Author – Matilde Barbini
The Existing Gap in AI Adoption
There is both a perceived and material gap in AI adoption between large national news organisations and smaller local news providers (Rinehart & Kung, 2022). This gap has empirical indicators and has been examined in the scholarly literature, with some estimates suggesting that only 10% of AI systems deployed in journalism are used in local newsrooms (Aubin Le Quéré & Jakesch, 2022). Several factors help explain this imbalance: large national and international publishers typically have the financial capacity to develop in-house AI models, while smaller regional and local outlets are more likely to rely on off-the-shelf tools provided by platform companies (Simon, F. M. 2024b). Current debates about AI in journalism tend to centre on larger organisations, leaving local news organisations with less sustained attention despite their heightened exposure to these changes (Simon, F. M. 2024a).
Structural Constraints on Local Newsrooms
The constraints facing local newsrooms are largely structural, as they often lack the technological infrastructure, staff time and financial capacity needed to experiment with AI and automation technologies (Rinehart & Kung, 2022). Smaller and emerging news organisations also struggle to allocate funding for qualified technical personnel or for the implementation and maintenance of AI systems (Beckett & Yaseen, 2023).
These constraints are compounded by difficulties in retaining technical knowledge. Staff turnover in local newsrooms can lead to the departure of those staff members with the technical capacity to drive innovation, interrupting or halting AI implementation (Rinehart & Kung, 2022). In such cases, institutional learning remains fragile. Broader working conditions further complicate adoption: journalists in smaller newsrooms face heightened work stressors, including job insecurity and increased responsibility toward their communities, which can limit the organisational capacity and willingness to introduce new experimental technologies (Tseng et al., 2025).
Differentiated Needs and Uses
AI adoption across the news industry does not follow a single pattern (Beckett & Yaseen, 2023). Well-resourced newsrooms are more likely to explore creative or experimental applications of AI, whereas under-resourced newsrooms tend to prioritise tools that reduce time spent on routine and repetitive tasks, reflecting a pragmatic response to constrained organisational conditions.
This efficiency-oriented pattern is particularly visible in local newsrooms. Local newsroom leaders often express a need for automation in areas such as transcribing public meetings, scraping court records and producing structured reports on high school sports or weather (Rinehart & Kung, 2022). These uses are typically justified as a way to free reporters for more substantive journalistic work, recovering editorial time that has been reduced by administrative and repetitive demands.
Yet this emphasis on efficiency should not be read as a lack of innovation. Smaller independent news organisations also show evidence of experimentation: some adopt AI in response to shrinking revenues and limited institutional support, while their smaller organisational structures may enable faster experimentation than is possible in larger newsrooms (Tseng et al., 2025).
Automation and the Local Information Gap
A more concerning dynamic emerges when local newsrooms lack the capacity to adopt AI themselves, while external actors use automation to produce local content in their absence. The instability of the local news ecosystem has created a market opportunity for automated forms of parachute journalism, in which for-profit companies mass-produce local stories without local journalists or community context (Aubin Le Quéré & Jakesch, 2022). Although such content may address local topics, it is produced without the embedded knowledge that local journalists typically bring to their reporting.
A related concern arises within local media organisations themselves, where corporate owners may use automation not to strengthen reporting capacity but to reduce labour costs and consolidate job roles. In local television, for example, automation technologies have been used to merge job descriptions and replace skilled human labour, increasing efficiency for owners while weakening reporting practices and reducing the civic quality and local specificity of news (Higgins-Dobney, 2021).
Taken together, these dynamics suggest that the central issue is not automation as such, but the conditions under which it is deployed. When automated tools are used to mass-produce localised journalism without local context or to replace rather than support local journalistic labour, they risk contributing to lower-quality content and further eroding public trust (Aubin Le Quéré & Jakesch, 2022).
Infrastructural Dependency
For local newsrooms that do adopt AI tools, adoption often occurs under conditions of limited bargaining power. Smaller media organisations typically have fewer resources and less influence over vendors than larger organisations, making it more difficult for them to shape or contest the editorial values embedded in third-party automated tools (Drunen & Fechner, 2023). As a result, reliance on platform companies can produce vendor lock-in and infrastructural dependency, leaving local newsrooms exposed to pricing decisions, technical constraints and design choices set by large technology companies (Simon, F. M. 2022).
This dependency reinforces existing inequalities within the news industry. Larger organisations are often better positioned to negotiate favourable terms with platforms and technology providers, whereas smaller news organisations are more likely to accept standardised tools, pricing structures and technical limitations (Tseng et al., 2025). Tools presented as solutions for resource-scarce newsrooms may therefore deepen the very dependencies they are meant to alleviate, since they are frequently controlled by actors that already hold significant power in the digital information environment.
