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Broadcasters want AI that makes production and newsroom work more useful, efficient and resilient—not automation for its own sake. Current use remains more limited than expectations of future impact, and any gains depend on reliable workflows, skilled staff, editorial control and audience trust.
How much are broadcasters using AI now?
Haivision’s 2026 Broadcast Transformation Report surveyed more than 1,300 broadcast professionals worldwide. Its findings point to a gap between current use and expectations: respondents reported some AI use, but many expect it to have a larger effect on production in the years ahead. These are survey responses, not a measure of results across every broadcaster. Haivision’s 2026 report does not show that anticipated impact has already been achieved.
Reuters Institute’s 2026 study offers a complementary view of newsrooms and media publishers, rather than a broadcaster-only sample. It describes AI initiatives that are often useful but still developing, which is a reminder to separate a promising pilot from a proven change in editorial or business performance. Reuters Institute’s report also discusses accuracy concerns around news provided by chatbots.
What the reported figures say
| Source and sample | Reported finding | What it indicates |
|---|---|---|
| Haivision, 2026 survey of more than 1,300 broadcast professionals worldwide | 27% reported AI use, compared with 25% in Haivision’s 2025 report; 64% expected AI to have the greatest impact on broadcast production over the next five years. | Reported use and expectations are different measures; the forecast is not evidence of realized impact. |
| Haivision, 2026 survey | 41% named remote production their leading technology priority for 2026. | AI expectations sit alongside core production modernization priorities. |
| Reuters Institute, 2026 survey of media leaders and publishers | 44% described current newsroom AI initiatives as promising, 42% as limited and 13% as transformational. | These are newsroom assessments, not broadcaster-wide outcome measures. |
| Reuters Institute, 2026 survey | 64% identified back-end automation, 82% newsgathering and 81% coding or product development as important AI use cases. | Respondents see potential across operational support, reporting and product work. |
| Local Media Association article reporting a 2026 survey with Trusting News of more than 1,400 news consumers | 98.8% wanted a human to retain oversight of AI use for news; 30% expressed a baseline belief that AI should never be used in news. | The results indicate strong preferences in this survey, not universal audience opinion. |
| Broadcast Media Africa, 2026 Africa Readiness Survey summary | Every surveyed organisation reported using generative AI in some capacity; more than half lacked formal AI policies. The article does not give a precise count for the latter finding. | This is a regional finding from the surveyed organisations, not a global adoption rate. |
The surveys use different samples, questions, geographies and definitions. Their percentages should not be combined into a single adoption or sentiment estimate.
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What work do broadcasters want AI to help with?
Automating repetitive newsroom tasks
Newsroom examples include transcription, copyediting assistance and automated metadata. These tasks can help staff search, label or prepare material, but a tool’s ability to produce a draft or transcript does not establish that its output is accurate enough to publish without review.
Supporting newsgathering and product work
Reuters Institute reports interest in AI-assisted newsgathering and coding or product development. It describes a New York Times investigation that used AI to search and transcribe large volumes of material, and monitoring bots at Helsingin Sanomat that alerted journalists to developing stories. These are examples reported by the institute, not independent proof of performance or a guarantee that the same approach will work in another newsroom.
Improving access to archives and content
AI can be applied to archive management, search and discovery, helping teams locate material or organize large collections. The Digital TV Group’s March 2026 workshop summary also identifies distribution, monetisation and internal operations as parts of the television value chain affected by AI. Its public summary frames the strategic question as where value and control will sit, and how deployment can be commercially viable and operationally sustainable. The Digital TV Group’s workshop summary sets out those themes; its accessible page does not provide the full report’s details.
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Extending reach and adapting formats
Reuters Institute respondents expect video and audio formats, as well as distribution through platforms including YouTube, TikTok, Instagram and AI services, to matter. That is adjacent publisher evidence, not a universal forecast for broadcasters. A practical implication is to assess whether AI can support adaptation, accessibility or discovery for a particular audience and channel, then measure whether it does.
