Across two great conversations with agency leaders, state execs, solution providers, and tech innovators, one thing stood out: AI is no longer a side project. It is becoming part of everyday agency operations. The agencies that clean up their data, tighten their processes, and train their teams will see real efficiency gains and new revenue opportunities.
Here are the big takeaways.
Leaders repeatedly emphasized a critical sequence:
Fix the foundation first, then add AI.
Key prerequisites surfaced:
Clean data
AI cannot compensate for poor workflows, outdated AMS fields, or inconsistent documentation.
Core systems in place
Many agencies still struggle with basic tech like workflows, cloud infrastructure, disaster readiness, and user adoption.
Clear problem statements
Start with the outcome, not the tool. Agencies that chase shiny AI apps without clarity waste time and money.
AI policies and governance
ACT provides free templates, guidelines, and risk considerations that every agency should implement before adoption.
Internal Operations
Customer Experience
Policy Checking and Compliance
The biggest wins came from:
Early and frequent training
Teams adopt AI faster with hands-on practice and guided prompts.
Custom prompts and branded templates
Standardized prompts produce consistent results.
Enterprise-grade tools
Enterprise versions of AI tools offer better security and shared workspaces.
Automation for repetitive work
Phone interruptions, document comparison, remarketing prep, and email drafting are top use cases.
Clear leadership communication
When leaders explain the goals and benefits, adoption increases dramatically.
The obstacles holding agencies back included:
Cultural resistance
Long-time staff often distrust automation or worry about replacement.
Prompting skills
AI performs poorly when users provide vague instructions.
Disconnected systems
AI struggles when agencies have fragmented or outdated tech stacks.
Lack of clarity
If owners and managers cannot articulate the purpose of AI, teams cannot follow.
Broken workflows
AI cannot fix processes that do not exist or are not documented.
Several speakers pointed to significant changes in staffing models:
AI is becoming a digital employee
It manages calls, summarizes policies, drafts emails, and finds revenue opportunities.
Hiring profiles are changing
Tech adaptability is becoming as important as industry experience.
AI allows agencies to redeploy people
Instead of replacing staff, agencies are moving them into higher-value roles.
Outsourcing is evolving
As AI automates back-office work, some outsourced tasks may return in-house.