Artificial intelligence in scientific research: more efficiency, less exploration?
L'artificial intelligence In scientific research, it is dramatically increasing research productivity. At the same time, however, artificial intelligence raises questions about the diversity of topics being explored and the future trajectories of science.
A maxi study accepted by Nature A survey of 41 million publications shows how AI multiplies the impact of individual scientists, but narrows the overall focus of disciplines. A survey conducted by Wiley of 2,400 researchers adds a complementary perspective: strong enthusiasm for efficiency, coupled with concerns about errors, opacity, and a decline in critical thinking.
These two sources reveal a paradox: more efficiency, less exploration. A theme that concerns not only academia, but also businesses and digital marketing, where the same mechanisms of concentration and polarization are repeating themselves.
Artificial Intelligence in Scientific Research: How It's Changing Scientists' Work
In the last two years the adoption of artificial intelligence AI has accelerated in scientific research at an unprecedented pace. More and more researchers are using AI systems to analyze data, write papers, generate code, translate texts, run simulations, or automatically detect errors.
This isn't simply the automation of repetitive tasks. The massive integration of AI is impacting the very structure of scientific work, individual careers, and the direction in which entire disciplines are moving. The study accepted by Nature, titled "Artificial Intelligence Tools Expand Scientists' Impact but Contract Science's Focus," quantitatively analyzes 41 million articles published between 1980 and 2025.
In this study, artificial intelligence is treated as a structural variable: its effects on productivity, citation count, career progression, and discipline configuration are observed. In parallel, a survey by Wiley—a leading scientific publisher founded in 1807 and now active with over 1,600 peer-reviewed journals—collects the perceptions, stated practices, and concerns of researchers who use its publishing services.
The combination of these two perspectives—objective data and subjective experience—allows for a more complete picture of AI's impact on the knowledge system. This framework is also useful for companies integrating artificial intelligence into their decision-making and management processes. marketing automation.
The measurable benefits of AI for researchers' careers
The academic study measures with great precision the impact of the’artificial intelligence in scientific research on the work of individual scientists. The results are consistent across all six disciplinary fields analyzed, demonstrating a clear "career multiplier" effect.
On average, researchers who adopt AI tools:
- they publish 3.02 times more articles than their colleagues who don't use them;
- receive 4.84 times more citations;
- they become group leaders 1.37 years earlier;
- are significantly more likely to remain in the academy and advance through the ranks.
Artificial intelligence helps manage huge volumes of data, filter thousands of results, edit and revise texts more quickly, and discover correlations invisible to traditional tools. It's no surprise that, according to the Wiley survey, 621 of the 2,400 researchers interviewed already use AI tools in their daily work, a significant increase from 451 in 2024.
The survey doesn't measure actual outputs, but rather perceived adoption and the impact on daily work. Specifically, it shows that:
- the 85% feels an increase in efficiency;
- the 77% notices an increase in the amount of work handled;
- The 73% believes that AI improves the quality of results.
These subjective data are aligned with the objective findings of the academic study: the’artificial intelligence In scientific research, it is seen as a crucial competitive lever, especially by early-career scientists who need to quickly build their reputation.
The shrinking of exploratory space: the risk of the "streetlight"“
However, according to the data, the increase in individual efficiency corresponds to a contraction in thematic diversity. The authors of the study analyzed the semantic position of millions of articles in a high-dimensional vector space (SPECTER 2), observing how the’artificial intelligence in scientific research you influence the spectrum of knowledge explored.
Several signals emerge from the mapping:
- the “knowledge extent” of AI-assisted papers is 4.63% lower than that of traditional papers;
- the contraction is visible in more than 70% of the 200 disciplinary sub-fields analyzed;
- thematic distribution has less entropy: research tends to concentrate in clusters that are already mature and rich in data;
- the citation network shows a 22% reduction in interaction between works, with parallel trajectories interacting less with each other;
- a strong “star system” effect emerges: the 20% of AI-assisted papers receives the 80% of citations.
In practice, AI amplifies what is already strong. Models work best in fields with abundant, clean, and historical data: astrophysics, computational biology, medical imaging, materials science, computational chemistry. Less structured or more difficult-to-measure disciplines are left behind.