Meeting regional language and operational needs
Broadcast Media Africa’s summary of its Africa Readiness Survey lists translation and subtitling, audience analytics, personalisation, newsroom automation and moderation among areas of use. It also names skills, funding, infrastructure, privacy and integration as barriers. Those constraints are specific to the African organisations surveyed; the article provides no precise rate for each barrier. Broadcast Media Africa’s survey summary is useful for understanding those regional conditions, not for generalizing them worldwide.
What operating conditions matter as much as the AI itself?
Reliability and integration
A workflow that fails during a live or deadline-driven operation can create more risk than the task automation saves. Haivision’s report emphasizes reliability, flexibility and efficient contribution workflows, while its survey places remote production among leading near-term technology priorities. Broadcasters evaluating AI therefore need to consider how it fits with production systems and fallback procedures, not only the model’s apparent capability.
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People, skills and governance
Staff need to know what a system is allowed to do, which outputs require review, and who is accountable when it is wrong. Formal policies help define approved uses, data handling, escalation and sign-off. Adoption without staff capacity or clear ownership can leave an organization with tools in use but no consistent way to control them.
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Before using source material with an AI system, establish whether the organization has permission to process it and how sensitive information is protected. Preserve records of where content came from and what changes were made where that matters to editorial accountability. Content-signing and provenance services are one category of vendor offering, but a service does not by itself establish the rights to use source material or guarantee that an output is accurate.
How should broadcasters judge an AI initiative?
Start with the workflow problem and the outcome the organization actually needs. A transcription pilot, an archive-search system and an audience-facing generated-news feature have different risks and should not be evaluated as if they were interchangeable.
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- Name the task. Specify whether the system will support transcription, metadata, archive search, monitoring, production, distribution, discovery or audience adaptation.
- Define a measurable outcome. Choose the intended result—such as time saved, improved accessibility, greater resilience, editorial quality or reach—and record a baseline before deployment.
- Classify the evidence. Make clear whether the proposed benefit is an expectation, a pilot observation or a measured result in the organization’s own workflow.
- Assign human responsibility. Name who checks outputs, verifies facts, corrects errors and makes editorial decisions. Human review is an essential control, but it does not automatically make an inaccurate output safe.
- Check rights and data handling. Confirm permitted source material, privacy and security requirements, and how provenance or changes will be recorded.
- Test operational fit. Assess integration with existing systems, reliability under real working conditions, fallback options and whether staff have the skills to operate the process.
- Review results before expanding. Compare actual performance with the chosen baseline and stop, revise or scale the initiative based on evidence rather than the general promise of AI.
The available evidence supports no single best tool or universal ranking of AI priorities. It points instead to a practical test: does a specific system improve a defined workflow while preserving reliability, rights, editorial responsibility and audience trust?
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why trust and human oversight are central in news
The Local Media Association’s article reports that nearly all respondents in its survey with Trusting News wanted a human to retain oversight, while a substantial minority held that AI should not be used in news at all. Those findings make trust a deployment condition, not a communications issue to address only after a tool is introduced. The association’s October 2026 article reports the survey results; its findings should not be treated as the views of every audience.
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Broadcasters should distinguish internal assistance from content presented to the public as news. Transcribing material, organizing archive records or helping a journalist search documents is not the same editorial act as publishing AI-generated claims, images, audio or summaries. For any audience-facing use, a credible process should explain what AI did, verify factual claims against appropriate sources, identify the editor responsible and correct errors transparently. Disclosure alone does not validate content, and human sign-off is only meaningful when reviewers have the time and information to challenge the output.
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Justin Stevens, Director of News at ABC Australia, told Reuters Institute: “I think it is an incredible time to be in News and journalism and the public are seeking out trustworthy news and information.” That observation captures the strategic balance: technology may expand what a newsroom can do, but the public value still depends on trustworthy journalism.
What broadcasters actually want from AI
The evidence points to demand for practical help across production, newsroom operations, archives, discovery and distribution, with enough reliability and flexibility to fit real workflows. It also shows that expectations can outpace demonstrated outcomes, and that use without skills, clear governance, rights controls or editorial ownership creates avoidable risk. The useful question for each broadcaster is not whether AI is important in the abstract, but which task deserves it, what result will count as success, and who remains responsible for the result.
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