Broadcasters want AI to make production and newsroom workflows more efficient, help teams find and adapt content, and support new ways to reach audiences. But the evidence points to ambition, not a settled transformation: Haivision’s 2026 survey found limited reported use alongside high expectations, while newsroom research and audience findings underline the need for human judgment, reliable systems, and clear accountability.
What do broadcasters actually want from AI?
They want useful results that fit real operations—not AI for its own sake. The clearest broadcast-specific signal comes from Haivision’s 2026 survey of more than 1,300 broadcast professionals worldwide: 27% of respondents reported using AI, while 64% expected it to have the greatest impact on broadcast production over the next five years. Those are survey responses, not a measure of realized gains across the industry. Haivision’s 2026 Broadcast Transformation Report also put remote production ahead of AI as an immediate technology priority, a reminder that AI investment competes with foundational workflow needs.
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The numbers are best read as a signal of interest and uncertainty, not as a single industry-wide adoption rate. Each source below surveyed a different group and asked different questions.
| Finding | What it measures |
|---|---|
| 27% in 2026, compared with 25% in Haivision’s 2025 report | Share of Haivision survey respondents reporting AI use; this is not a count of all broadcasters. |
| 64% in Haivision’s 2026 survey | Share expecting AI to have the greatest impact on broadcast production over the following five years—not a reported outcome. |
| 41% in Haivision’s 2026 survey | Share naming remote production as their leading technology priority for 2026. |
| 44% promising; 42% limited; 13% transformational | Reuters Institute’s 2026 survey of media leaders and publishers on current newsroom AI initiatives. This is adjacent newsroom evidence, not a broadcaster-only poll. |
| 64% back-end automation; 82% newsgathering; 81% coding and product development | Share of Reuters Institute’s 2026 respondents identifying each area as an important AI use case. These categories come from a publisher and newsroom sample. |
| 98.8% wanted human oversight; 30% said AI should never be used in news | Findings reported by the Local Media Association from its 2026 collaboration survey with Trusting News, which included more than 1,400 news consumers. They do not represent all audiences. |
| All surveyed organizations reported some generative AI use | Broadcast Media Africa’s 2026 Africa Readiness Survey summary. It also says more than half lacked formal AI policies, without giving a precise share in the accessible article. |
The Reuters Institute’s 2026 Journalism, media, and technology trends and predictions shows the gap between promise and transformation: 44% called current initiatives promising, 42% limited, and 13% transformational. The categories and percentages describe respondents’ assessments, not independently measured performance.
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Where can AI help in broadcast and newsroom workflows?
Automate repetitive back-end work
Transcription, copyediting assistance, and automated metadata are prominent newsroom use cases in the Reuters Institute’s survey. These tasks can help teams process material and organize content, but the cited survey does not establish a standard time saving or financial return. In African media, Broadcast Media Africa’s 2026 survey summary lists translation and subtitling, audience analytics, personalization, newsroom automation, and moderation among reported areas of use.
Support newsgathering and product work
Reuters Institute respondents also identified newsgathering and coding or product development as important areas. Its report 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 proof that the same approach will perform equally well in another newsroom.
Make archives and distribution easier to use
AI’s possible role extends beyond the newsroom desk. The Digital TV Group’s summary of a March 2026 workshop discusses development, production, archives, distribution, discovery, monetization, and internal operations. That wider view matters for broadcasters because content can be created, stored, packaged, surfaced, and reused across connected workflows. The DTG summary frames the strategic question as where value and control will sit in the television ecosystem; it does not provide a public, quantified return for each use case.
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The Reuters Institute also reports that media leaders expect video and audio formats, and distribution through platforms such as YouTube, TikTok, Instagram, and AI services, to matter. This is publisher research, so it should be treated as adjacent evidence rather than a universal forecast for television and radio broadcasters. For broadcasters, the practical implication is to assess whether AI can help adapt or make existing material discoverable in the places audiences use.
Is AI already changing broadcast production?
Some organizations report using AI, but the available evidence does not show that broadcast production has already been broadly transformed. In Haivision’s survey, reported use was 27% in 2026, up from 25% in its 2025 report; the same survey found much greater expectation of future impact. Those figures measure different things: use today versus anticipated importance over five years.
Operational modernization remains part of the picture. Haivision’s 2026 respondents more often named remote production than AI as their leading technology priority for the year. The company’s Vice President of Marketing, Marcus Schioler, described the sector’s task as balancing innovation with operational reality, emphasizing reliability, flexibility, and efficient contribution workflows. That is a vendor’s summary of its survey, not independent evidence that a particular AI system meets those requirements.
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What can prevent useful AI adoption?
Operational fit and reliability
A tool has to work with existing production and content systems and meet the reliability needs of the workflow it supports. A promising demonstration is not enough if the process becomes harder to operate, outputs arrive inconsistently, or staff cannot recover when the tool fails. Haivision’s emphasis on reliability, flexibility, and contribution workflows reflects these operational concerns.
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Broadcast Media Africa’s Africa Readiness Survey summary identifies skills, funding, infrastructure, privacy, and integration as barriers. These are findings reported for the surveyed African media organizations; they should not be generalized to every region. The same summary says all surveyed organizations were using generative AI in some capacity, while more than half lacked formal AI policies. Adoption, in other words, can precede institutional readiness.
Editorial trust, accuracy, and rights
AI assistance inside a workflow and AI-generated material presented to an audience carry different editorial risks. The Reuters Institute reports concerns about the accuracy of chatbot news output. For any use that can affect published reporting, a newsroom needs to decide who verifies facts, who approves the material, how errors are corrected, and whether audiences should be told how AI was used. Human review is an important control, but it does not by itself guarantee accuracy.
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Rights and provenance also matter when AI systems process or transform source material. Teams should establish which material may be used, under what permissions, and how its origin and edits can be tracked. The DTG workshop summary notes that value and control across the television ecosystem are strategic questions, not resolved outcomes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Do audiences want humans to oversee AI in news?
The Local Media Association reported that 98.8% of more than 1,400 respondents in its 2026 collaboration survey with Trusting News wanted a human to retain oversight of AI use for news. The same article reported that 30% held a baseline belief that AI should never be used in news. These results indicate strong concern in that survey, but they are not a universal measure of audience opinion. The LMA article is an association’s report of the findings rather than the original survey instrument.
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How should a broadcaster assess an AI initiative?
Compare the proposed task with the outcome and the controls needed to deliver it. The surveys do not establish one best tool or a universal ranking of uses; these questions help separate a real operational case from an expectation or a pilot.
- Task: Is the system intended for transcription, metadata, archive search, story monitoring, production, discovery, moderation, or audience adaptation?
- Outcome: What should improve—time spent on a task, accessibility, editorial quality, resilience, audience reach, or commercial opportunity? Define a measure before rollout.
- Evidence: Is the claimed benefit a measured result in the organization’s own workflow, a pilot observation, or an expectation from a survey?
- Human control: Who reviews outputs, verifies facts, handles corrections, and makes the final editorial decision?
- Rights and data: Which source materials can be processed, under what permissions, and how will provenance be retained?
- Operational fit: Can the system integrate with current tools and meet the organization’s requirements for reliability, privacy, security, and staff capability?
The Digital TV Group’s March 2026 workshop summary puts the wider strategic issue plainly: the question is not only whether AI will be adopted, but where value and control will sit and whether deployment can be commercially viable and operationally sustainable. For broadcasters, that makes workflow fit and editorial accountability part of the business case, not afterthoughts.
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