What Is Robotic Process Automation

If you've been looking into automating your support operations, you've probably heard of Robotic Process Automation. Known as RPA, it conjures images of cyborg workers making calls and processing tickets. In reality, it's much less exciting and much more virtual.

Support managers who are buried in manual work want the efficiency RPA can deliver, but often get muddled about what it actually is. Is it a chatbot? Is it AI? Will it stop tickets from flooding your queue?

In this article we’ll discuss what RPA actually is and how it differs from AI agents. You’ll also learn how to determine which kind of automation will solve your ticket volume problem.

What Chatbots and AI Agents Do Differently

One area where teams get hung up is understanding the difference between RPA and AI.

AI deals with conversing with your customers. AI Agents “read” your customer’s message and understand the intent (“I want a refund”). Then it responds or takes action.

They operate directly with your customers and converse with them in natural language. The ultimate goal of many AI agents is to close the ticket without ever involving a human agent.

AI agents can carry on a conversation because they understand context, tone, and intent. They don’t stumble over “ttyl” and “omw” because they know what you mean.

RPA operates behind the scenes, interfacing with your applications. They don’t talk to your customers. They automate the administrative tasks that happen after you receive the request. Your AI agent might speak with your customer and understand they want a refund. The RPA would be the one clicking through your old finance system, button by button, to actually process it.

AI agents talk to customers to gather requests. RPA fulfills the work generated by that request.

Where RPA Fits in Customer Support

RPA handles the repetitive clicking and data entry that keeps agents chained to their screens. Because it works invisibly in the background, customers never see it. What they experience is faster resolution when agents aren't bogged down by administrative grunt work.

Take ticket triage as an example. When a ticket arrives marked "Invoice" in the category dropdown, RPA can automatically route it to the Finance queue. No human needs to manually review and forward it. The agent skips the sorting and jumps straight to solving the actual problem.

The bigger time-saver is what support teams call "swivel chair" work: toggling between disconnected systems to copy-paste the same information. An agent closes a ticket in Zendesk, then has to manually update the legacy CRM because the two systems don't talk to each other. RPA copies those resolution notes from Zendesk and pastes them into the CRM automatically. That's 5 minutes saved per ticket.

Or consider the "Where is my order?" question. The agent needs to check inventory in the ERP, tracking status from the shipping provider, and payment details from the gateway. RPA can pull all that data into a single window so the agent isn't jumping between tabs.

RPA also excels at batch processing. If management approves 500 refund requests, clicking "Confirm" 500 times in an outdated accounting system is mind-numbing and error-prone. RPA can process the entire batch overnight in seconds each, so the work is done by morning.

The key limitation: RPA doesn't reduce ticket volume. The customer still emails you. The ticket still arrives. RPA just handles the administrative side of that ticket more efficiently once it's in your queue.

Where AI Agents Fit

AI agents sit at the front line of your support process. They’re literally your first line of defense, stopping questions before they become tickets.

AI agents don't unthinkingly follow rules like RPA systems do. AI agents powered by large language models (LLMs) understand context. They don't just push data around. They fix problems.

• Providing answers: Agents can retrieve answers from your knowledge base to instantly answer "How-to" questions. "How do I reset my password?" The agent types out the instructions for the customer.
• Taking actions: Action-based AI agents don't have to be limited to answering text inquiries. They can look up order statuses, change shipping addresses, or even cancel subscriptions by connecting to your API.
• Handoff: What if it's unable to resolve the issue? Your AI agent can automatically route the question to a human, including most of the time the full conversation summary, so your agent doesn't have to repeat questions.

This is the fundamental difference between AI agents and RPA. Rather than making your agents click and type faster, AI agents exist to prevent work from reaching your human agents at all. AI agents decrease ticket volume. RPA increases agent throughput.

Which Approach Reduces Support Workload?

To choose between RPA and AI agents, first identify your biggest pain point:

Is your team drowning because work processes too slowly, or because too much work arrives?