The Democratic Stakes of AI in Local Journalism
These risks do not mean that AI is necessarily harmful to local journalism. Rather, they show that its effects depend on the institutional conditions under which it is deployed. The same capacity for scale that is problematic when used to produce decontextualised local content may support local journalism when governed by editorial standards, local accountability and public-interest aims. AI can enable more comprehensive local coverage, including for underserved communities and localities where traditional reporting may be economically difficult to sustain (Hansen et al., 2023). Automated localised reporting may also have social and political value if it expands local participation and responds to the decline of local coverage (Hansen et al., 2023).
The central issue is the set of conditions under which AI is introduced, governed and controlled in local journalism. The trajectory of the AI gap will depend on ownership, access, bargaining power and institutional capacity (van Drunen, M. Z., & Fechner, D. 2023). These conditions will shape whether AI expands local reporting capacity or reinforces existing inequalities. The question is therefore partly technological, but it is equally a question of political economy, democratic infrastructure and the future organisation of local public information.
References
Aubin Le Quéré, M., & Jakesch, M. (2022). Trust in AI in under-resourced environments: Lessons from local journalism. In CHI ’22 Workshop on Trust and Reliance in AI-Human Teams. Association for Computing Machinery.
Beckett, C., & Yaseen, M. (2023). Generating change: A global survey of what news organisations are doing with artificial intelligence. JournalismAI https://www.journalismai.info/research/2023-generating-change.
Hansen, A. S., Helberger, N., Blanke, T., & Bočytė, R. (2023). Initial white paper on the social, economic, and political impact of media AI technologies (Deliverable D2.2). AI4Media: A European Excellence Centre for Media, Society and Democracy https://www.ai4media.eu/download.php?file_url=2022/03/AI4Media_D2.2_final-compressed_compressed.pdf.
Higgins-Dobney, C. L. (2021). News work: The impact of corporate-implemented technology on local television newsroom labor. Journalism Practice, 15(8), 1054–1071. https://doi.org/10.1080/17512786.2020.1762506.
Rinehart, A., & Kung, E. (2022). Artificial intelligence in local news: A survey of US newsrooms’ AI readiness The Associated Press. https://www.amic.media/media/files/file_352_3673.pdf.
Simon, F. M. (2022). Uneasy bedfellows: AI in the news, platform companies and the issue of journalistic autonomy. Digital Journalism, 10(10), 1832–1854. https://doi.org/10.1080/21670811.2022.2063150.
Simon, F. M. (2024a). Artificial intelligence in the news: How AI retools, rationalizes, and reshapes journalism and the public arena. Tow Center for Digital Journalism, Columbia University https://www.cjr.org/tow_center_reports/artificial-intelligence-in-the-news.php.
Simon, F. M. (2024b). Escape me if you can: How AI reshapes news organisations’ dependency on platform companies. Digital Journalism, 12(2), 149–170. https://doi.org/10.1080/21670811.2023.2287464.
Tseng, E., Young, M., Aubin Le Quéré, M., Rinehart, A., & Suresh, H. (2025). “Ownership, not just happy talk”: Co-designing a participatory large language model for journalism. In Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency (pp. 3119–3130). Association for Computing Machinery. https://doi.org/10.1145/3715275.3732198.
van Drunen, M. Z., & Fechner, D. (2023). Safeguarding editorial independence in an automated media system: The relationship between law and journalistic perspectives. Digital Journalism, 11(9), 1723–1750. https://doi.org/10.1080/21670811.2022.2108868.
Further Reading:
Darr, J. P. (2026, January 9). Participatory journalism and its potential in AI-assisted local news. Knight First Amendment Institute at Columbia University. https://knightcolumbia.org/content/participatory-journalism-and-its-potential-in-ai-assisted-local-news.
Radcliffe, D. (2025, January). Journalism in the AI era: Opportunities and challenges in the Global South and emerging economies. Thomson Reuters Foundation. https://www.trust.org/wp-content/uploads/2025/01/TRF-Insights-Journalism-in-the-AI-Era.pdf.
Further Watching/Listening:
Roy, N., Castro Varón, J., Köppen, U., O’Rourke, T., & Siegel, B. (2025, March 27). Beyond the hype: The real impact of AI on newsrooms [Panel session]. 26th International Symposium on Online Journalism, Austin, TX, United States. https://isoj.org/panel/panel-beyond-the-hype-the-real-impact-of-ai-on-newsrooms/.
Image Attribution
Generated by: Midjourney
Date: 21 May 2026
Prompt: “Split composition: left side shows a local newsroom embedded in its community, with reporters working among town hall papers, court records, local maps and community noticeboards; right side shows corporate AI infrastructure generating streams of automated local stories from abstract data, detached from place and context; subtle figures of local journalists and residents appear on the community side; theme of inequality, bargaining power and the future of local public information; contemplative, civic, critical; muted greys and warm local tones transitioning into cool blues and metallic light, cinematic lighting, refined concept art style, no readable text, no logos –ar 16:9 –v 6.0”