own products and services first. “In addition to testing and validating what we create, this also allows us to build use cases that can benefit our customers,” Ramaswamy said. “For example, we have deployed a dedicated, conversational AI-powered platform called AskHR for IBMers globally. This platform has transformed how our HR teams work, allowing them to focus on more critical tasks at hand,” he added.
The platform provides better speed, satisfaction, productivity and optimised inputs leading to far better outputs. Around 59% of enterprise-scale organisations in India are now using AI actively in their businesses, according to IBM. This statistic comes from the IBM Global AI Adoption Index 2023. The report highlights that early adopters are accelerating their AI investments, particularly in R&D and workforce reskilling.“Indian businesses are increasingly relying on AI to automate key business processes and reduce costs,” Ramaswamy said. Common applications include automating manual tasks, enhancing customer self-service, and improving recruitment and HR operations.
Responsible AI
The adoption of responsible AI is gaining momentum as Indian companies aim to scale their AI systems within a changing regulatory environment. The prominence of responsible AI, especially in regulated sectors like BFSI, is growing as these technologies become more integrated into daily operations.Ramaswamy explains that organisations must ensure that their AI models adhere to principles of explainability, fairness, robustness, transparency, and privacy. “Business leaders are concerned about new cybersecurity vulnerabilities and legal uncertainties,” he added. Financial institutions should use AI governance to ensure ethical and accountable use of GenAI across the sector.
However, creating trustworthy AI systems presents several challenges for organisations. Ramaswamy cited lack of a consolidated view of various AI models and the need for a centralised repository as major hurdles. Additionally, ensuring that AI models receive the necessary approvals before deployment is often a manual process in many organisations.“Many businesses also lack a central platform that allows them to establish tolerances and alerts, detect accuracy, and monitor performance while meeting compliance requirements,” Ramaswamy said.
IBM’s role in enhancing AI governance capabilities
The company provides solutions that help businesses audit and mitigate risks, implement governance frameworks, and operationalize AI effectively. A prime example is IBM watsonx.governance, a key component of the integrated AI platform IBM watsonx. This solution spans the entire AI lifecycle, including model building, deployment, monitoring, and centralizing information for AI transparency and explainability, Ramaswamy said.
