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News & Market INSIGHTS
eLearning has been evolving for some time and, like many things, this evolution is being accelerated by AI. As static courses and content repositories shift rapidly into intelligent, personalized learning experiences that target learner needs, integrate into workflows, and deliver measurable outcomes, publishers are determining how to adapt. This evolution represents a significant opportunity for content creators. Organizations possess the trusted content, subject matter expertise, and audience relationships needed to power next-generation learning experiences . The challenge is transforming existing assets into scalable, AI-enabled learning businesses. Evolving Learner Expectations Today’s learners increasingly expect experiences that are dynamic, searchable, interactive, and available within the flow of their work. AI accelerates capabilities for personalization, adaptive learning pathways, assessment, and credentialing. At the same time, publishers and associations face growing pressure to diversify revenue streams, strengthen audience engagement, and demonstrate ongoing value to professional and academic communities . Many organizations possess valuable assets across journals, books, certification programs, conferences, guidelines, and more. However, those assets often remain siloed within discreet platforms, products, and teams. The opportunity is clear. Breaking down those silos once and for all to develop products and services based on user need rather than organizational structure is required to take advantage of the current opportunities. From Static Content to Intelligent Learning AI enables publishers to offer existing content in entirely new ways. Trusted content can now support personalized learning pathways, microlearning modules, conversational interfaces, AI-assisted assessments, real-time recommendations, and skills-based credentialing experiences, creating the opportunity for publishers to increasingly support measurable learning outcomes. Nearly every publisher or professional society owns content and expertise that supports education and learning. Clinical journals, technical references, standards, conference content, guidelines, and scholarly books all provide foundations for AI-enabled learning experiences. Users also increasingly expect seamless, consumer-grade experiences that are mobile, personalized, and data-rich. The instructional value, enabled by the proper technologies enhances skill development and mastery, underpins credential attainment, and supports career advancement. Why Publishers and Societies Are Well Positioned As generative AI expands, trusted content is still the coin of the realm. AI-enabled learning systems require authoritative, domain-specific content to produce reliable, high-quality experiences. Publishers and societies already possess what many technology providers lack: curated expertise, editorial rigor, professional credibility, and established audience trust. This creates a meaningful strategic advantage. Organizations that successfully integrate AI-enabled learning into their portfolios can deepen audience engagement, establish stronger relationships earlier in professional careers, and expand long-term customer and member value . Subscription learning models, certification ecosystems, and continuing education programs can also create more durable and recurring revenue streams. The Strategy and Technology Gap The opportunity to leverage AI in eLearning is significant if your organization is operationally prepared to execute against it. Content may exist across siloed systems. Metadata may not be structured to support adaptive delivery or credentialing. Legacy platforms may lack interoperability, analytics frameworks, personalization capabilities, or scalable AI integration. And then there is the strategy. Organizations must answer broader strategic questions such as: How do we create learning experiences that align with evolving learner expectations and workflows? Which content assets are best positioned for transformation? What business models support sustainable growth? Should capabilities be built, partnered, or acquired? How should AI and eLearning integrate with and support existing publishing and product strategies? Successfully addressing these questions requires strategic alignment and the adoption of fit-for-purpose enabling technology infrastructure. This is where Delta Think, now with the technology power of Impelsys, provides a differentiated and powerful approach. Delta Think has been successfully working with publishers, societies, and information providers to define AI-enabled eLearning strategies aligned to audience needs, market opportunities, monetization models, and long-term goals. Together with Impelsys, we can now go further and provide the technology assessment and foundation needed to operationalize strategy through AI-enabled content delivery, personalization, analytics, interoperability, credentialing workflows, and scalable digital learning platforms. Looking Ahead AI has already begun to redefine what learners expect and what eLearning can deliver. Publishers and societies that move now have an opportunity to transform trusted content into scalable, intelligent learning experiences that deepen engagement, expand revenue opportunities, and strengthen long-term relevance. However, success requires aligning strategy, content, technology, and business models to create sustainable learning ecosystems. Delta Think helps organizations bridge strategy and execution, combining market insight and business planning with the technology infrastructure needed to build and scale next-generation eLearning solutions. Ready to get the conversation started? Reach out today .
