Artificial intelligence in journalism is transforming how news is researched, produced, distributed, and consumed. However, its adoption is not an isolated phenomenon. It is part of a broader technological evolution that has been reshaping the media industry for years.
In an interview published by El Eco de Tandil, Fabricio Defelippe, Tuxdi’s Director and a university professor, discussed how artificial intelligence, social media, and conversational interfaces are redefining the relationship between media organizations, journalists, and their audiences.
His perspective moves beyond the debate over whether AI will replace journalists. The deeper transformation lies elsewhere: when generating and summarizing information becomes increasingly easy, the true differentiator is the ability to decide what should be investigated, what must be verified, and which stories are genuinely worth telling.
From Traditional Media to Distributed Audiences
Fifteen years ago, the relationship between media organizations and their audiences was much more direct. Newspapers, radio, and television accounted for a significant share of news consumption. Websites were already part of the landscape, but they often operated as an additional channel rather than the main point of contact.
As internet adoption grew, media companies began moving their content online. Social media, mobile apps, newsletters, short-form video, and messaging platforms followed.
Audiences no longer need to visit a news website’s homepage to find information. They may come across a story on Instagram, TikTok, YouTube, Google Discover, a WhatsApp channel, or through a conversation with an AI chatbot.
Defelippe described the speed and impact of this transformation clearly:
“Technology hit the media like a slap in the face.”
Media organizations are no longer competing only with one another. They are also competing for attention within platforms that control distribution, algorithms, and a large part of the relationship with users.
Technological adaptation is therefore no longer an optional improvement. It has become essential for remaining relevant in an increasingly fragmented information ecosystem.
News Distribution Channels Are Changing Faster
The rise of generative AI is the latest stage of this transformation, but it will probably not be the last. Every new technology changes consumption habits and forces media organizations to reconsider how information is presented and distributed.
As Defelippe summarized in the interview:
“They are all technological changes.”
What is different today is the speed at which these changes occur. A media organization may build a strategy around one platform only to discover that its audience has started consuming information through an entirely different format.
This requires a more flexible approach to content. A single investigation may become a written article, video, podcast, newsletter, social media post, or answer delivered through a conversational interface.
The underlying story remains the same, but it must be able to adapt to different channels, moments, and consumption habits.
From Searching for Information to Talking to It
For years, search engines were the main gateway to digital news. A user entered a query, received a list of results, and decided which website to visit.
Artificial intelligence is changing this behavior. Users can now ask a complete question and receive a synthesized answer without visiting several pages or reading multiple sources.
This shift creates a major challenge for digital journalism. Media organizations no longer need to optimize content only for traditional search engines. They must also create clear, structured, authoritative, and verifiable information that AI-powered search engines and answer engines can understand and cite.
According to the Reuters Institute’s Digital News Report 2026, 10% of surveyed users now use AI chatbots to access news, compared with 7% the previous year. Among people under 35, the figure reaches 16%. However, trust in chatbot responses remains lower than trust in news produced by professional media organizations. Reuters Institute Digital News Report 2026
Media companies therefore face two related challenges: gaining visibility within emerging AI interfaces and giving audiences meaningful reasons to visit, trust, and return to the original source.
How Is Artificial Intelligence Used in Journalism?
AI can support different stages of the editorial process. Some of the most practical uses of artificial intelligence in journalism include:
- Transcribing interviews, conferences, and audiovisual content.
- Analyzing large collections of documents.
- Identifying patterns and connections within datasets.
- Classifying articles by topic or section.
- Suggesting headlines, summaries, and keywords.
- Creating different versions of a story for multiple channels.
- Generating tags, descriptions, and metadata.
- Adapting articles for newsletters and social media.
- Personalizing content recommendations.
- Searching and retrieving information from news archives.
- Supporting initial fact-checking and verification tasks.
These tools can reduce the time spent on repetitive work and allow editorial teams to focus on tasks that provide greater journalistic value.
Automation, however, does not mean fully delegating news production. AI can generate a draft, organize information, or suggest a structure. Data verification, editorial judgment, accountability, and the final decision to publish must remain under human control.
Will Artificial Intelligence Replace Journalists?
Generative AI can produce coherent text and summarize existing information within seconds. It can imitate journalistic structures, adapt writing styles, and process more data than a person could manually review in the same amount of time.