Science thus risks being concentrated "under the streetlight": it optimizes what is well-lit – where data is abundant – while neglecting the shadowy areas. This dynamic, already discussed in contexts such as data analysis and behavioral economics, recalls the tendency to seek solutions only where it is easier to measure (selection bias).
The Wiley survey, while not directly measuring thematic contraction, captures this concern in the responses of respondents, who fear a system driven too much by available datasets and less by scientific curiosity.
Smaller teams, less space for young people and impact on human capital
Another effect of the’artificial intelligence In scientific research, it concerns team composition. AI-assisted publishing groups tend to be smaller than traditional teams because some of the operational work done by junior researchers is absorbed by digital tools.
The consequences are significant:
- the presence of junior researchers decreases by 7%, reducing opportunities for on-the-job learning;
- the share of senior researchers (+5%) is increasing, as they are more capable of using AI as a lever for acceleration;
- Teams become more vertical and less formative, with a weakening of the pedagogical function of research.
This phenomenon is similar to what we're seeing in the labor market with the adoption of AI by companies. The widespread introduction of intelligent systems increases overall productivity, but reduces opportunities for junior professionals: operational or repetitive tasks are being automated, while the importance of highly skilled and strategic roles is growing.
This results in a more efficient but more polarized workforce, with less room for generational turnover. If young people aren't trained today, there will be a shortage of seniors tomorrow. This is a systemic risk that affects both research and companies that work with data, AI, and digital marketing.
Errors, opacity, and cognitive laziness: the dark side of AI
The Wiley survey also highlights researchers' concerns. 87% of respondents report concerns on several fronts related to the’artificial intelligence in scientific research. Among these:
- the possibility that models generate errors or hallucinations;
- the security and protection of sensitive data;
- the opacity of the training datasets;
- the risk of a non-critical use that reduces the capacity for analysis and the rigor of scientific reasoning.
A more subtle bias arises: the tendency to delegate to AI operations that should remain under the researcher's cognitive control, with the risk of weakening mental discipline and systematic doubt, the foundation of the scientific method. Mathematician Nigel Hitchin (University of Oxford) has highlighted this risk, highlighting the possibility of "cognitive laziness" induced by excessive trust in models.
Recent studies also show how the extensive use of generative tools can reduce brain activation, linguistic variety and the ability to write independently without assistance (analysis on AI and cognitive abilities). We are talking about cognitive offloadingBy delegating demanding cognitive processes to machines, we risk weakening the very faculties that science and business need most.
Connected to this is the concept of brainrot It describes a broader cognitive impairment: shorter, shallower, less structured thoughts; a reduction in the ability to sustain attention, develop original ideas, and sustain complex analytical processes over time.
The paradox of individual efficiency and collective weakness
From the comparison between quantitative data and surveys, a structural paradox emerges in the use of’artificial intelligence in scientific research. On the one hand, AI amplifies the ability of individuals to impact their field, accelerates production times, increases scientific visibility, makes data management more efficient, and facilitates the identification of hidden patterns.
On the other hand, the same technology tends to restrict the overall scope of research. The community is pushed toward already consolidated territories, where data is abundant and algorithms offer the best performance. Increased individual productivity does not automatically equate to an expansion of collective exploration space.
The challenge of the coming years will be to understand whether AI can become not only an accelerator of efficiency, but also an ally capable of withstanding doubt, the slow pace of exploration, and the courage to venture off the beaten track. This applies to science and, by analogy, to all data-driven decisions in companies.
Artificial Intelligence in Scientific Research: Scenarios and Risks of Polarization
If the’artificial intelligence In scientific research, the trend tends to favor data-rich disciplines, leading to a realignment between the geography of science and the geography of large technological platforms. The idea of a "knowledge cartel" is emerging: domains capable of feeding models with large datasets become more fundable, publishable, and visible.
The boundaries between what is scientifically relevant and what is technically expedient risk blurring. Research remains vibrant, but increasingly concentrated in a few clusters of knowledge, while entire disciplinary areas may slip into a gray area.
Alongside this scenario, however, there are other possible ones:
Scenario 2 – New senses for science
In a more positive scenario, AI doesn't just process existing data, but also allows for the generation of new data. Autonomous robots in the laboratory, intelligent sensors, advanced simulation platforms, and models capable of proposing novel experiments are expanding the scope of what can be known.
