In the 2026 Publishing Tech Trends report, we asked industry leaders what they see as the number one challenge for digital content publications in the year to come. Instead of one single problem, they landed on something more unsettling: you're losing control of discovery, measurement, and infrastructure all at once.
And unlike losing your keys, you can't just retrace your steps to find them.
When Discovery Happens Without You
For two decades, digital publishing strategy meant one thing: optimize for search. Rank well, drive traffic, convert usage into subscription value. It was clean, linear, and measurable.
That world is over.
Discovery increasingly happens inside AI systems that summarize, synthesize, and answer questions without ever sending users to your platform.
Nicholas Liu, Lead Strategic Analyst for AI at Oxford University Press, describes the shift bluntly: "Avoiding becoming an 'aggregated imprint.' We are seeing a massive shift from SEO to a GEO focus as publishers will want to meet users where they are: at the chatbots."
If your brand becomes invisible inside AI-mediated discovery, you risk becoming raw material instead of a destination. Your carefully curated content gets summarized, mashed up with a dozen other sources, and served to researchers who won’t see that it’s yours.
Michael Di Natale, Director of Journal Production and Platform at AACR, highlights the core tension: "Finding ways to take advantage of emerging discovery tools that protect the value of publisher content as users adopt GenAI as a replacement to traditional search."
Notice that word: protect. Not "leverage" or "optimize." Protect. Because discovery is decentralizing, and the challenge isn't just showing up in AI answers—it's making sure that presence actually translates into attribution, value, and revenue.
The Measurement Problem No One Wants to Talk About
When users stop clicking through to your platform, how do you measure anything?
Nancy Roberts, Chief Operating Officer at Maverick Publishing Specialists, frames it starkly: "Remaining relevant in a Google Zero world. When AI summarizes content from across the web and users no longer need to click through to a platform, it will be a challenge to measure impact and to ensure that what appears in these summaries is verifiable, trusted content."
For scholarly publishers, usage data has always underpinned subscription renewals, pricing models, and institutional relationships. But what happens when the article is never clicked? When the summary gets generated elsewhere? When an AI agent quietly digests 200 papers at 2 a.m. and no COUNTER report captures it?
Traditional metrics break down. And if you can't measure usage, how do you prove value? How do you price access? How do you renew subscriptions?
Publishers are already exploring solutions. Some are developing new measurement frameworks that go beyond old metrics – tracking engagement signals, measuring content influence inside AI systems, and building intelligence around how their content actually gets used. Others are building authenticated access tiers that make machine usage visible and monetizable.The challenge isn't that measurement is impossible in an AI world—it's that it requires different instrumentation.
Your Systems Are Drowning (And They Know It)
Even if discovery were stable and measurement were working, your infrastructure probably still needs improvement.
Jeremy Little, AI Technology Lead at Silverchair, describes the scale of the problem: "Volume is becoming an existential challenge on multiple fronts. We're seeing 30-50% increases in submission rates from AI-assisted research while simultaneously watching bot traffic increase by orders of magnitude. Infrastructure that was built for thousands of human readers is suddenly handling millions of automated requests."
Bot traffic is increasing by orders of magnitude. Systems that were designed for human behavior are now supporting machine ecosystems that operate at speeds and scales you never planned for.
Publishers who are investing in scalable infrastructure, implementing intelligent traffic management, and designing systems with machine consumption in mind are handling this transition. The key is recognizing that infrastructure built for human traffic patterns needs fundamental rearchitecting, not just incremental upgrades.
This means:
- Building API-first architectures that can handle automated requests efficiently
- Implementing smart rate limiting and authentication frameworks
- Partnering with platform providers who've already solved for machine-scale traffic
- Treating infrastructure investment as a strategic priority, not a cost center
Trust Is the Only Moat That Matters
AI is accelerating research workflows. Unfortunately, it's also accelerating bad behavior.
James Butcher, Director of Journalology, doesn't sugarcoat it: "Trust is everything. Lose that and reputation and revenue disappear. We are living through a perfect storm. Academics need to publish or perish. AI allows researchers to generate slop at scale. Publishers can generate revenue by publishing more articles. Maintaining research integrity is mission critical for publishers, who ignore it at their peril."
Submission volume can boost short-term revenue, but integrity failures can destroy long-term viability. You can make money publishing more, or you can maintain the trust that makes your content valuable in the first place. Pick one.
Teo Pulvirenti, Vice President of Global Editorial Strategy at ACS Publications, adds the operational reality: "The biggest challenge is definitely preserving research integrity in an AI-driven world. AI is transforming peer review and editorial workflows for the better—but it also introduces a critical threat: policing AI-generated content and safeguarding research integrity. The challenge for 2026 is clear: leverage AI's benefits without compromising trust, visibility, or financial viability for publishers."
Solutions like Hum’s Alchemist Review are delivering AI-powered manuscript screening and editorial intelligence that help scale integrity without sacrificing speed—catching problematic submissions early while supporting legitimate research.
In other words, AI is both the opportunity and the threat. And scholarly publishers don't win by publishing more—they win by publishing better and being able to prove it.
The Real Challenge: Value Without a Platform
If we zoom out, the core challenge for digital content publications in 2026 isn't "AI disruption." It's something more fundamental:
How do you capture value when discovery, usage, and consumption no longer happen inside your platform?
Traffic isn't the currency anymore. Clicks aren't the clean metric. Destination platforms aren't the default. The old rules don't apply, and the new ones haven't been written yet.
Publishers now need to think in terms of authenticated pathways, controlled access, machine-readable licensing, attribution frameworks for AI consumption, and integrity as economic strategy. This isn't a single problem to solve—it's a system-wide recalibration.
Publishers are shifting toward AI-ready metadata, infrastructure modernization, and strategic partnerships. Because the challenge isn't adopting AI. It's building a publishing business that still works because of AI.
What This Means for You
The number one challenge for digital content publications in 2026 isn't AI adoption. It's staying relevant, trusted, measurable, and financially viable in an environment where discovery happens away from your platform, usage is becoming invisible to traditional metrics, infrastructure is creaking under machine-scale traffic, and research integrity is at risk.
Publishers need to prioritize:
- Treating research integrity as revenue protection — Building robust peer review and screening processes that scale with AI tools
- Redesigning infrastructure for machine traffic — Partnering with platforms and investing in architectures built for this reality
- Rethinking measurement beyond human clicks — Developing new frameworks that capture AI-mediated consumption
- Building new models for value capture — Creating authenticated pathways and licensing structures for AI ecosystems
In short, 2026 isn't about fighting AI. It's about architecting a publishing business that still works when AI reshapes everything around it.