Velocity problem: If agents spend 10 minutes per ticket manually extracting data from five legacy systems, that's a velocity problem. Your agents are acting as human middleware, connecting disparate applications. RPA solves this by automating the data entry keystrokes and cutting handle time in half.

Volume problem: If you're drowning simply because too many customers contact you, RPA won't help. You'll just route tickets faster—but the tickets keep coming. Most support teams today face a volume problem. As your customer base grows, the linear hiring model breaks down fast.

AI agents solve volume at the root. Instead of optimizing how quickly agents copy-paste information (the symptom), AI agents resolve tickets autonomously (the cause). Same headcount, exponentially more customers served.

The Challenge with RPA: Brittleness and Breakage

RPA bots operate on your software user interface (UI). They expect buttons to stay in the same place. They expect fields to have the same name. When Salesforce moves the “Save” button two pixels to the right, or Microsoft releases an updated browser version that loads a web page differently, your RPA will break.

This brittleness makes maintenance a constant headache. Studies show up to 50% of RPA deployments fail before completing the pilot phase. That’s because companies underestimate the ongoing IT overhead required to keep bots running as software evolves.

The Challenge with Chatbots: Deflection vs. Resolution

Old-school chatbots have scarred the industry. They were built with one goal: deflection. Keep the customer away from a live agent at all costs.

The result? Automated responses full of useless FAQ links and repetitive loops that frustrated customers more than they helped.

For an AI agent to actually work, it needs resolution power. That means solving the customer's problem, not just bouncing them around.

Resolution requires three things.

First, your AI agent needs access to your knowledge base so it can pull accurate answers.

Second, it needs integration with your backend systems so it can take action (process a refund, update an order, reset a password).

Third, and most importantly, it needs an escalation path. When the AI gets stuck, it should hand off to a human agent seamlessly.

How to Decide What Your Team Needs

Here's how to choose between RPA and AI agents:

Choose RPA if:

Your team is playing "human middleware." They spend hours each day manually copying information between disconnected systems because you have no APIs linking them together. RPA automates those keystrokes.

RPA works best for stable, high-volume back-office tasks like claims processing or invoicing. Just budget for ongoing IT support. These bots break when software updates, and someone needs to fix them.

Choose AI agents if:

Your biggest problem is ticket volume. Your human agents are exhausted from answering the same "Where is my order?" and "How do I reset my password?" questions over and over. AI agents handle these instantly, 24/7, without human intervention.

AI agents also resolve issues, not just route them. If you need something that actually answers the question or performs the task (like processing a refund or updating account details), AI is the answer.

Choose both if:

You have high ticket volume and complex back-office workflows. Deploy an AI agent for the front-end customer conversation. When the issue requires updating a legacy system that the AI can't access directly, the AI hands off to RPA (or a human assisted by RPA) to complete the backend work.

How Helply Approaches This

We designed Helply's AI agent for support leaders who are buried in ticket volume. We're laser-focused on solving just the front-office problem: Decreasing ticket volume by resolving tickets.

Helply isn't an RPA system. We don't build systems that click around your legacy desktop software.

We designed a purpose-built AI Support Agent that talks to your customers and resolves their issues directly.

• Resolution-oriented. Our AI agent doesn't just deflect. It can [verified capabilities only - e.g., query inventory databases, pull Stripe invoice links, alert teams via Slack]. It acts like a Tier-1 agent.
• Safe escalation. If your AI agent can't help, it won't make things up. It will escalate the issue by creating a ticket in your existing help desk ([list verified integrations]) with a link to the full conversation for context.
• Quick setup. RPA projects can take months of mapping and building. Helply is built to be deployed in 15 minutes. There are no workflow builders, no Zapier integrations to manage. You don't have systems to maintain.

Helply keeps the repetitive Tier-1 tickets from reaching your agents, allowing them to focus on complex issues that truly require human touch.

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With a 65% resolution rate guarantee and 15-minute setup, there's no risk in trying it.

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