Artificial intelligence thus becomes an extension of the scientific senses, not just calculation. Today's marginal disciplines can regain space thanks to tools that make what was previously unmeasurable measurable.
Scenario 3 – Science rediscovers its cognitive center of gravity
A third scenario sees the system react to the risks of cognitive laziness and brainrot. AI is integrated into educational programs as a tool to train—not replace—critical thinking. Students learn to use models, but above all to verify, check, and refute them.
Technology becomes a cognitive tutor that strengthens reasoning skills, encapsulated in an "ethic of attention" and understanding. This approach can be replicated by companies in internal training on AI, data analysis, and marketing data-driven.
Artificial Intelligence in Scientific Research: Impact on Marketing and Business
The dynamics observed with the’artificial intelligence In scientific research, they are very similar to those transforming digital marketing and business. Here too, AI increases efficiency, speed, and data management capabilities, but introduces risks of concentration and standardization.
In marketing, models tend to favor data-rich segments: highly active customers, already mature markets, highly traceable digital channels such as email, social and WhatsApp Business. This can lead to campaigns that are highly optimized in the short term, but less exploratory of new segments, products, or markets.
Companies that only use what algorithms “see” risk a lampoon effect similar to that of science: focusing on what is easily measurable and neglecting what requires creativity, qualitative research, and experimentation not guided by historical data.
At the same time, AI in systems marketing automation allows you to:
- personalize communications on a large scale;
- dynamically segment leads;
- predict purchase propensity and churn;
- optimize contact times, channels and content.
The key is balancing efficiency and exploration: using models to enhance what works, while constantly testing new ideas, new touchpoints, and new brand narratives. In this sense, conversational channels like WhatsApp Business, supported by automation platforms, become essential for maintaining a human and qualitative connection with customers.
A mature approach to AI in the company should draw inspiration from lessons learned from scientific research: monitoring not only productivity KPIs, but also the diversity of strategies, the quality of internal critical thinking, and the development of junior profiles' skills.
How SendApp Can Help with Artificial Intelligence in Scientific Research (and Business)
The evolutions of the’artificial intelligence Scientific research shows how strategic it is to integrate AI in a controlled, transparent manner, and geared toward human capital growth. The same applies to companies looking to use AI in communications and marketing, particularly on highly relational channels like WhatsApp Business.
SendApp It was created to help businesses and professionals transform conversations into value, responsibly leveraging automation, integration with existing systems, and the potential of AI.
Official WhatsApp API and AI Integration
With SendApp Official Companies can access the official WhatsApp APIs in a structured way. This allows them to connect the WhatsApp channel to their AI models and CRMs, managing:
- transactional notifications and automatic updates;
- nurturing flows supported by scoring and segmentation algorithms;
- intelligent automatic responses based on internal knowledge bases.
As in research, the goal here is not to replace people, but to increase their ability to manage large volumes of interactions without losing quality.
Omnichannel team management and conversations
SendApp Agent It allows you to orchestrate the work of your sales, support, and customer care teams on a single dashboard. You can distribute conversations, assign tickets, monitor performance, and integrate AI logic into contact prioritization or suggested responses.
This avoids one of the risks that has emerged in the use of AI in research: the reduction of opportunities for junior profiles. Automation handles repetitive tasks, while operators can focus on complex cases, learning from data and interactions.
Advanced automation and cloud scalability
With SendApp Cloud Businesses can set up advanced automation flows on WhatsApp Business: acquisition funnels, onboarding sequences, dynamic reminders, segmented campaigns based on events and behaviors.
These flows can be fueled by AI models that analyze interaction data to optimize timing, content, and paths. Unlike a closed and polarized scenario, the platform allows you to design true "marketing experiments," testing different messages, segments, and journeys in a controlled manner.
For companies that want to bring the best lessons from the’artificial intelligence In scientific research – efficiency yes, but without sacrificing exploration and critical thinking – the SendApp ecosystem offers a solid, scalable foundation that complies with official WhatsApp guidelines.
If you're considering integrating AI and automation into your WhatsApp Business communications strategy, you can request a dedicated consultation or activate a trial of SendApp solutions to see the impact on your marketing, sales, and support processes firsthand.
