A year-long Local Media Association initiative with 10 broadcast companies reveals practical ways to use artificial intelligence, protect newsroom content, create new revenue opportunities and preserve audience trust.
Artificial intelligence is no longer a technology local news organizations can afford to simply experiment with on the sidelines. It’s already changing how journalists work, how audiences consume information and how media companies operate.
For local broadcasters, the challenge is figuring out how to harness AI’s potential while protecting their intellectual property, journalistic standards and relationships with the communities they serve.
That was the focus of a year-long Local Media Association initiative, supported by Google News Initiative, that brought together 10 broadcast companies to examine both the risks and opportunities of artificial intelligence.
The participating companies included major broadcast groups such as Sinclair, Nexstar and Scripps, along with local media organizations including WRAL, Draper, Morgan Murphy, Cox and Graham.
The project produced an AI roadmap for local broadcasters, along with a more detailed report outlining practical recommendations for the industry.
In a new episode of LMA’s Keep It Local podcast, Chief Innovation Officer Frank Mungeam joined host Ryan Welton to discuss the initiative’s most important findings, from protecting newsrooms against unauthorized AI use to transforming decades of archived video into potential revenue-generating assets.
Mungeam’s central message: News organizations need to move beyond experimentation and begin integrating AI into their operations — but they must do it thoughtfully.
The first priority: Protect your newsroom
Before news organizations can fully embrace AI’s opportunities, Mungeam believes they must address one of its most immediate risks: Employees using unapproved AI tools without organizational oversight.
Mungeam said newsroom leaders should not assume that simply prohibiting AI use will prevent employees from adopting the technology.
“The reality is people are using AI, and the only question is whether as a leadership team you are organizing how they’re using AI,” Mungeam said.
That means establishing clear policies about what employees can and cannot do with AI, while also giving them access to approved tools that meet the organization’s security and intellectual property requirements.
Free AI applications can be particularly problematic when employees upload proprietary information, unpublished reporting or copyrighted content without understanding how those platforms handle the data.
Mungeam recommends that organizations take two immediate steps: Develop an AI policy, even if it’s only an initial version, and establish agreements for approved enterprise-grade AI tools.
He also cautions against waiting until an organization can craft a comprehensive, permanent policy.
“Don’t let the perfect be the enemy of the good,” Mungeam said.
Because AI capabilities are changing rapidly, organizations should establish multidisciplinary teams to regularly review and update their policies, with representation from editorial, sales, technology and other departments.
AI can eliminate one of broadcasting’s most frustrating workflows
For decades, one of the biggest challenges facing broadcast digital teams has been converting television reporting into content suitable for websites, mobile platforms and other digital destinations.
Broadcast scripts are written for spoken delivery, often with formatting and sentence structures that don’t translate naturally into compelling digital articles.
Mungeam, who previously worked in broadcast digital leadership, remembers the enormous amount of time spent teaching television reporters to write for digital platforms or having digital teams rewrite their scripts.
Artificial intelligence is fundamentally changing that process.
One of the most immediately valuable applications identified during LMA’s initiative is content versioning: Using AI to help transform an original piece of journalism into different formats for different platforms.
A television script can become a draft digital article. An online story can be adapted into a television voiceover. Reporting can be converted into audio, summaries or other formats designed for different audience preferences.
The result isn’t simply greater efficiency. It’s an opportunity for local newsrooms to serve more audiences without repeatedly creating the same underlying reporting from scratch.
Mungeam also highlighted AI-assisted transcription, searchable public meeting recordings, quote identification and headline optimization as practical ways to save time.
He suggested that transcription alone can recover 45 minutes to an hour of a television reporter’s workday.
These efficiencies are particularly important for news organizations facing staffing and resource constraints.
For Mungeam, the underlying philosophy is that the individual story — rather than the traditional television newscast — is the fundamental unit of news production.
With AI making it easier to adapt that reporting, broadcasters can more efficiently reach audiences wherever and however they prefer to consume journalism.
The revenue opportunity: AI isn’t just for journalists
Although much of the industry’s AI discussion has focused on editorial applications, LMA’s initiative also explored opportunities to improve advertising sales and business operations.
Mungeam said broadcasters should examine the entire advertising sales process, from identifying prospects and understanding customer needs to developing proposals, managing fulfillment and reporting results.
AI can assist sales teams in researching prospective customers, identifying potential matches between advertisers and a media company’s audience, preparing for sales conversations and building customized presentations.
It can also streamline post-campaign reporting and help sales representatives communicate results to advertisers.
Mungeam cited an example shared during the initiative in which news organizations applying AI-supported sales practices reported close rates 30% to 40% higher.
That figure illustrates the potential value of applying AI beyond the newsroom, although individual results will depend on how organizations implement the technology.
For local media companies confronting revenue pressure, more effective sales operations could be just as consequential as editorial workflow improvements.
The ‘gold in the basement’: Unlocking decades of broadcast archives
One of the more intriguing opportunities discussed during the initiative involves an asset many broadcasters already own but have struggled to fully utilize: Their historic video archives.
For decades, television stations accumulated extensive collections of footage documenting their communities, including major breaking news events, political developments, cultural milestones and everyday local history.
Much of that content was originally stored on videotape or in other formats that were difficult to search.
Even stations that digitized their archives have often faced another problem: Inadequate metadata describing what’s actually in the footage.
