Guide

Recruiting and onboarding AI talent: Lessons from the Lenfest AI Collaborative and Fellowship Program

What we’ve learned about finding the right people, setting them up for success, and accelerating innovation across news organizations

By David Chivers

August 13, 2025

The Lenfest Institute for Journalism’s AI Collaborative and Fellowship Program, in partnership with OpenAI and Microsoft, helps local news organizations across the United States experiment responsibly and transparently with AI by embedding technical talent inside our grantee organizations. The AI Fellows build practical tools to drive audience and revenue growth, drive cross-functional learning, and push innovation forward in service of local journalism.

As five new organizations join the Collaborative and Fellowship Program, we’ve been sharing strategies for how to successfully recruit and onboard AI talent from the first round of the cohort. This guide, sharing the publishers’ key insights, is built from interviews, working group discussions, and real-world lessons from the people doing the work.

The AI Fellows create research tools, streamline ad operations, improve workflows, and help their host organizations better understand how AI can support their mission. But, their success hasn’t been accidental. It’s the result of careful hiring, thoughtful onboarding, and strong support systems. Plus, a commitment to building responsibility with AI. 

Here’s what we’ve learned so far. And, what anyone hiring for an AI role in a news organization should know.

What makes a great AI hire? 

Hiring for AI talent involves establishing clear qualifications and criteria. We published job descriptions that included combinations of the following: 

• Have 5+ years of technical consulting (or equivalent) experience.
• Are proficient in Python and JavaScript.
• Built and/or delivered prototypes on top of OpenAI’s API platform.
• Led complex technical projects and programs with many stakeholders.
• Can proactively identify opportunities for maximizing business value through leveraging the OpenAI API.
• Own problems end-to-end, and are willing to pick up whatever knowledge they’re  missing to get the job done to ensure both their team and stakeholders succeed.
• Operate with high horsepower, adept at frequent context switching and working on multiple projects at once with expansive ownership, and able to ruthlessly prioritize.
• Thrive in dynamic environments and can navigate ambiguity with ease.
• Have a humble attitude and an eagerness to help others with empathy.

The last one proved the most important. “We had a number of exceptionally strong candidates, all of whom had the technical expertise to succeed in the role,” said T.C. McCarthy, vice president of digital development at Newsday Media Group. “What set our chosen candidate apart was her ability to communicate effectively with our journalists and colleagues across the business – to understand their goals and serve as a mentor.” 

The first round of AI Fellows came from a wide range of backgrounds and the interview pool was even broader: military tech, civic data science, academia, and startups. Not one came from traditional journalism. And yet, each quickly earned the trust of their colleagues across functional areas and in the newsroom by: 

• Asking smart questions 
• Navigating ambiguity with humility 
• Collaborating across departments 

Just as important, they worked with transparency and generosity. The cohort collaborated by sharing code, refining prototypes, documenting progress, helping each other solve problems faster than any individual could alone, and sharing failures as freely as successes. This openness accelerated progress across the cohort, not just inside their own news organizations.

What to look for (and what to avoid)

Not every candidate that was hired as an AI Fellow had every qualification or ideal criteria that was listed in the job descriptions. However, the best candidates demonstrated their compatibility through the interview process, not solely through resume reviews, according to Chicago Public Media’s Vice President of Product Matt Watson. 

“We needed someone who could walk into a room, not know anyone, and start asking questions,” he said. “That’s not on a resume, but it matters so much.”

What worked: 

• AI fluency + translation skills: Fellows who could explain complex technical ideas in plain English were especially effective.
• Mission alignment: Many were drawn to journalism’s public service mission and saw the fellowship as a way to apply their skills for good.
• Fresh perspective: Candidates without newsroom experience often brought sharper, more creative questions to the table, unburdened by traditional ‘media bubble’ assumptions. This external viewpoint can lead to unexpected solutions and help challenge existing preconceptions about how things are done.
• Relationship building: Fellows that spoke proactively about building trust across editorial, tech, and product had the most impact. 

What didn’t: 

• “Vibe coders” who knew the tools but lacked foundational AI understanding.
  Red flag: Inability to explain key differences between foundational model approaches or how models are fine-tuned.
• Academics without real-world experience or senior engineers who weren’t hands-on. 
• Poor internal structure (after hiring): Fellows who weren’t clearly integrated into the team risked becoming isolated. 

Interviewing for the right fit

Cast a wide net. Participating newsrooms had the most success recruiting through civic tech communities, open-source forums, and AI-focused newsletters, in addition to traditional journalism job boards.

Once you’ve built a strong candidate pool, interviews are the best tool to surface who has the right mix of technical skill, communication ability, and mission alignment.

Don’t assume everyone in the interview process will have deep technical knowledge. You’re looking for someone who can both explain and build responsibly with AI. And, fit within your mission-driven culture. 

Interview panels typically include product, tech, and editorial representatives. You don’t need everyone to grill on technical depth. What matters most is understanding how the candidate might show up on your team, with your stakeholders, in your news organization’s culture.

