News product teams are uniquely positioned to unlock AI value

There are many reasons I found the first in-person News Product Alliance Summit this past October to be enlightening and enjoyable — the meticulously organized programming, the brilliant cadre of speakers, beautiful autumn days in Chicago and a chance to reconnect with many of the friends and colleagues who have long paved the way in the news product ecosystem. But the most compelling statement inspiring my 2026 prediction came immediately from the first keynote speaker, Anthony Maggio, vice president and head of product management at Airtable. Discussing the future of product, both in news organizations and beyond, he stated, “the one thing I have become most convinced of is that product teams are really in a unique position to unlock AI value for their organizations.”

Product professionals are particularly suited to lead the AI charge in media organizations, due to the cross-disciplinary nature of the function, combining business, technology and user needs. So, what does that look like? In 2026, I see product and AI merging in three key areas: agents, archives, and vibe coding.

Agents

Much of the talk around AI has to do with using large language models and generative AI to create content. Rightly so, both professionals and consumers are leery of AI’s ability to replace human writing and reporting. In 2026, however, AI practices will move from primarily writing prompts in an app like ChatGPT to what is known as agentic AI, or tools made for specific purposes that can improve and redirect workflows.

For example, agents can work behind the scenes as research and fact-checking assistants, content transcribers and summarizers, and generators of audio and video presentations. Agents can be developed to “watch” city council meeting videos and alert for certain words or phrases. An agent could be created to reformat stories across platforms, starting with a human-prepared, 700-word article and turning that into a vertical video with TikTok script, a captioned Instagram carousel, a newsletter blurb or any other useful platform or format.

Product professionals will need to be involved with both infrastructure and orchestration layers in the development of agentic systems to assure that what these tools produce is safe, practical, valuable, and repeatable and that the experience matches the expectations of the internal users. But the potential is limitless and can be crucial to the future sustainability of many media organizations.

Archives

There are many ways that AI can also be integrated into audience-facing products — personalized news feeds that create a “liquid” home page, dynamic paywalls based on user interests, location-aware news, interactive story features, to name a few. But I feel the most potential lies in the ways that organizations could activate their archives. Archives are a vast field of unlocked value that, if executed well, can be converted to marketable and potentially revenue-generating advantages. Archives can be used to create new value from decades of reporting, becoming a living, dynamic asset rather than an attic of locked-away, hidden gems.

An AI-powered archive allows the products to produce results based on only organization-created or approved content, providing a structured knowledge base with deep, trustworthy, and most importantly, localized context. Using what is known as Retrieval-Augmented Generation (RAG), a system can first fine-tune the body of knowledge to certain domains or organizations from which further prompts are applied.

Some archive-based products might be related to recipes or restaurant reviews; local history applications; classic sporting events or unique competitions (perhaps cheese rolling or the local soapbox derby champion?); trivia games, scavenger hunts or BuzzFeed-style quizzes with a local flair; or a myriad of ways to support tourism or the local entertainment scene with discovery tools based on archived reporting.

The possibilities are endless to connect what already exists as archived content to user interests, personalized context and the local setting. Not to mention that these competencies could lead to development of revenue products for premium access or APIs for external clients. Or by combining with agentic AI, archival information could populate tools that automate regular tasks for both newsroom and audience users (reminders of special dates, suggestions for gifts and outings, ticket discounts and merchandise deals are just a few possibilities).

Vibe coding

The third area in which I see product and AI merging is in the realm of vibe coding, a phrase that originated with a post on X in February 2025. Vibe coding involves using an AI platform to write code based on prompting for the needs and desires of an application. Code can be generated to make websites, interactive features, mobile apps or to analyze and present data. Vibe coding involves a blend of imagination, prompt engineering and user-experience design to develop product interactions quickly and iteratively. AI is particularly effective at coding support due to its rules-based nature and plethora of code samples publicly available on sites like Stack Overflow and GitHub.

Skills with vibe coding will be necessary for the agentic and archive goals mentioned above to come to fruition. In 2026, I see vibe coding emerging as a core product competency, a way to rapidly prototype experiences by encoding the intent, behavior, and “vibe” of a product before full engineering investment is involved. Vibe coding platforms are quickly emerging, from using coding canvas features in the general LLMs to code-specific products like Lovable, Replit, or Cursor to AI features being integrated into coding platforms like Visual Studio Code or Google Colab.

Related to journalism education, this integration of product and AI brings to the forefront the critical nature of exposing students to AI literacy and judgment, data fluency, product thinking and an appreciation for how algorithmic systems shape news production and distribution. Media curricula must evolve from solely instructing how to produce stories to teaching how media products are designed, delivered and managed within an AI-powered news product ecosystem.

Product professionals will also have the vast responsibility for AI governance associated with ethics, privacy, safety and risk management, for users both within and outside the organization. They will need to define practices for when human review is necessary, develop AI usage guidelines, and communicate and share ethical boundaries for automation. Their cross-functional perspective makes product teams the natural owners of newsroom AI policy that balances innovation and speed with trust and accuracy. In 2026, the media organizations that thrive will be those that understand AI not just as a technology, but as a product transformation partner.