AI Agents vs. Traditional Automation: What’s the Difference?

AI agents can assist with the unpredictable tasks which currently are difficult for automated tools to accomplish due to their reliance on pre-determined rules. Most businesses have used an automated tool at one time or another (such as a Zapier workflow, email auto-responder, scheduled report, etc.), these tools work very well in routine, predictable situations.

What Traditional Automation Does

Traditional automation follows explicit rules – If you have one of these conditions, take this action. Because of its reliance upon the predictability of input and output as well as volume and repetition of task, traditional automation is best suited for large volumes of repeatable work. Add a new contact to your CRM when they submit their form and send them a welcoming message after adding that contact. Send an invoice for review by a manager once the total has reached a specific dollar amount. Generate a weekly report based on data in your database every Tuesday morning at 09:00.

Deterministic workflow means the same process will always have the same outcome as long as you use the exact same process inputs. The predictability of these types of workflow can be very beneficial but it may create limitations on their potential uses.

Traditional automation cannot work properly if inputs do not fit into pre-programmed categories. For example: If an automated system expects an email to follow a specific format, an email that has a different format than what was anticipated will cause either an error message or incorrectly processed. Any time there is an exception to the expected pattern in data, the traditional automation will fail or generate incorrect results.

What AI Agents Do Differently

The AI agent integrates a capability to perform action(s) with its language capabilities. The AI agent does not follow pre-defined if-then rule sets; it will be able to interpret user input in natural language format as well as ambiguous input from users, understand context and user’s intentions when interpreting user input, make decisions about what actions should occur based upon interpretation of user input and execute multiple step actions across various system types, and adapt to new and previously undefined situations through decision making based on its interpretation of user input rather than using predefined decision-making logic.

The key distinction in this area is flexibility. A “traditional” email sorting system by an automation would likely put an incoming email into one of five possible categories through a series of word searches. On the other hand, an artificial intelligence (AI) agent will read all content in an email, understand what the author intended to express with or without using words that are typical of their expression, determine how urgent the request appears to be based upon contextual information and route it properly — regardless of whether the AI agent has ever seen that type of message.

When to Use Each Approach

Traditional automation remains the best choice for doing large volume data exchanges (synchronizing contact lists from a Customer Relationship Management system and an Email Platform), scheduling tasks that follow rigid logic (nightly back-ups of customer databases, generating reports every week), simple routing of forms to designated team inboxes based upon well-defined criteria, or any other task you can perform where the input will always be known and there will be clearly defined rules.

AI agents are the better choice for processing unstructured inputs (i.e., natural language emails; varying forms of documents; free-form form submissions); tasks that require judgment calls (e.g., is this inquiry urgent?, is this lead qualified?, does this content meet quality standards?); multi-step workflows where each step depends on the output of the previous one and processes with a high degree of input variety which would make rule-based automation infeasible.

The hybrid approach will typically be the best option. Traditional Automation can be used with your Predictable High Volume Workflow pieces. AI Agents should be applied at your Decision Points (i.e., where you need the AI to decide). As an example; An AI Agent reads all Customer Emails that are received by the organization and Classifies them. Then, using traditional automation, based on how they were classified, each classified Email is sent to the appropriate Team, The Support System is automatically populated with the appropriate Ticket information, and the first acknowledgement is generated and automatically sent back to the Customer.

Real Business Examples

Customer support triage. The AI agent will read incoming customer requests and determine which customer request is for what product/service, whether it’s an issue of type or a specific type and how quickly that needs to be resolved. The agent will send the customer’s message to a technician who handles similar issues. This differs from simply sending customer inquiries to a technician based on keywords used by the customer; the agent also looks at the content of the customer inquiry (e.g. if the customer is using language indicating frustration about being late with something) in order to identify the customer’s need for assistance.

Invoice processing. Invoices are delivered to your organization in multiple formats by different suppliers. They can be PDF’s, email messages, or even just photographs. The relevant information (supplier name, amount, date, etc.) is extracted using an AI Agent, no matter what format the invoice was sent in. The extracted information will then be validated against the related Purchase Orders prior to being entered into the Accounting System. Using traditional Optical Character Recognition (OCR)-based methods have failed with varied invoice formats; AI Agents do not have this problem.

Content creation pipeline. An AI Agent will take a previously published blog entry and create various social media posting drafts based on the platform (i.e., shorter draft for X and a longer, more professional draft for LinkedIn). The AI Agent will also suggest images that could be used as well as queue up the postings for Human Review. Once the postings are reviewed and approved by Humans, the standard automated processes will schedule and publish them.

Implementation Considerations

Cost. Traditional forms of automation are significantly cheaper to operate on a per-transaction basis compared to using AI agents. Traditional forms of automation are far less expensive when used for high volume low-value transactions. However, the increased productivity associated with the use of AI agents will be sufficient to justify their higher costs for low volume applications that require significant judgement or critical thinking skills.

Reliability. Business processes driven by AI agents have inherent uncertainties (probabilism) as opposed to traditional methods which provide determinate outcomes. An AI agent may generate output that differs slightly based upon identical inputs. Therefore, it is imperative that such systems are continuously monitored and quality checked throughout the initial deployment phase; particularly for business critical decisions.

Security. Guardrails are necessary for AI agents to operate with security in mind when they will be accessing company information and interacting with other programs/systems. These guardrails include; access controls, audit logs, and content filters to help define how an agent can function.

Start small. Successful implementations of this method typically start by implementing one process. Evaluate performance metrics after thirty (30) days. Refine your process using real world performance data and then build upon that process once you have proven it works.

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For the broader picture, read our AI for Bsiness Guide.