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AI-Powered Research: How it's changing the industry landscape

AI-Powered Research: How it's changing the industry landscape

AI-Powered Research: How it's changing the industry landscape

AI-Powered Research: How it's changing the industry landscape

Feb 3, 2025

In 1999, Peter F. Drucker, the renowned management thinker, highlighted an important achievement: a fiftyfold increase in the productivity of manual workers in the 20th century. Think finishing a week's work in under an hour or producing fifty times the results in the same amount of time. This transformation revolutionized industries, economies, and lives. But just as the internet was taking off and starting to change the way we work, Drucker noticed something strange. Data showed that productivity gains for knowledge workers had hit a pause. This made him pose a provocative challenge for the future: could we achieve for knowledge workers the extraordinary productivity leap we accomplished for manual workers [1]?


Today feels different – and maybe it indeed is. However, it’s not just the technology; it’s also about how we use and engage with it. Generative AI, while making waves now, is not earth-shatteringly new. It has its roots in the early AI systems of the 1960s and 1970s, and the capabilities that we see today are steady improvements built on those foundations [2]. Since 2014, the underlying technology has gradually advanced, but the launch of ChatGPT in November 2022 opened the floodgates and those impressive capabilities were available directly to millions of consumers for the first time. That has sparked a widespread revaluation of what AI can achieve.

 

Everything, right from the way knowledge is discovered, analyzed, and applied, is undergoing rapid change. Now that AI has unleashed a world of possibilities, it becomes imperative to examine how AI research tools are changing - and will continue to change - the narrative for individuals, organizations, and the world of research itself to make sure AI not just simplifies tasks but also drives meaningful improvements and progress.


What’s in it for workers?

For knowledge workers—researchers, analysts, consultants, scientists, lawyers, and professionals whose work revolves around processing and applying information—the demand for accuracy, speed, and actionable insights is immense. The trillions of bytes of data available today have made conventional research methods time-consuming and exhausting. This is where AI-powered research tools step in, reimagining how knowledge workers operate by streamlining processes, decreasing effort, and improving outcomes.

 

Imagine you are a healthcare policy researcher and are tasked with delivering a presentation on mental health. Instead of poring over hundreds of research papers manually, you take the help of AI for research, for analyzing research papers, for looking through databases, for summarizing key findings, and for unearthing trends like telehealth adoption. In mere hours, you craft a compelling, data-driven story, saving time.  Or imagine you're a legal researcher trying to build a case for a client and have access to AI research tools that effortlessly look through many past court rulings for precedents on keying the case details. To top it off, instead of inundating you with information, the AI selectively highlights decisions most relevant to your argument and prepares notes on how they were challenged in similar cases. With this customized research, building a compelling case strategy rooted in facts becomes a cakewalk.

 

Each of these scenarios shows how AI-powered research not only eases existing workloads but also enables new ways of working even. It makes sure that knowledge workers manage time better by helping them find insights faster, collaborate better, and unearth opportunities that might otherwise remain hidden. The result? Better decisions, innovative solutions, and a future where knowledge workers are well-adapted to a data-driven world.


Benefits for organizations

It can seem irresistible to credit technology for all the productivity gains achieved during the industrial revolution. However, a closer look at history reveals that the real magic bullet was actually optimizing workflows. Productivity surged not because of the introduction of new machines (although it did have an impact), but because businesses first fixed their processes – creating efficient, step-by-step workflows. And once these were perfected, technology was then able to supercharge them which resulted in the 50X productivity gain that defined the era.

 

For over 30 years, scientists worked tirelessly to develop a vaccine for Japanese encephalitis, a deadly viral brain infection spread by mosquitoes. During this time, 10,000 to 15,000 people lost their lives each year to the disease, waiting for a solution that would finally bring hope [3]. Contrast that with the COVID-19 vaccine which is the fastest developed vaccine in history.  Apart from prior research & mRNA technology, this unprecedented speed was made possible by technology, specifically AI-powered research tools which played a critical role in streamlining the development and clinical trial phases [4]. AI didn't just help with technology, it optimized the research workflow, accelerating the process and making the breakthrough possible in record time.

 

AI-powered research tools are an important arsenal in any organization’s armory. They help organizations by providing quick and easy access to selected studies and data, saving time spent on manual searches. These tools have been shown to distill complex research into practical insights, making it easier to apply findings quickly. AI also aces at cross-referencing knowledge across multiple disciplines, bringing to light hidden connections and opportunities. And of course, anything that is data-backed sells more easily and AI ensures that organizations are equipped with the most relevant, up-to-date information, thereby supporting the decision-making process.


Impacts on research quality

AI research tools and AI assistants came to scientists' rescue and played a decisive part in detecting genetic markers and potential vaccine targets that otherwise would have taken much longer to identify manually [5]. Worry lines on AI skeptics commonly reflect their concern that AI might sacrifice quality for speed. But on the contrary, AI actually enhances research by improving accuracy and surfacing hard-to-find information which can lead to insights. It reduces human error, especially when dealing with large and complex datasets. While fatigue can make the average human miss important patterns and make mistakes, AI can swiftly analyze millions of data points, uncovering trends that might otherwise go unnoticed. 

 

AI also doesn’t shy away from repetitive tasks and enhances reproducibility by automating data management and categorization, ensuring consistency across studies. By taking over and streamlining the more tedious aspects of the research process like extracting information from lists of papers and cross-referencing knowledge, AI is pushing the envelope of what research can achieve, resulting in more reliable, innovative, and impactful discoveries.

 

The future of AI-powered research

Beyond individuals and organizations, AI is reimagining the very nature of research. AI-powered research is helping to break down global collaboration barriers, allowing professionals to work together in real time. The availability of advanced AI research tools to small startups and independent researchers on some level has also leveled the playing field. AI also creates real-time feedback loops, something that is so essential if we want to keep pace with the changes in the world. This shift is accelerating discoveries, and making research more collaborative, accessible, and adaptable. AI for research is dramatically altering industry landscapes, turning what was once an unhurried, isolated process into one that is nimble, and collaborative, making this data-driven journey easy for everyone.



[1] Drucker, P. F. (1999). Knowledge-Worker Productivity: The Biggest Challenge. California Management Review, 41(2), 79-94. https://doi.org/10.2307/41165987 

[2] Dataversity. (n.d.). A brief history of generative AI. Dataversity.  https://www.dataversity.net/a-brief-history-of-generative-ai/

[3] Gavi. "The Extraordinary Impact of Japanese Encephalitis Vaccines." VaccinesWork. August 2022.https://www.gavi.org/vaccineswork/routine-vaccines/extraordinary-impact-japanese-encephalitis#:~:text=Vaccine%20development,China%20by%2010%2Dfold%E2%80%9D.

[4] UCLA Health, The fastest vaccine in history, UCLA Health Newsroom, December 2020.https://www.uclahealth.org/news/article/the-fastest-vaccine-in-history.

[5] Chenrui et al, “Innovative applications of artificial intelligence during the COVID-19 pandemic”, Infectious Medicine, March 2024. https://www.sciencedirect.com/science/article/pii/S2772431X24000091

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The AI platform for applied sciences and deep tech teams.

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The AI platform for applied sciences and deep tech teams.

Copyright © 2024 Spotlight Labs Inc.