How big academic publishers use AI

How Big Academic Publishers Use AI

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Table of Contents

Introduction

The academic publishing industry has long been proficient in two key areas: controlling access to scholarly knowledge and profiting from it. With the rapid advancement of artificial intelligence, big academic publishers aren’t merely adapting; they’re taking the lead. AI is no longer a toy for experimental labs. It’s a comprehensive strategy, a fundamental infrastructure that is embedded in how companies like Elsevier, Springer Nature, Wiley, and Taylor & Francis operate today.

Academic publishers have been accused for years of putting profits over openness. Now, those same publishers are deploying AI across the scholarly communication pipeline. Manuscript screening, reviewer matching, metadata enrichment, plagiarism detection, content discoverability, and even citation forecasting are increasingly managed or enhanced by AI systems. This isn’t a sprinkle of automation here and there. This is a reengineering of the publishing process.

While many of these tools bring obvious benefits—speed, consistency, cost savings—they also concentrate power. Publishers now control not just access to knowledge, but also the systems that process, filter, and elevate it. As we trace the evolution of AI in academic publishing, one thing becomes clear: this isn’t just a technological shift. It’s a transformation of the very architecture of scholarly communication.

AI in Editorial Workflows

In the past, academic journal workflows were time-consuming and plagued by inefficiencies. Editors would sift through hundreds of submissions, manually match reviewers, and wait months for feedback. Peer review turnaround times were unpredictable. Quality control varied wildly. Enter AI.

Elsevier’s “Reviewer Recommender” system is a clear example of how AI tools are being used to streamline and support editorial workflows in academic publishing. It uses machine learning to analyze the topic, language, citations, and structure of submitted manuscripts, then matches them to a global database of researchers. It learns from prior review activity and editorial decisions, becoming more accurate with time. Elsevier reports that its AI-driven editorial tools, including Reviewer Recommender, have helped reduce reviewer identification time by up to 50% in some cases.

Springer Nature’s SNAPP (Springer Nature Article Processing Platform) is another major player. It uses AI to conduct language checks, reference verification, and initial content screening before an editor touches the file. Authors receive an automated report on the manuscript’s readability, citation compliance, and potential issues within minutes of submission. This not only saves time but also reduces desk rejections for technical reasons.

Wiley, meanwhile, has piloted editorial bots that assist in first-pass triage, assessing whether manuscripts meet basic scope and formatting requirements. They’ve even explored using neural networks to predict peer review outcomes based on early submission data. While still evolving, these tools illustrate the shift: AI is becoming the assistant editor many journals never had.

Historically, editorial decision-making was slow, opaque, and deeply human. That’s changing. And while these systems bring efficiencies, they also raise uncomfortable questions: Are we trusting AI too much to make judgment calls about what counts as valid science? And whose standards are these algorithms really enforcing?

Enhancing Discoverability and Citation Impact

Publishers understand the value of visibility. An unread paper is a wasted investment. That’s why AI-driven discoverability is now a top priority. Through natural language processing, machine learning, and data mining, publishers are using AI to ensure articles are found, cited, and tracked across the digital landscape.

Elsevier’s Scopus doesn’t just index papers. It uses AI to classify documents, link citations, and build author profiles. It can suggest relevant papers based on abstract similarity, predict future citations, and even map scientific trends in near real-time. This predictive capacity makes Scopus not just a discovery tool but a strategic asset for researchers and institutions.

Clarivate, through its Web of Science platform, also deploys AI for clustering related papers, tracking impact, and curating thematic collections. Their AI tools help identify emerging fields, under-cited work, and influential authors. These capabilities directly influence where researchers publish, whom they cite, and how journals rank.

Of course, this AI-driven visibility isn’t equally distributed. The algorithms tend to favor English-language publications, highly cited authors, and journals from established Western publishers. As a result, scholars from the Global South or working in niche disciplines may struggle to break through the algorithmic fog.

Automated Peer Review and Quality Control

Peer review has long been hailed as the cornerstone of academic integrity. But it’s also a bottleneck. Reviewers are overworked, editors are overwhelmed, and inconsistent quality is the norm. AI is stepping in to streamline this chaos—but with mixed results.

Elsevier utilizes AI-driven tools to evaluate manuscripts for clarity, originality, and potential impact, generating what it refers to as “manuscript readiness indicators.” These indicators enable editors to quickly determine whether a submission should proceed to peer review or be rejected outright. While Elsevier emphasizes that these tools do not replace peer review, they are used to streamline editorial triage and reduce delays in the review process.

Taylor & Francis, in collaboration with UNSILO (a Danish AI company), has developed tools to assess manuscript structure, keyword quality, and coherence automatically. These tools generate scores that guide editorial decisions. Wiley has been exploring reviewer sentiment analysis, using AI to evaluate the tone and helpfulness of reviewer comments.

One particularly controversial use of AI is in reviewer evaluation. Some publishers now assign reviewers a reliability or consistency score based on past behavior. Those with low scores may be deprioritized or even removed from the reviewer pool. While this can weed out bad-faith actors, it also risks punishing unconventional reviewers or those new to the system.

Peer review automation saves time, but it shifts the burden of scrutiny onto algorithms that are often opaque and rarely transparent. What if a high-quality, field-defining paper is flagged as an outlier and quietly sidelined? The risk is not hypothetical. In fields like climate science or gender studies, where ideas often challenge dominant paradigms, algorithmic conservatism can become a form of intellectual gatekeeping.