Mungeam recalled working at KGW in Portland, where finding historic footage frequently depended on the institutional knowledge of longtime employees who knew where particular recordings were stored.
AI offers the potential to change that.
By combining digitization, transcription, content recognition and semantic tagging, broadcasters can make previously inaccessible collections searchable.
Journalists could locate historic footage to provide context for ongoing stories, produce retrospective programming or create entirely new content from material already in the station’s possession.
Mungeam cited WRAL’s work making archived video accessible and searchable within its newsroom as an example of what’s possible.
But the opportunity extends beyond editorial use.
Broadcasters could potentially license archived footage to outside organizations or explore licensing arrangements with AI companies seeking video material for model training.
Unlike traditional digital publishers whose online content may already have been widely accessible to AI systems, broadcasters possess large collections of historic video that may represent relatively untapped licensing opportunities.
For local television companies, those archives could become a new source of revenue as well as a resource for better journalism.
Audience trust may be local journalism’s most valuable advantage
While AI can help media companies produce and distribute information more efficiently, Mungeam argues that technology alone cannot replicate the trust local news organizations have developed through years of community service.
He described that trust as journalism’s competitive “moat” — an advantage that becomes increasingly important when AI makes content easier to generate.
AI systems can produce information quickly, but accuracy, accountability and credibility are not automatic.
That distinction matters in an environment where audiences are becoming more familiar with AI-generated material while also expressing concerns about its reliability.
Mungeam pointed to research conducted by LMA and Trusting News examining audience attitudes toward artificial intelligence in journalism.
The findings suggest that people respond more positively when news organizations are transparent about how and why they use AI.
But a generic disclosure that AI was involved may not be enough.
Mungeam said organizations should explain what technology was used, what role it played in producing the content and, importantly, how that use benefited the audience.
For example, a newsroom might disclose that an AI tool helped transcribe an interview, allowing journalists to spend more time verifying information or reporting additional details.
The emphasis should be on the public benefit rather than simply the newsroom’s operational savings.
Audience comfort also varies considerably depending on the application.
According to Mungeam, transcription and translation are among the uses audiences are most comfortable with, while having AI independently write news stories generates substantially more concern.
He emphasized that human editorial oversight remains essential.
“‘Human touches last’ is the world’s simplest and best AI policy,” Mungeam said.
News organizations should also make their broader AI policies publicly accessible, explaining which applications they permit and which they prohibit.
That transparency, Mungeam believes, is essential to preserving the relationships that differentiate trusted local journalism from the rapidly expanding volume of information available online.
A practical starting point for newsrooms
For organizations still struggling to determine where to begin, Mungeam recommends a straightforward approach.
First, review the available audience research to identify AI applications that people are most comfortable with.
Next, bring newsroom leaders and employees together to discuss which uses could improve journalism and benefit the communities they serve.
Then establish basic internal guardrails, approve appropriate tools and communicate the policies to employees.
Finally, share the organization’s approach publicly.
Mungeam suggested that a news director or general manager write a message to the audience explaining how the newsroom plans to use AI, why it has chosen those applications and how it will maintain human oversight.
That communication should also invite audience feedback.
The objective isn’t to create a policy that never changes. It’s to establish a responsible foundation that can evolve alongside technology and audience expectations.
Mungeam believes newsrooms can begin that process in as little as a week.
LMA’s next step: From experimentation to implementation
The findings from the broadcaster initiative are already helping inform LMA’s next phase of AI work.
Mungeam announced during the podcast that LMA is launching a new six-month AI cohort, supported by Google News Initiative, involving 12 news organizations.
The new initiative will focus on operational efficiency and practical implementation rather than simply exploring potential use cases.
Participants will include small and large publishers, along with organizations serving traditionally underrepresented communities.
Each participating organization will receive AI training and coaching from Google and work toward developing an implementation project.
The resulting capstone projects are intended to provide examples and practical roadmaps that other news organizations can follow.
The new cohort is expected to begin in mid-November.
The future of local journalism requires adaptation
For media professionals who have spent decades navigating technological disruption, the rise of artificial intelligence may feel familiar.
Mungeam noted that news organizations have already weathered dramatic changes driven by the internet, search engines, smartphones, social media and shifting audience expectations around video.
AI may represent an even more consequential transformation.
But he believes the industry’s experience adapting to previous disruptions provides a foundation for responding to this one.
The biggest immediate opportunity, he said, is helping resource-constrained news organizations overcome the persistent challenge of limited capacity.
Local journalists continually face demands to produce more reporting, reach additional audiences and operate across an expanding range of platforms, often without corresponding increases in staffing.
Used responsibly, AI can help close that gap.
“Right now, AI is transformative for news organizations in creating capacity to allow us to do more of the things we want to do to serve our audiences,” Mungeam said.
The essential question for local media leaders is no longer whether artificial intelligence will affect their organizations.
It’s whether they will establish the policies, tools and workflows needed to make that transformation work for their journalists, their businesses and, most importantly, their communities.
Keep It Local is a podcast from Local Media Association featuring conversations with the people building the future of sustainable local journalism.
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Editor’s note: Artificial Intelligence was used to transcribe the podcast and create a summary, which was then edited for accuracy and clarity. The thumbnail accompanying this story and the YouTube video embedded in it was created in ChatGPT with instructions to not alter the photo, only adding graphics.