Science once drove technology – but now the reverse is true. Here's how we can benefit

For centuries, science and technology have propelled each other forward in a dance of progress. Traditionally, scientific discoveries led the way, with breakthroughs like Newton's laws enabling the Industrial Revolution, and quantum mechanics paving the path for the digital age. In this paradigm, science predominantly fueled technological advancements.

Today, we're witnessing a fascinating role reversal.

While scientific insights still drive technological innovation, we're increasingly seeing technology, particularly artificial intelligence and machine learning, catalyzing scientific breakthroughs. AI algorithms are predicting protein structures faster than traditional lab methods, machine learning models are accelerating drug discovery, and advanced computing is enabling scientific simulations of unprecedented complexity.

In this new paradigm, emerging technological capabilities open doors to deeper scientific understanding, which in turn drives new opportunities for furthering technological enrichment.

This virtuous flywheel of science and technology has tremendous potential to translate into tangible improvements in our daily lives, from more effective medications to smarter devices and cleaner energy solutions.

AI is dramatically shrinking research and experimentation cycles, making discoveries that once took decades to unfold possible within mere years.

In chemistry and biology, AI-driven algorithms are transforming the way we approach complex challenges, such as predicting molecular structures and interactions. A notable example is DeepMind’s AlphaFold, which has revolutionized protein folding by predicting protein structures with unprecedented accuracy.

In the same fields, consider the realm of personalized medicine and disease control, where AI-driven platforms are identifying potential treatments in a fraction of the time it once took. A prime example of this is the integration of AI with CRISPR gene editing technology. AI models help predict potential off-target effects of CRISPR, making gene editing safer and more precise. This accelerates the development of new therapies, pushing the boundaries of what’s possible in genetic modifications and personalized treatments.

Another area that benefits greatly from accelerated tech-driven innovation is material sciences. This field is undergoing a transformation through the integration of advanced technologies. New materials, from sustainable alternatives to stronger alloys, are being developed at unprecedented rates thanks to innovative research and technology.

For industries ranging from aerospace to renewable energy, the implications are vast: faster innovation cycles, reduced R&D costs and a competitive edge in developing materials that meet the demands of tomorrow's markets.

And while AI is the catalyst for the fresh momentum in technology-propelled scientific innovation, other technologies like machine learning and quantum computing are also accelerating this trend. Quantum computing usage is still nascent, yet companies like D-Wave are investing early in areas like quantum annealing, which could solve complex molecular comparison problems faster than classical computers, or gate-based quantum to simulate molecular structures and chemical reactions.

The democratization effect of AI – broadening access to many types of specialized knowledge and tools by removing many traditional cost and access barriers – extends also to the overall research and development process itself. Through rapid data analysis, automated experimentation and cost reduction, AI now enables organizations of all sizes to conduct more sophisticated research.

AI agents, acting as tireless research assistants that can analyze data, generate hypotheses and even design experiments autonomously, will soon supplement human work. These AI-powered tools won't replace human creativity; rather, they will amplify it, allowing researchers to explore ideas at a pace and scale previously reserved for well-funded institutions.

Furthermore, AI is driving open data initiatives and collaborative platforms, allowing researchers worldwide to build on each other's work more easily. Work like Cognizant's contribution to Project Resilience in support of UN’s Sustainable Development goals showcase how AI can foster global collaboration on pressing challenges, from climate change to education and pandemic preparedness.

The acceleration AI and other technologies bring to scientific innovation isn't just a challenge to keep up with – it's an opportunity to redefine industry boundaries and transform everyday life.

Leaders must proactively explore and evaluate how to incorporate technology-fueled R&D into their business strategy and build a culture of experimentation.

By embracing this dynamic interplay, leaders have the chance to drive transformative change; not only within their industries, but also in solving some of the world's most pressing challenges – turning complex problems into breakthroughs that benefit businesses, people, and the planet. As we navigate this evolving landscape, the question isn't whether to participate in this cycle, but how boldly we choose to engage with it.