Source: Quorum AI Inner Circle discussion with Manuel Gomez and Jonathan Scharff, and additional insights from the Quorum AI Summit.
For many public affairs teams, the biggest obstacle to AI adoption isn’t the technology itself. It’s trust — and the human side of change.
While some organizations are already using AI to analyze legislation, draft stakeholder communications, and streamline reporting, others are still debating whether employees should be allowed to use AI tools at all.
In conversations across Quorum’s AI Inner Circle and at the AI Summit hosted this spring, public affairs leaders and change management experts talked about how their organizations tackled AI adoption. They faced pushback but found ways to create frameworks that foster responsible innovation.
The key takeaway? Successful AI adoption isn’t just about picking the right tool. What’s arguably more important is building a culture that encourages experimentation, good governance, and trust.
Make It Emotional Before You Make It Operational
One of the most consistent themes across both conversations: successful adoption starts with people, not process.
A leadership strategist and advisor who has coached executives across Fortune 500 companies and government put it plainly — leaders who roll out AI by leading with the tool and the process, while skipping the people piece, consistently face the strongest resistance. “Adoption starts with people, not process,” she said. “You have to make it emotional before it becomes operational.”
That means meeting your team where they are. Are they using AI personally? What’s their general attitude toward change? Are they skeptical, enthusiastic, or quietly using it on their own terms while keeping it off their work profile? Leaders who acknowledge all of those realities — rather than issuing a blanket mandate and moving on — are the ones who actually close the gap.
Start With Governance, Not Restrictions
One of the most common misconceptions about AI adoption is that organizations need to choose between innovation and control. The most successful teams are doing both.
Manuel Gomez, Manager of AI, Data, and Advocacy at Independent Sector, described how his organization is formalizing AI governance by creating a cross-functional AI committee. “We want managers, but we want leadership. We want the legal perspective, the IT perspective, communications, public policy. Because it’s a big decision.”
Rather than leaving AI decisions to a single department, the organization is bringing together stakeholders from across the business to evaluate tools, establish policies, and create shared accountability.
Jonathan Scharff, Vice President of Audience Intelligence & Performance at Avoq, described a similar approach. “What really brought us together was that we created a task force with leadership and leaders from different practice areas to talk through what people wanted to accomplish.” The result wasn’t a restrictive policy — it was a roadmap.
The Most Important Question Is Trust
When organizations hesitate to adopt AI, the concern is rarely about productivity. It’s almost always about security, privacy, and risk.
According to Manuel, trust sits at the center of every AI conversation. “The big question that we think about AI right now is trust. How you can build trust externally, but also internally.”
That trust starts with clear boundaries. Both AI Inner Circle speakers emphasized the importance of establishing rules around sensitive data, approved tools, and acceptable use cases before encouraging widespread adoption. For Avoq, that meant investing in enterprise AI subscriptions that protect client data and prevent proprietary information from being used to train public models. “The guiding factor was making sure client data is secure. That’s really the one guiding principle.”
The lesson for public affairs teams is simple: you don’t need to eliminate risk. You need to create confidence that risk is being managed appropriately.
Don’t Roll Out AI Everywhere at Once
One of the most practical takeaways from both conversations was the value of starting small. Organizations often feel pressure to create a comprehensive AI strategy before they begin. In reality, most successful programs start with a handful of pilots.
“Baby steps and pilots are the best practice.” — Manuel Gomez
Different teams have different needs. The workflows that benefit a communications team may look nothing like those used in government affairs, policy analysis, or finance. Giving teams room to experiment within clear guardrails allows organizations to learn what works before scaling adoption more broadly.
An executive-level government affairs leader at the Summit drove this home with a real example: when introducing a new public affairs platform to a team of more than sixty people, she didn’t just hand them a tool. She sat down with each function — lobbying, grassroots, PAC — and connected the technology directly to their day-to-day work. “We didn’t just introduce a tool. We actually took it down to, ‘This is what you do on a day-to-day basis. This is how it’s going to make that easier.'” She also built AI adoption into team goals and performance incentives to ensure it wasn’t a one-time exercise. “We’re not trying to be better, faster, smarter. We’re working differently.”
This approach also creates internal success stories that make future adoption easier. Nothing builds confidence faster than seeing a colleague save hours of work using a tool that leadership has already approved.
Address the Fear of Replacement Head-On
A question that came up at the AI Summit captures what many employees are thinking but not saying out loud: When leadership pushes AI, people quietly worry about their jobs — especially in content-heavy roles. How do you address the fear of replacement so people actually feel comfortable using it?
The answer, consistently, was to reframe AI as intelligence augmentation, not automation.
A senior communications leader at the AI Summit described the shift her team made: rather than framing AI as a way to do the same work more efficiently, they started asking how it could help them deliver more in new ways — turning dense monthly reports into interactive HTML summaries or ten-minute audio recaps. “We’re coming up with new ways to deliver more, not necessarily the same, but more in just different ways.”
The leadership strategist echoed the point with an important caution: there is a right and a wrong way to use AI. Using it to augment your judgment, free up time, and tackle repetitive tasks? That’s the goal. Using it to replace your thinking entirely, and then being unable to explain or stand behind what it produced? That’s where it gets people into trouble. “AI is a tool that allows you to do what only you can do.”
Build In a Reflex for Experimentation
One barrier that came up repeatedly: people try AI once, get mediocre results, and write it off. The problem is that the models they tried may have been deprecated entirely since then.
The senior communications leader at the AI Summit was direct about this: “What you did a month ago doesn’t even exist anymore. You have to keep trying.” She described building a deliberate culture of experimentation at her organization — not because it’s easy, but because the pace of change makes it necessary. “If you are not constantly experimenting and trying again, you would be amazed at what you find.”
One practical suggestion from the AI Summit: a standing one-hour monthly session dedicated to open, no-agenda exploration — pitch-and-catch conversations where team members can try things, share what they’ve discovered, and give each other permission to play. No deliverable required, and no wrong answers. Just a protected space to build the experimentation habit over time.
The Conversation Has Changed
Perhaps the most notable theme across both conversations is how quickly the AI conversation is evolving, including how openly people talk about using it.
Just a year ago, many professionals were hesitant to admit they used AI at work. Today, those conversations are happening in the open, and that transparency itself accelerates adoption. Teams are openly discussing how they use AI to draft content, summarize meetings, create dashboards, and analyze data. As Jonathan put it: “Everything you do with it [AI] to create a deliverable has that human-verified aspect to it.” The strongest AI programs aren’t replacing expertise. They’re amplifying it.
Two years ago, organizations were still debating whether AI belonged in the workplace at all. Today, many teams are evaluating which tools are best for specific jobs. “People are recognizing the different strengths of the different models now.” The question is no longer whether AI will become part of public affairs work. It’s already here — embedded in the software teams use every day, from productivity tools to CRM platforms to legislative intelligence systems.
The organizations gaining the most value aren’t necessarily the ones adopting the most tools. They’re the ones creating the culture, governance, and trust required to use those tools effectively.
Four Lessons for Public Affairs Leaders
If you want to get your organization on board with AI, try these steps:
- Create a cross-functional governance group that includes legal, IT, leadership, and business users.
- Establish clear guardrails around data privacy and approved tools.
- Start with pilots rather than enterprise-wide mandates — and connect the technology directly to each team’s day-to-day work.
- Encourage transparency, human oversight, and a culture of experimentation in every AI-assisted workflow.
The future of AI in public affairs won’t be determined by technology alone. And as more teams move from experimentation to implementation, that balance may become the most important competitive advantage of all.
