If you’ve evaluated new technology recently, you’ve likely noticed how quickly “AI-powered” capabilities become part of the conversation.
From automated emails to workflow triggers to renewal reminders, many capabilities are grouped together. In practice, they serve different roles and are designed to address different types of work.
When automation and AI are treated interchangeably, teams either expect too much from the tools they already have or miss opportunities to get more value from them. In an industry where efficiency, accuracy, and responsiveness directly impact growth, that misunderstanding can quietly hold your agency back.
Let’s clarify the difference between AI and automation and how they can work together.
Automation and AI serve different purposes
A useful way to think about AI and automation is, AI determines what needs to happen, and automation ensures it happens consistently.
Automation: Rules-based execution within your workflows
Automation is a rule-based execution. It follows a pre-defined instruction set. For example, if X happens then do Y. It’s triggered actions, scheduled processes, and structured, repeatable tasks. Well-designed automation ensures consistency, reduces manual effort, and keeps workflows moving.
Common examples include:
- Sending renewal reminders at set intervals
- Assigning leads based on geography or producer
- Generating certificates of insurance from existing data
- Routing service requests to the appropriate team
Automation doesn’t interpret; it executes. And that’s where its strength lies.
AI: Tools can interpret unstructured data to influence decision-making
The National Institute of Standards and Technology (NIST) defines AI systems as those that can make predictions or recommendations based on data to influence outcomes. AI is about interpreting information and supporting decisions, especially when inputs are not perfectly structured. AI can understand unstructured data (like emails, documents, and notes), recognize patterns, generate summaries, and make recommendations.
Common examples include:
- Interpreting an inbound email and identifying the client’s request
- Extracting data from loss runs or ACORD forms
- Drafting client communications based on account context
- Identifying potential coverage gaps or cross-sell opportunities
Where automation brings consistency, AI brings adaptability.
How leading agencies are using automation and AI in harmony
They don’t choose between automation and AI—they combine them intentionally.
From treating automation and AI the same to understanding their distinct roles
Many agencies still rely heavily on rules-based workflows, even as their systems evolve to support more advanced capabilities. That leads to missed opportunities to improve how work gets done.
From overcomplicating with AI to using automation where it works best
If a process is repeatable, predictable, and based on clear rules, automation is the right tool. For example, renewal reminders should be automated because a rules-based workflow will consistently handle this more reliably and efficiently.
From forcing rules on complex inputs to letting AI handle variability
Automation works best when inputs are structured, but many agency processes start with emails, inconsistent documents, or client requests that require interpretation. In those cases, rules alone will fall short, which is where AI adds tremendous value.
Practical use cases of AI and automation
Use case 1: Inbound email triage
AI interprets the email’s intent (claim, endorsement, billing question, etc.). Then, automation routes it to the right team and triggers the appropriate workflow.
This combination results in faster response times, more consistent handling, and less manual triage.
Use case 2: Policy renewal process
AI helps summarize policy changes, highlight potential coverage gaps, and support more personalized client outreach. Automation then ensures those insights are acted on through the appropriate renewal workflows.
Use case 3: Document intake and data entry
AI extracts relevant information from unstructured data while automation moves that data into the appropriate system fields.
The result is reduced manual effort, improved accuracy, and faster processing time.
The bottom line
If your team is spending time on manual triage, slowed down by inconsistent inputs, struggling to keep workflows moving, the issue isn’t lacking technology—it’s how technology is being applied.
The agencies seeing the most progress aren’t necessarily replacing their systems; they’re learning how to get more out of them.
Automation creates consistency. AI introduces adaptability. Used together, they improve how your agency operates without requiring a complete rebuild.
Ready to apply this to your agency?
Understanding the difference between AI and automation is the first step. The next is seeing how those capabilities are already being built into the systems you use every day.
Vertafore is actively advancing how agencies apply both by bringing automation and AI together within real workflows through innovations, like our new Vertafore Velocity AI Platform and the AI-powered agents recently introduced at Accelerate. These agents are designed to support how agencies actually operate, not just theoretical use cases.
Connect with one of our experts to learn more about our Velocity AI Platform and our AI-powered agents.