We recommend sharing the structured interview guide (from our colleagues at The Philadelphia Inquirer) across your team and assigning focus areas in advance. You’ll get sharper feedback and avoid redundant questions.

The Philadelphia Inquirer AI interview guide The Philadelphia Inquirer AI interview guide

Here are some suggested topics to cover in interviews  to help assess AI talent across four key areas:

1. AI fluency 

• Can they explain technical topics clearly and concisely?
• Do they understand foundational concepts beyond toolkits or prebuilt models?
• Sample question: “How would you explain the difference between a fine-tuned model and RAG to a non-technical stakeholder?”

2. Product and collaboration mindset 

• Have they worked cross-functionally before with stakeholders outside of product and engineering? 
• Do they understand how to scope and prioritize problems and opportunities? 
• Sample question: “Tell me about a time you helped define a project based on ambiguous or shifting priorities.” 

3. Public-interest orientation 

• Do they care about impact? Can they connect their work to broader civic or journalistic goals? 
• Sample question: “What excites you about bringing AI into a local news setting?” 

4. Communication and team fit

• Are they comfortable with ambiguity? 
• Can they build trust with people who may be skeptical of AI? 
• Sample question: “Tell me about a time you had to persuade a skeptical teammate to try a new approach.” 

Bonus: Look for signs they’ve thought about responsible AI practices.

Example: Candidates who asked about transparency, data sources, or ethical implications of automation stood out.

Onboarding: Set up to succeed

Successful onboarding starts before the first day through planning. This planning should extend beyond just access to tools and initial meetings. Critically think through how your AI hire’s projects will integrate with existing team workflows and sprint cycles. While offering freedom to explore is vital, a lack of predefined process for managing their work can lead to awkwardness or perceived friction with other team members who have strict, often tedious, responsibilities. Having a more focused project proposal from the outset, even if it evolves, can help define boundaries and ensure tangible outputs rather than a collection of smaller, disconnected efforts.

Kati Erwert, senior vice president, product, marketing and public service at The Seattle Times suggested publishers “avoid creating a ‘special unicorn’ who gets to do all the fun innovation work while the rest of the team is just maintaining systems.”

Before your hire starts: 

• Assign a dedicated internal partner or two (e.g. a product manager and/or senior engineer)
• Draft a lightweight project proposal or problem set to ground early work
• Clarify how exploratory work will feed into org-wide priorities

In the first two weeks: 

• Ensure your hire has access to internal tools (Jira, SharePoint, Slack, GitHub, CMS, etc.) 
• Schedule meetings across editorial, product, advertising, tech, and any other relevant departments 
• Create a meeting cadence and include them on existing sprint planning and key team rituals
• Share your newsroom’s AI policy, guidelines, or principles (if available)

From months 3-12: 

• Be open to sharing work with other organizations through demos and reusable code 
• Present learnings at industry events
• Encourage networking among AI colleagues across the industry and participation in external communities of practice

The culture matters 

AI talent success isn’t only about technical skills. It is also about building trust within your organization. 

That means: 

• Prioritizing relationship-builders over solo coders 
• Avoiding “innovation islands” where your AI hire works alone on exciting projects with no team support 
• Tapping into the power of external communities of practice to accelerate learning and reduce isolation. 

The most successful organizations in the Collaborative and Fellowship Program plugged their fellow into something bigger, helping them understand the organization’s business goals, mission, and strategy while facilitating building strong relationships across our cohort.

Getting started: A checklist for hiring and onboarding 

✅ Draft your job description using examples from the Collaborative

✅ Align internally on priorities, reporting structure, and goals

✅ Create a respectful, efficient, and transparent interview process

✅ Map out onboarding: tools, team intros, and a 30–60–90 day plan

✅ Plug into an external community of practice or network of learning 

Our work is still evolving, but the early results are promising. The right structure, support, and community make a meaningful difference for your new AI hire or partner and for your entire organization.

If you’re part of a news organization or civic tech team thinking about what it means to build responsibly with AI, we’d love to hear from you.

To learn more about the work emerging from The Lenfest Institute AI Collaborative and Fellowship Program — or to share your own perspective on AI in local news — contact David Chivers at  [email protected].

About the Lenfest AI Collaborative and Fellowship Program  

The Lenfest AI Collaborative and Fellowship Program is an initiative led by The Lenfest Institute for Journalism in partnership with OpenAI and Microsoft. The program supports local news organizations in exploring and implementing artificial intelligence solutions to enhance business sustainability, audience engagement, and newsroom innovation. Through two-year fellowships, selected newsrooms receive direct funding, AI expertise, and Microsoft Azure and OpenAI credits to develop tools that improve reporting, data analysis, content discovery, and revenue generation.

The program fosters cross-industry collaboration, enabling participating organizations to share best practices, product developments, and technical insights to benefit the broader news ecosystem. By equipping local newsrooms with cutting-edge AI capabilities, the Lenfest AI Collaborative and Fellowship aims to create a more sustainable, ethical, and innovative future for independent journalism.