Government relations teams are already using AI to write. The bigger payoff in 2027 lies in using it to monitor public meetings while backing insights with traceable evidence.

Every good briefing eventually meets the same question from an executive, client or stakeholder: “How do you know?”

That question has gotten harder to answer. AI-generated slop, disinformation campaigns and sheer volume — eight-hour city council meetings, thousand-page packets and a whirlwind week at the United Nations — all compete for the same limited attention.

On paper, AI adoption in public affairs is already here. In the Public Affairs Council’s AI in Public Affairs 2026 Benchmarking Survey, 99% said their organizations use AI.

But most teams use it to write, not to watch. Eighty-five percent use AI for content, while only 26% use it to track legislative or regulatory trends — the core function of the profession. Just 7% say AI is embedded in strategic decision-making.

Relationships, judgment and trust are still the work. The bottleneck was never writing the memo. It was knowing what the memo should be about and knowing it before everyone else.

Because generic questions get generic answers, AI is only as useful as its understanding of you. Instead of relying on a stale keyword list, teams get far sharper intelligence when they build monitoring around a comprehensive inventory of strategic priorities. Two examples from our experience illustrate this:

The local signal: the meeting nobody mentioned you in

The most important policy signals often don’t even mention your organization or industry by name.

In March, the Arvada, Colorado city council adopted the city’s first Wildfire Resiliency Code, a week ahead of an April 1 state deadline. Nearby Castle Rock also adopted the state code the same night. No company or product came up on the record.

For a materials manufacturer, though, it was a direct market signal. Arvada’s code requires structural hardening in designated wildfire zones—creating binding local demand for fire-resistant roofing on a state timeline, rather than the usual national code cycle.

Having spent years on a local planning commission, I know code updates like this rarely draw a crowd. Traditional keyword alerts miss them because no brand name or bill title matches a saved search. And with more than 91,000 local governments, coverage is a staffing problem disguised as an information problem.

That gap is where AI belongs: connecting a local ordinance to a state mandate to a business impact, while linking directly to a video timestamp.

The global signal: listening to the UN for market priorities

The same principle applies globally. This fall, Locunity supported a global affairs team at an F500 enterprise as they navigated the UN’s High-Level Week. Their question wasn’t “What happened at UNGA?” It was sharper: Where is government demand for, and regulation of, our product heading? Where should our team spend its resources next year?
No single session answered that. The pattern emerged across 200 leaders’ remarks and hundreds of side events. Sovereign AI and trust in contractors bubbled up as top priorities alongside ongoing geopolitical conflicts, while climate change received less attention than ever.

Four workflows to move from experimentation to implementation

Both cases follow the same path: what the team needed, what the public record showed, how it was verified and what someone did differently with the information. Public affairs teams can apply that approach across four core workflows in 2027:

1. How can teams follow congressional hearings without reading every transcript?

   What to watch: The committees, subcommittees, issues and witnesses tied to your priorities, using official calendars, agendas and recordings.
   Where AI helps: Summarizing exchanges that touch your issues on a conceptual level, naming who said what and linking to the exact video timestamp for quick verification.
   Watch out: Testimony isn’t policy, and a witness’s proposal is not committee action. End each briefing with the open questions and the next decision point.

2. How can small teams track bills across multiple states?

   What to watch: A focused watchlist by issue, jurisdiction and stakeholders. NCSL’s databases offer a helpful starting point, while official legislative sites remain the source of truth for bill text, hearings and votes.
   Where AI helps: Flagging substantive amendments and status changes across states, and routing alerts to an assigned owner with an engagement deadline.
   Watch out: Keywords and tags rarely tell the full story; effective monitoring requires conceptual analysis grounded in your organization’s priorities.

3. How do global teams track the UN, WTO and regulators across countries?

   What to watch: The multilateral institutions, negotiations and national regulators tied to your footprint. Pair UN meeting records and speeches with national regulatory publications. For trade, the WTO’s ePing platform covers notifications on technical and sanitary measures.
   Where AI helps: Comparing country positions across languages, organizing commitments and spotting potential coalition partners.
   Watch out: A speech isn’t a deal, and a deal isn’t domestic implementation. Verify translations and be transparent about uncertainty.

4. How can companies, associations and nonprofits cover local government at scale?

   What to watch: City councils, planning commissions and school boards, plus regional bodies like air districts, transit agencies and JPAs. Combine agendas, attachments and recordings.
   Where AI helps: Surfacing relevant items across hundreds of jurisdictions without anyone watching every recording—giving teams time to engage before the vote.
   Watch out: A discussion isn’t a vote, and a continuance isn’t an approval. If a recording or attachment is missing from the municipal portal, flag it explicitly as a coverage gap.

Make trust the operating standard

Every AI workflow needs three safeguards:
Traceable evidence: Separate fact from interpretation, link every claim to a primary record and flag missing documents, uncertain attribution or conflicting accounts. All findings must also be tied to real business goals, policy priorities and risk management to avoid wasting staff time.

Accountable review: Name a human reviewer, disclose meaningful AI assistance and correct errors quickly.

Respectful engagement: Protect confidential strategy and stakeholder data, and never mistake a handful of public comments for broader public opinion.

Measure what matters in 2027
Only 12% say they are formally measuring AI’s impact. If you are new to this kind of benchmarking, start small. Pilot one workflow and test it against what your team knows well. Track four metrics: lead time gained, developments revealed, verification time saved and actions taken. Then ask executives whether briefings explain the evidence, its limits and what to do next. If they can’t, rethink the workflow. At the conclusion, weigh the pros and cons of building a complete system in-house versus buying intelligence services.

Ultimately, AI won’t replace people. Used responsibly, it will give them back time to do what only they can: build relationships, make judgment calls and show up when it counts.

Jonathan Bash is Co-Founder and CEO of Locunity, where his work focuses on building responsible AI tools for public affairs professionals. He previously served as a county chief of staff, planning commissioner, political consultant and journalist.

Locunity is an agentic civic and geopolitical intelligence platform for public affairs professionals, reporters and policy advocates, working to make government decisions understandable, verifiable and actionable.