From AI Assistance to AI Operations: Takeaways from Eight Trend Reports for Scholarly Publishing

by Hong Zhou, VP of Product Management

One habit I’ve built over time is reading outside our own lane. Scholarly publishing and education have their own constraints, of course—but technology waves rarely start here. They show up first in sectors that live and die by speed, automation, and customer experience: financial services, commerce, and especially marketing. When marketing leaders start redesigning for agent-mediated discovery, or when enterprises restructure around autonomous operations, it’s usually a preview of what will land—just with different risk tolerances—in our industry.

With that lens, I recently read eight impactful technology trend reports from Bain, CB Insights, Deloitte, Gartner, IBM, McKinsey, Kantar, and SaM. What surprised me wasn’t any single prediction. It was the consistency of the underlying shift across very different authors and audiences. The same message surfaced repeatedly:

We’re moving from AI as a feature to AI as an operating layer—one that executes work, routes exceptions, and enforces policy across systems.

For scholarly publishing and education, that shift matters because our value is created in workflows: submissions, peer review support, production quality loops, metadata enrichment, content accessibility, assessment integrity, learner support. These are multi-step, exception-heavy processes where coordination costs are often higher than the task itself.

What’s changing is not just that models are better. It’s that agents can now act—and increasingly, teams of specialized agents can coordinate. But the reports are also clear about a hard truth: agent success is less about “cool capability” and more about operational design. The organizations that win won’t be the ones with the most pilots; they’ll be the ones that redesign end-to-end workflows, build measurable controls, and scale responsibly.

Here are five implications I believe the scholarly publishing industry should take seriously.

1) Redesign beats automation.

The fastest path to disappointment is automating a broken process. Agents amplify whatever you give them—clarity or chaos. The real unlock is mapping workflows as value streams, classifying exceptions, and deciding where humans remain the authority.

2) Trust is becoming infrastructure.

In scholarly and learning ecosystems, trust is not branding—it’s adoption. Provenance (what changed, when, and why), explainability (how decisions were made), and AI security (preventing leakage and rogue actions) are moving from “nice to have” to “table stakes.”

3) ROI must move from stories to outcomes.

Several reports highlight that organizations still measure AI mostly through time saved, while struggling to tie it to business value. In our world, value often shows up as fewer rework loops, earlier risk detection, improved compliance, and better customer confidence—not just faster throughput and integrity detection. If we don’t measure those outcomes, we’ll create activity without impact.

4) Sovereignty and computational economics will shape what can scale.

AI demand is colliding with real constraints: cost, power, and regional requirements. That pushes us toward model optionality, flexible deployment patterns, and cost-aware design—especially for high-volume tasks like screening, enrichment, and personalization.

5) Discovery is moving into AI answer engines.

A few reports made this point sharply: the “storefront” is disappearing. People (and their agents) increasingly ask an AI system to choose. That means publishers and learning providers will need to publish not only for human readers, but for machine selection: richer semantic metadata, clear rights signals, accessibility indicators, faithful structured summaries, and provenance by design.

The practical next steps are not “add more AI.” For me they’re:

• Make workflows agent-ready: decompose tasks, define escalation, and instrument outcomes (time, quality, risk).
• Build trust-by-design: provenance, audit trails, and security controls as default deliverables.
• Prepare for AI-mediated discovery: treat metadata and content structure as strategic assets, not downstream hygiene.

The biggest shift isn’t that AI can write more text and create more content. It’s that AI is starting to make knowledge executable—and that changes what “good operations” and “good content” look like.

KnowledgeWorks Global Ltd. (KGL) is the industry leader in editorial, production, online hosting, and transformative services for every stage of the content lifecycle. We are your source for society services, research integrity, intelligent automation, digital delivery, and more. Email us at info@kwglobal.com.