Data Harvesting and Analytics

Academic publishers are no longer just disseminators of content; they’ve become data analytics companies. In its 2023 annual report, RELX—the parent company of Elsevier—revealed that more than 60% of its total revenue came from analytics, decision tools, and databases. Through platforms like Scopus, ScienceDirect, and Mendeley, Elsevier collects vast quantities of data on manuscripts, citations, downloads, and reading behavior, creating an enormous reservoir of research intelligence.

SciVal, Elsevier’s flagship analytics product, mines Scopus and other databases to generate reports on institutional productivity, research impact, collaboration networks, and funding success. Universities utilize it to shape hiring strategies, allocate grants, and benchmark their performance against competitors. It’s powerful—and expensive.

Springer Nature has its analytics suite, offering performance dashboards to funders and institutions. Clarivate’s InCites platform delivers similar services, promising “actionable insights” drawn from publication metrics. These tools utilize AI to identify emerging stars, track disciplinary shifts, and predict future breakthroughs.

But here’s the twist: the data used to power these tools comes from researchers who were never paid, often publishing in journals their libraries already pay to access. The value extracted far exceeds what most researchers see in return. This is surveillance capitalism with a scholarly twist.

The shift from content provider to data monopolist has implications far beyond publishing. When publishers also become arbiters of performance and prestige, they control the metrics by which academia governs itself. AI is the driving force behind this consolidation.

AI for Language Editing and Formatting

Not all AI uses are headline-grabbing. Some are quietly transformative. Language editing and formatting, once the domain of freelance editors and in-house production staff, are now being streamlined through AI.

Writefull, a language editing tool powered by NLP and deep learning, is integrated into Springer Nature’s submission systems to help authors improve grammar, style, and technical compliance with journal guidelines. Authors get instant feedback, often before peer review begins.

Wiley offers similar tools through its Author Services platform, including automated reference checks, figure quality assessment, and citation formatting. Elsevier’s “Language Editing Services” now includes AI-driven tools for rewriting awkward phrasing and improving academic tone.

These services are particularly valuable for non-native English speakers. A 2020 study estimated that more than 35% of submissions to English-language journals are authored by researchers writing in a second or third language, with some disciplines reporting figures as high as 60% depending on geographic region and field of study. For these scholars, AI tools can be the difference between desk rejection and complete review.

But there’s a downside too. As AI normalizes a particular kind of academic English, it may marginalize diverse writing styles or locally grounded scholarship. The unspoken assumption is that all good science sounds as if it were written at Oxbridge.

Monetizing AI-Driven Services

AI isn’t just a backend enhancement. It’s a product line. Major publishers are now selling AI-driven tools directly to researchers, institutions, and governments.

Elsevier’s Research Intelligence suite, which includes tools such as Pure, SciVal, and Expert Lookup, is marketed as essential for informed strategic decision-making. These tools aggregate data from multiple sources, run predictive models, and generate institutional dashboards. They cost tens or even hundreds of thousands of dollars annually.

Wiley’s Author Services includes premium AI editing, submission optimization, and post-acceptance production tracking. Springer Nature’s In Review platform enables authors to view peer review progress in real-time, utilizing AI to generate updates on the likelihood of reviewer responses.

Even Clarivate has jumped into the fray, expanding its acquisition of AI companies that offer workflow automation and grant discovery tools. Their goal is straightforward: to transform every step of the research journey into a monetizable service.

This SaaS-ification of academic publishing might be efficient, but it also deepens dependency. Institutions that invest in these ecosystems often struggle to exit. Data portability is limited. Pricing is opaque. And because each tool offers proprietary insights, comparisons across platforms are nearly impossible.

Ethical Concerns and Power Asymmetry

With great data comes great responsibility, or at least it should. The ethical challenges of AI in academic publishing are vast. Bias, transparency, and accountability are significant concerns.

AI systems are trained on historical data that reflects existing biases in academia. Suppose most highly cited authors in a field are male, from elite institutions in North America and Europe. In that case, the algorithm is likely to perpetuate this bias in visibility and recommendation tools. The result? A feedback loop that amplifies inequality.

The opacity of AI models compounds the problem. Publishers treat these tools as trade secrets. Researchers often don’t know how their manuscripts were triaged, what metrics determined rejection, or why a competitor’s paper was promoted in a recommendation engine.

Worse, there is almost no external audit of these systems. No third-party validation. No appeals process. If your work is misclassified, wrongly flagged for plagiarism, or suppressed by an algorithm, your recourse is limited.

And then there’s the broader issue of power asymmetry. Publishers have positioned themselves as indispensable intermediaries. But they now control more than the journals. They control the platforms, the algorithms, the data flows, and the tools used to judge scholarly worth.

Conclusion

Artificial intelligence has become the invisible scaffolding of modern academic publishing. From submission to review to dissemination, AI is shaping the flow of scholarly knowledge. It offers speed, efficiency, and scalability. But it also reinforces centralization, exacerbates bias, and commodifies the research process.

The big publishers didn’t invent AI. But they have mastered the art of using it to entrench their dominance. What began as a digital transition has evolved into a data takeover, with AI at the forefront. The tools may be new, but the game remains the same: control the content, control the system, control the profit.

If academia wants to reclaim its autonomy, it must engage more critically with the AI infrastructures now embedded in scholarly publishing. That means demanding transparency, supporting open infrastructure, and building alternatives that prioritize access over algorithms.

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