Many libraries are discovering that APC management is evolving from operational tracking into institutional analysis of publishing economics to inform strategy development. To date, APC-related workflows have focused primarily on operational needs: processing payments, tracking compliance, or monitoring publishing activity under institutional agreements. However, the questions libraries are being asked now are increasingly financial and strategic: How much are researchers spending to publish? Are institutional agreements producing measurable economic value? In what subject areas are APC costs rising fastest? How do institutional publishing patterns compare with broader market behavior? How can libraries better support authors navigating an increasingly complex journal landscape? Current institutional systems are not designed to answer these questions comprehensively. Publisher dashboards, finance systems, repositories, and bibliometric tools each provide partial visibility, rather than holistic, normalized APC pricing intelligence or longitudinal market context. As a result, many libraries are beginning to combine internal institutional reporting with external market datasets and benchmarking tools. APC Spend Is Becoming a Strategic Institutional Metric APC tracking is often decentralized and incomplete, particularly when payments originate from multiple budgets, departments, grants, or publisher agreements. As OA participation grows and publishing costs and associated models continue to diversify, scaling that approach becomes increasingly difficult. Recent APC market behavior highlights why this analysis matters. While average APCs continue to rise, the more significant trend may be the widening spread of pricing across journal portfolios. In practice, this means institutions can experience meaningful cost increases even when publishing output remains relatively stable. This is driving greater interest in analyses that move beyond aggregate APC totals to examine: Publisher-level APC pricing behavior Subject-specific APC pricing and trends over time Hybrid versus fully OA pricing dynamics Concentration of spend within specific publisher portfolios Longitudinal changes in publishing activity and output Delta Think’s Data & Analytics Tool (DAT) enables libraries to benchmark institutional APC activity against longitudinal market pricing trends across publishers, journals, and disciplines. By normalizing APC pricing data over time, institutions with subscription access to DAT identify cost concentration, track pricing movement, and better understand how local publishing activity compares with broader market behavior. For example, the chart below tracks APC spending and popular price bands from 2019-2026:
Overview The US National Institutes of Health (NIH) has proposed caps on allowable publication costs, including article publishing charges (APCs) for NIH-funded research. Given the NIH’s scale, how might this policy affect the scholarly publishing market? Could publishers lose revenue? Could funders save costs? What is the potential net economic impact on the research and scholarly ecosystem? Background In summer 2025, the NIH announced an intent to establish new policies limiting allowable publication costs levied against papers arising from NIH-funded research and invited feedback to its proposals. The recently published responses naturally attracted some attention and engagement from a diverse group of stakeholders. The NIH proposed five options, among them disallowing all publication costs, price caps per publication, and limiting publication costs to a fixed percentage of the research grant’s direct costs. The NIH’s proposal cited some research into average publication costs. The analysis looked at journal APCs as listed in the Directory of Open Access Journals (DOAJ), averaging from $1,236 to $2,177. It also analyzed budget requests from its grants, with costs per publication averaging between $2,565 and $3,647. For publishers, these proposals are about more than compliance, it’s about understanding revenue risk, pressure points across portfolios, and where pricing strategies may need to adapt . For libraries, consortia, and funders, it’s about anticipating shifts in spend, benchmarking agreements, and planning for sustainable access models in a constrained environment . Our analysis What effects might the proposals have on total prices paid if implemented? To examine this, we combined data from our Data & Analytics tool with numbers from the NIH’s proposed caps. We also needed to handle a couple of nuances in the NIH’s analysis: The DOAJ lists only fully open access (“gold”) journals. However, under current NIH policy, authors may also publish in hybrid journals 1 . Fully open APCs are typically less expensive than hybrid ones, so we need to include both in our analysis. Quantifying the proposal for a fixed 0.8% of grants’ direct costs presents a challenge as the the figures for these are not published. We estimated the applicable costs by combining figures from NIH reports and statements 2 . Results We took the current APCs multiplied by corresponding volumes of articles as a baseline, allowing for typical discounting that we have observed. We then calculated the change from the baseline for some of the NIH’s proposed options noted above. The results are shown in the chart below.
