Firms bank on AI-led R&D for more innovation, efficiency

Companies are ramping up investments in research and development (R&D) to drive innovation and maintain competitiveness. Experts believe that technologies like artificial intelligence (AI) and advanced data analytics are transforming traditional research methodologies, making them more efficient, data-driven and scalable.

Major tech firms such as Google, Microsoft, Amason, Apple, IBM, and Meta are heavily investing in AI-led R&D, applying AI across various processes, including machine learning, natural language processing, and computer vision. Similarly, pharmaceutical companies like Janssen (Johnson & Johnson) Boehringer Ingelheim, and many others, are leveraging AI for drug discovery and clinical trials, significantly enhancing their R&D efforts.

AI maximising R&D potential

Despite India's R&D expenditure being less than one per cent of its GDP—below the global average—the country is making strides in R&D, with companies in healthcare, life sciences, energy, and engineering embracing digital transformation. McKinsey estimates in June 2023 that AI-driven R&D could yield an additional $328 billion in revenue in 2-3 years, with AI already accounting for about 15% of global R&D spending. While Indian firms lag behind the US and China in AI R&D investments, the ongoing digital transformation across industries is prompting businesses to explore AI for new opportunities and improved efficiency, the consulting firm said.

Shardul Sangal, Senior Vice President at IT software firm R Systems, said that many companies are investing in AI capabilities by setting up innovation labs and centre of excellences (CoEs), and modernising infrastructure for AI experimentation. “AI's most significant contribution is in data analysis, where algorithms can swiftly process vast datasets, revealing patterns that human researchers might miss. This capability is particularly beneficial in complex fields like genomics and climate modelling, where AI helps identify trends that drive innovation,” he said.

Predictive analytics is another transformative aspect, enhancing forecasting accuracy and optimising research outcomes. AI also improves collaboration in R&D by providing intelligent tools for data sharing, project management, and real-time knowledge exchange, allowing research teams to tackle complex problems more efficiently.

Bikram Dasgupta, Life Sciences & Health Consulting Leader at EY Global Delivery Services, highlighted that companies across various sectors in India are investing in R&D to leverage AI and other technologies. As example, he said, the pharmaceutical sector is forming AI partnerships to enhance drug discovery, the automotive industry is focusing on R&D for autonomous driving, and manufacturing is investing in predictive maintenance through IoT and AI. Telecom giants are investing in 5G and AI for advanced solutions, while defense organisations are developing AI-powered technologies.

In healthcare, the pharmaceutical industry is utilising AI to analyse patient data for targeted therapies, while aerospace companies employ AI simulations to reduce the need for physical prototypes. Government research centres and space organisations in India are also using AI for drug discovery, diagnostics, and satellite data analysis.

“As AI evolves, its role in R&D will expand, accelerating innovation, reducing costs, and unlocking new possibilities across industries,” said Sangal.

But not without challenges

Despite its potential, integrating AI into R&D still faces challenges like data quality issues, lack of skilled talent, data privacy and ethics, the need for significant infrastructure investment, and ensuring the accuracy and reliability of AI models when making critical decisions; making it crucial to tread cautiously in order to fully utilise AI's benefits in the processes.

Sudha KV, Vice President at Dell Technologies, emphasised that integrating AI into research processes requires substantial investments in technology, talent, and change management. “Many enterprises face limitations due to legacy infrastructure that is not optimised for AI workloads, hindering their ability to scale AI initiatives. Concerns about infrastructure, data privacy, ethical AI deployment, skills gaps, and regulatory compliance must be addressed for responsible AI adoption,” she said.

Sangal noted that AI-generated results may require expert intervention due to regulatory and ethical considerations, especially in healthcare, where strict guidelines govern AI-powered research. Additionally, data security risks are rising as AI relies on large amounts of sensitive data, making compliance with privacy regulations crucial. Dasgupta further said that the scarcity of skilled AI professionals and issues surrounding ethical AI development and bias mitigation are significant concerns, particularly in healthcare. In India, accessing quality AI training datasets and translating academic research into industry applications pose key challenges.

Companies like Dell Technologies are advancing AI-driven R&D through strategic partnerships and innovative solutions. “Our partnership with NVIDIA through the Dell AI Factory for example delivers pre-validated, full-stack AI solutions to help enterprises seamlessly integrate AI into their research workflows,” said Sudha, contributing to increased efficiency and quality.

Paving a successful AI-led R&D future

Successful AI integration necessitates cultivating an innovation-driven culture, investing in skill development, establishing robust data governance, and embracing iterative AI model testing. “These best practices not only help businesses harness AI’s current potential but also position them for long-term growth and sustained competitive advantage as AI continues to evolve,” Rahul Lodhe, Vice President and head of SAP Artificial Intelligence Technology India said.

Certain aspects of AI usage must be non-negotiable, particularly regarding privacy and security. Hyther Nizam, VP of product management at Chennai-headquartered Zoho Corp., said that enterprise data should not be entrusted to third-party LLMs, where customer data could be compromised. Instead, businesses should utilise RAG (retrieval augmentation generation) to incorporate real-time enterprise data into AI models.

Recent advancements in generative AI (GenAI) are also creating new opportunities. GenAI can analyse vast amounts of unstructured information, enabling researchers to efficiently extract knowledge from sources like academic literature, patents, and technical data sheets. This broadens the scope for exploring new materials and scientific methods while improving the quality and reliability of predictive models.

Nizam added that integrating Agentic AI, can further enable businesses to automate multiple tasks. Enterprises can leverage AI capabilities from vendors for development work, such as code generation and testing automation. He said, “Choosing the right software that effectively utilises Agentic AI while ensuring data privacy and security is critical.”

Looking ahead, Dasgupta believes AI will transform R&D enabling faster discoveries, sustainable solutions, and responses to critical challenges in climate change, cybersecurity, and healthcare. For example, the adoption of digital twins will allow for design optimisation before its physical production. In India, AI is expected to support sustainable agriculture and the creation of models for various regional languages.

Overall, companies with robust AI-driven R&D strategies will gain a competitive advantage, promoting innovation-led growth. Sudha from Dell emphasised that maximising AI's potential requires stakeholders to invest in ethical policies, AI infrastructure, and skilled talent. “With rapid advancements in AI, we envision a world where technology accelerates innovation and empowers industries to tackle their most complex challenges with unmatched speed and precision,” she said.