As the entire media industry grapples with AI’s rapidly evolving future, I can’t help but see a potential harmony between artificial intelligence and news media. And this prompts me to wonder: What do we truly want this relationship to look like now, in 10 years – and beyond? AI is transforming how we process information, forcing us to confront fundamental issues of trust, ethics, and the sustainable future of journalism.
I’m sure I’m not alone in turning to television, film and books to articulate the ways in which AI may play out in our industry. In this case, my mind goes straight to Star Trek. Data and the Borg offer two starkly different visions of what AI can become. The character Data was built to learn from and with humans. Data is curious and evolving, committed to understanding and serving the people around him. The Borg, on the other hand, are designed to assimilate. Their goal is not growth through understanding but domination through absorption. One AI is shaped around growth and human relationships. The other is built for control.
It is my belief that AI – developed with purpose – can enhance human understanding, much like Star Trek’s beloved Data. At the other extreme, it will indiscriminately devour information, similar to the formidable antagonist Borg. It would yield results that erase our humanity, putting technology and efficiency above helping communities better engage with news media and understand the world around us.
AI design for good news
Audiences now expect access to large volumes of data and information delivered at high speed. What they’re looking for now is clarity and understanding to make sense of what they’re reading and seeing and how it connects to everyday life. Whether it’s a school policy or a change to housing codes, people are beginning to rely more on AI-synthesized insight and summaries pulled from whatever is most available, statistically frequent, and easy to read. That doesn’t always guarantee the information is complete, useful, or meaningful. And over time, these easy (often incomplete) answers shape how people view institutions, public decisions, causes, beliefs and even journalism.
When developers design AI tools to support clarity and accuracy, journalists can focus on the parts of their work that deepen understanding. They can focus on the work of the journalist: asking thoughtful questions rooted in real life, connecting facts in meaningful ways, and highlighting stories that help people see why something happened and what it means next. With the right tools, they amplify voices that often go unheard, raise questions absent from the record, and draw a clear throughline between past events and current developments—showing how decisions take shape, how systems evolve, and how stories gain meaning over time.
AI, much like the character Data, possesses a remarkable ability to process vast quantities of information with incredible speed. AI tools can deliver a clarity that propels opportunities for thought and innovation to move forward. We’re already seeing this in newsrooms today. AI can effortlessly summarize dense material, identify patterns that might otherwise go unnoticed in large datasets, and eliminate some of those tedious manual steps that often slow us down, like transcribing interviews or sifting through public records. This gives journalists more opportunity to delve deeper into their work and operate with heightened focus. Intentional AI design is truly about augmentation, providing a powerful partner rather than a replacement for human ingenuity.
Developing AI tools that serve the audience
Pew Research finds that people continue to view local journalism as absolutely essential. Audiences place their trust in local reporters to deliver accurate information. Many media organizations aim to reduce the workload for (or even the need for) journalists. However, some are beginning to build agentic AI that directly serves audiences. The prospect of creating tools that empower individuals to make everyday decisions with confidence is exciting. It directly builds on that trust.
These tools don’t need to be all-encompassing. They simply have to be useful and intuitively reflect the ways people already seek out information. By design, these AI tools would deliver task-oriented insights, much like explanatory journalism, but do so in an interactive and personalized way.
For instance, newsrooms are already experimenting with tools that help residents visualize how a proposed school budget might impact classroom sizes at the schools closest to them or how a specific zoning decision could influence traffic and housing prices in their very own neighborhood. Another tool might, for example, transform a public health message into something clear and easily understandable. Even better, it might adapt to how an individual comprehends language or processes information.
Wise AI tool design
On the contrary, we must think carefully about how we use AI tools. If we apply them solely to speed up output or increase volume, we risk reinforcing the same pressures that have already strained the industry—pressures that replace depth with summaries and bury real insight in the shuffle. When systems treat all inputs the same, they strip away context and diminish quality.
The value of journalism comes from knowing what questions to ask and why the story matters. AI might spot a spike in illness at a local hospital. However, if a reporter doesn’t dig into who is affected, why it’s happening, and what it means for the community, the human story—and its impact—won’t come through. AI tools can support better access and make the work more efficient. But their impact depends on how they’re guided and who is behind the questions.
We’ve seen before how technology built for efficiency can still deepen harm when no one questions its impact. The cotton gin made cotton production faster, but it also fueled the expansion of slavery across the South. That outcome wasn’t baked into the machine, it emerged through systems that used speed and scale without regard for justice.
Similarly, today’s AI tools generate articles, prioritize content, and process vast datasets in ways that appear productive on the surface. But without human judgment, AI tools fail to explain why something matters, who is being left out, or what risks might follow. The strength of journalism isn’t in speed or volume. It’s in knowing which questions to ask, what stories need depth, and how to connect the facts to people’s lives.
AI is a powerful accelerant for journalism, not a replacement for the invaluable human element. As of today, It excels at streamlining certain tasks, which can allow journalists to dedicate more time to the core of the profession. This has and will remain human-centered storytelling, nuanced interpretation, and the exercise of sound editorial judgment. AI can efficiently surface the foundational “who, what, when, and where.” However, it is the unique human capacity for critical inquiry, lived understanding, and ethical reasoning that uncovers the “how” and “why,” transforming mere facts into meaningful narratives.
The future of journalism hinges on intentionally integrating human ingenuity with technological tools. We can leverage AI for efficiency while unequivocally prioritizing human traits like interpretation, accountability, and narrative depth. These qualities remain paramount in helping audiences not just receive information, but truly comprehend its significance.