The integration of AI worldwide has undoubtedly changed how we will do things in the future. Research, in particular, will never be the same with the advent of AI, which can support researchers in ways they could have only imagined in the past.
While it’s unlikely that AI will take over the entire industry anytime soon, the question remains: Will AI being introduced more and more into research take away the skills and spark that make researchers so good at what they do? Will they become so reliant on technology that they won’t even bother to do the “easy” things in favour of allowing technology to do it for them?
Will AI make researchers complacent?
Learned Helplessness
While the entire degradation of a job role may seem extreme, this notion of learned helplessness raises valid concerns. Coined by psychologists Martin Seligman and Steven Maier, learned helplessness occurs when individuals become so accustomed to relying on external factors that they stop attempting to solve problems themselves, even when they can do so.
This could mean that researchers may gradually use technology so much in their daily lives, whether through choice or business direction, that they lose confidence in their skills. This may creep in slowly, but ultimately, they don’t feel able to analyse results without external support from technology.
When AI effortlessly processes vast amounts of information, formulates hypotheses, and even drafts conclusions, will human researchers start deferring to AI rather than critically engaging with their own work?
One example of how this has already happened in the world is the shift from mental arithmetic to calculators. Calculators are undoubtedly more efficient and less likely to give incorrect answers. This has led to a steady decline in general calculation skills across the globe.
Similarly, AI in research could lead to a growing dependence on automation, particularly where the world now often values the speed of results over everything.
Unquestioning Acceptance
As well as the potential degradation of skills if an algorithm identifies an anomaly or trend that appears inconsistent, will researchers critically assess the finding, or will they accept it at face value, simply because “the system said so”? AI’s efficiency and perceived authority can discourage deeper engagement with data, leading to a detachment from the analytical processes that define rigorous research.
Again, this raises concerns about the potential of those critical thinking skills. Sorting, analysing and interpreting data are not mechanical skills. They are an organic, collaborative process – with other researchers or even within a researcher’s mind.
If, in the future, researchers hand over too much control to AI, they could inadvertently weaken their ability to assess, validate and challenge findings.
This is a well-known concert, however, with The House of Lords Communications and Digital Committee releasing a review that highlights the concerns of this.
AI Can Make Us BETTER Researchers
However, despite these concerns, it’s more than likely that AI will make us better researchers if we use it as a tool and not a crutch.
While calculators may have diminished older skills, the chance of the average person to calculate numbers that would otherwise be done by mathematical savants increased. It became more accessible to all.
One of AI’s greatest contributions is its ability to automate time-consuming, repetitive tasks. A 2023 Bank of England survey found that companies investing in AI expect measurable productivity improvements within the next 2–3 years, particularly in automating routine tasks, optimising workflows, and enhancing data analysis.
The misconception that AI will lead to intellectual laziness overlooks a key reality: AI is most powerful when used as a collaborator, not a replacement.
Rather than turning research into something passive, it gives researchers the time, energy, and cognitive bandwidth to actually deep dive further into the results and even question in ways they wouldn’t have been able to in the past.
AI Can Improve Accuracy and Reduce Human Bias
Two of the biggest issues in any type of research are the influence of errors and preconceptions. No matter what type of research you are involved with, this is something that is a key issue.
AI can process massive datasets quickly and spot patterns that a human may not be able to detect. In addition, any AI won’t suffer from things like confirmation bias or even fatigue. Even when a team runs at full speed, the AI is unlikely to make huge mistakes that a human would. And while no AI is infallible, it’s easier to have the time to check things, and the researchers’ questioning should flush these out.
AI Encourages Strategic Thinking and More
While touched on already, the chance to free up more time is really the biggest win of AI in research. Researchers will find that they spend a lot of time dedicated to time-consuming admin and data entry. AI, and automation in general, gets rid of much of this. AI frees cognitive resources, allowing researchers to analyse deeper, tackle more complex questions, and drive innovation.
AI also enhances collaboration across market research and product development, making connecting consumer insights with business strategy easier. Integrating data from multiple sources, such as surveys, social listening, and customer behaviour tracking, provides a more comprehensive view of overall trends.
AI Is A Collaborative Tool, Not A Replacement
So, while complacency is a valid concern, it’s also one that we are aware of. Being able to know this and be aware of this creates opportunities for deeper dives, faster insights and more meaningful engagement with the data.
Tasks can be automated to the point of efficiency, allowing researchers to focus on things like critical thinking, creativity, and strategic decision-making based on experience.
AI is a powerful partner in research, when used effectively and efficiently can make research even more effective and allow researchers to work smarter, not lazier.
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