But producing text is not the same as practicing journalism.
Journalistic work begins before the writing stage. It involves identifying an issue that has not yet entered the public agenda, understanding a community, building trust with sources, asking questions, comparing different accounts, and considering the consequences of publishing sensitive information.
AI systems can process what already exists. They cannot fully replace the human ability to observe a situation and recognize that something important is happening.
In this context, journalists do not lose their value. Instead, the point at which they create that value changes.
From Providing Answers to Asking Better Questions
For a long time, a significant part of journalism involved gathering information and communicating it to the public. Today, AI systems can retrieve, organize, and summarize large amounts of information within seconds.
As this capability becomes widely available, human value increasingly moves toward asking the right questions.
What information is missing? Which voice has not been heard? What interests are behind a particular decision? How could an event affect a specific community? Which claims appear credible but still need independent verification?
AI-generated answers depend on available information and the quality of the instructions given to the system. Journalists, however, can challenge the original premise, recognize contradictions, investigate undocumented situations, and ask questions that have not been asked before.
The ability to question, interpret, and discover new angles will become one of journalism’s strongest differentiators in an AI-driven media environment.
The Risk of Producing More but Saying Less
The accessibility of generative AI introduces another challenge: the rapid multiplication of generic content.
If different media organizations use similar tools, consult the same sources, and automatically generate articles about the same events, audiences may encounter a large amount of content with very little differentiation.
Speed alone is no longer enough to create a competitive advantage. When everyone can publish quickly, original reporting, proximity to sources, community knowledge, and meaningful context become more valuable.
The goal should not be to use AI simply to fill more pages. It should be to improve journalistic processes and give reporters more time to produce stories that cannot be obtained through automated summaries.
The Risks of AI in News Media
The adoption of artificial intelligence also introduces risks that media organizations must manage from the beginning.
AI models can generate inaccurate information, invent references, reproduce bias from their training data, or confidently present unverified claims as facts. An indiscriminate use of these tools can also weaken a publication’s editorial identity and damage audience trust.
A responsible AI strategy should therefore include:
- Human review before publication.
- Verification of names, dates, figures, quotations, and sources.
- Internal policies defining acceptable uses of AI.
- Protection of confidential and sensitive information.
- Traceability of automated processes.
- Transparency when AI has played a relevant role.
- Clearly identified accountability for every published piece.
- Regular evaluations of accuracy, quality, and business value.
Artificial intelligence can assist journalists, but it should not become an autonomous source or the final authority on whether information is true.
How Media Organizations Can Prepare for AI
Adopting artificial intelligence does not require automating an entire newsroom at once. Media companies can begin with specific, measurable, and relatively low-risk applications.
An initial phase may include transcription, tagging, metadata generation, archive search, or the preparation of first drafts. These use cases allow the organization to evaluate how much time is saved, what errors appear, and which human controls are required.
Media organizations can then move toward more advanced systems, including internal assistants for journalists, large-scale document analysis, personalized news experiences, or conversational interfaces connected to the publication’s archive.
Before implementing any AI solution, editorial and product teams should answer several questions:
- What editorial or operational problem are we trying to solve?
- Which tasks should never be fully delegated?
- What information can the system process?
- How will its output be verified?
- What level of human intervention is required?
- How will we determine whether the solution actually improves the process?
Technology should be implemented around a journalistic strategy. Using AI simply because it is available may increase content output without improving quality, trust, or the relationship with audiences.
The New Differentiator: Finding the Story Worth Telling
In an environment saturated with information, summarizing existing content will become increasingly easy. The difficult task will be recognizing which story matters, finding an original perspective, and explaining why it deserves the audience’s attention.
Defelippe summarizes this challenge in a statement that captures the core of the transformation:
“The value of a journalist will be finding what is worth telling.”
The future of journalism will not be defined by a choice between humans and machines. It will depend on how media organizations combine technological capabilities with original reporting, editorial judgment, and a deep understanding of their communities.
Artificial intelligence can organize information, automate processes, and expand what a newsroom is capable of doing. But the perspective required to identify a meaningful story, understand its context, and communicate it responsibly remains fundamentally human.
At Tuxdi, we develop digital solutions for media organizations and integrate automation and artificial intelligence into editorial workflows. Our goal is not to replace a publication’s identity, but to strengthen its ability to produce, organize, and distribute relevant information.
