AI Integration Empowers Local Journalism Beyond Efficiency

In a landscape rapidly reshaped by artificial intelligence, Eli Wohlenhaus, Director of Digital and AI News Strategy at Adams Multimedia, offers a grounded perspective on integrating these powerful tools into the often resource-strapped world of local journalism. This conversation reveals not just the practical applications of AI for efficiency, but also the subtle, long-term consequences of its adoption. It highlights how embracing AI thoughtfully can empower journalists to reclaim their core mission, rather than replace them. Anyone involved in local news, media management, or technology adoption will find strategic advantages in understanding these nuanced implications, particularly in navigating the tension between immediate efficiency gains and the preservation of journalistic integrity and community connection.

The Unseen Shift: From Skepticism to Strategic Integration

Eli Wohlenhaus's journey into AI strategy for local news is a compelling narrative of initial skepticism transforming into proactive leadership. He began his career with a traditional journalism aspiration, eventually finding his way into Adams Multimedia, a company with a significant footprint across numerous small and mid-sized markets. His initial reaction to AI integration was one of outright resistance, viewing it through the narrow lens of internet jokes. However, a deeper dive, driven partly by his existing involvement in digital initiatives and a willingness to participate in training, revealed a more complex reality. This personal evolution mirrors a broader industry challenge: understanding AI's potential beyond its most visible, and often superficial, applications.

The core of Wohlenhaus's work at Adams Multimedia involves guiding newsrooms, often operating with lean staffs, toward AI tools that can alleviate the burden of mundane tasks. This isn't about automating storytelling, but about freeing up valuable human hours. He emphasizes that many local newsrooms have seen significant staff reductions over the years, leaving individuals to juggle an overwhelming array of responsibilities. AI, in this context, becomes a tool for reclaiming lost capacity, potentially enabling the return of content types that were once standard but have become unsustainable.

"So a big focus for us has been, how can we take those mundane tasks off these editors' plates? How can we maybe bring some things back that left?"

This sentiment underscores a critical consequence of resource depletion in local news: the erosion of breadth and depth in coverage. Wohlenhaus points to the example of police logs, a traditional journalistic staple that is now feasible to process and format efficiently using LLMs. This isn't just about saving time; it's about enabling the publication of information that might otherwise be lost, thereby maintaining a more comprehensive record of community events. The implication is that by offloading repetitive tasks, AI can help restore a baseline level of journalistic output that has been eroded by economic pressures.

Beyond Efficiency: AI as a Creative Catalyst and Content Multiplier

While efficiency is a primary driver, Wohlenhaus also highlights AI's role as a creative partner and a tool for content diversification. His experience with wedding magazines and military publications demonstrates AI's utility in brainstorming and refining content ideas. Facing writer's block or needing to generate fresh angles for vendor interviews, he found that LLMs like Claude could provide a valuable starting point, offering questions and perspectives he might not have considered. This suggests a second-order effect: AI not only streamlines existing workflows but can also enhance the quality and originality of content by acting as a creative sounding board.

"Well, I was finding myself a little bit with writer's block, what question could I ask? I'd already asked about weather, I'd asked about finances. And so I just put a couple of examples of ones I'd written, and I had Claude in this example give me several other questions that I've been able to use, you know, moving forward with the publications."

Furthermore, Wohlenhaus discusses using AI for more nuanced creative tasks, such as advising on cover photoshoot aesthetics for magazines. By prompting AI with specific contexts--the subject's career, desired color palettes, fabric types--he could gain insights that informed his creative decisions. This moves beyond simple text generation to leveraging AI for strategic creative direction. The downstream effect here is the potential for higher-quality, more engaging published products, which can, in turn, attract and retain readership.

The conversation also touches upon the potential for AI to enable new product development, such as AI-assisted podcasts and video content. While an early podcast initiative didn't gain significant traction, the ongoing exploration of video products with attached advertising opportunities reveals a forward-looking strategy. This highlights a key competitive advantage: the willingness to experiment with new formats, even if initial attempts are not immediately successful. The long-term payoff lies in developing new revenue streams and audience engagement channels that competitors might not yet be exploring.

Navigating the Rapids: Training, Transparency, and Trust

A significant portion of Wohlenhaus's role involves training journalists and editors across Adams Multimedia's network. He notes a surprising trend: older journalists often exhibit more openness to AI than their younger counterparts, a dynamic he attributes to his own journey from skepticism to conversion. This points to a generational or experience-based difference in how AI is perceived, with some younger professionals perhaps seeing it as a direct threat rather than a tool.

Transparency and trust are paramount concerns. Wohlenhaus addresses the fear of job displacement head-on, framing AI as a co-pilot rather than a replacement. He advocates for clear labeling when AI is used in content creation, especially for tasks like rewriting press releases. However, he also distinguishes this from using AI for research or brainstorming, where the human journalist remains the ultimate arbiter of truth and the primary source of original thought.

"How do we tell the audience when something is used? There's no perfect answer for that. I have shown that if I have had a press release, say it's from a police agency, and I have Claude rewrite it in the style of what a journalist would, and then I print that after me, the human being looking at it, of course, then I put a tag on that."

This nuanced approach to transparency is crucial. By citing sources found through AI tools like Perplexity, and then fact-checking and editing the output, journalists can maintain credibility. The implication is that the process of using AI matters. When AI is used to augment human judgment and rigor, rather than replace it, trust can be maintained. The long-term advantage of this approach is building a sustainable model where AI enhances journalistic output without alienating the audience.

Actionable Insights for the Modern Newsroom

• Immediate Action (0-3 Months):

  • Identify Mundane Tasks: Inventory recurring, low-creative-value tasks within your newsroom (e.g., transcribing interviews, summarizing press releases, formatting basic reports).
  • Experiment with LLMs for Efficiency: Begin using tools like Claude or ChatGPT for these identified tasks. Focus on simple prompts to generate initial outputs.
  • Develop Basic Prompting Skills: Train yourself and your team on effective prompting. Start with clear instructions, specifying roles (e.g., "Act as a local news editor").
  • Establish Internal Guidelines: Draft initial, simple guidelines for AI use, focusing on transparency for direct content generation (e.g., rewriting press releases).

• Short-Term Investment (3-9 Months):

  • Explore AI for Brainstorming: Utilize LLMs for generating story ideas, interview questions, or content angles, particularly for special publications or features.
  • Test AI for Content Summarization/Repurposing: Experiment with AI to condense longer articles into social media posts or newsletter snippets.
  • Evaluate AI Transcription Services: Investigate AI-powered transcription tools for faster and more accurate interview and meeting transcriptions.
  • Begin Tracking Time Savings: Quantify the time saved by using AI for specific tasks to build a business case for broader adoption.

• Longer-Term Investment (9-18+ Months):

  • Pilot AI-Assisted Content Creation: Explore using AI to draft initial versions of routine content (e.g., police blotter summaries, event listings) that are then rigorously edited by humans.
  • Investigate AI for New Product Development: Explore AI's potential for creating new digital products like short-form video summaries or AI-assisted podcast segments, with a clear monetization strategy.
  • Foster a Culture of Continuous Learning: Encourage ongoing experimentation and sharing of AI best practices within the newsroom, recognizing the rapid evolution of the technology.
  • Focus on "Jockeying" the AI: Train staff to become adept at directing AI tools, understanding that human oversight and critical judgment remain paramount, especially when sensitive or complex information is involved. This requires patience and a willingness to correct the AI, ensuring accuracy and journalistic integrity.