Webinar Recap
Thanks to everyone who joined us for our first webinar, AI 101: From Automation to Creation! We covered the journey from simple, rule-based systems to today's powerful generative models. The single most important takeaway was the fundamental distinction between Automation and AI.
Understanding this difference is the key to thinking strategically about the tools you use every day. This post will help you put that knowledge into practice by showing you how to spot examples of both in your own workflow.
The Core Concept: Rules vs. Predictions
As a quick refresher, here’s the essential framework we discussed:
- Automation follows rules. It’s a deterministic system, meaning it follows explicit, "If-This-Then-That" instructions. For any given input, the output is 100% certain and will always be the same. Think of a calculator.
- AI makes predictions. It’s a probabilistic system, meaning it operates on likelihood. It analyzes vast amounts of data to predict the most probable outcome. The result isn't a certainty; it's a statistically informed guess. Think of a weather forecast.
Where to Find Automation (The Rule Followers)
Automation is the engine for tasks that are repetitive, predictable, and don't require judgment. You can find it in any process that follows a strict set of pre-programmed steps.
Here are a few common examples you might see at work:
- Calendar Scheduling Tools: When you book a meeting with an online tool, it follows a simple rule: If a time slot is marked as busy on the calendar, then it is shown as unavailable. The system doesn't guess your availability; it just follows the rules of the calendar data.
- Automatic Invoicing: In many project management systems, you can set a rule: If a project's status is changed to 'Complete,' then automatically generate and send the final invoice to the client.
- Email Auto-Responders: Your "out of office" message is pure automation. The rule is: If an email is received between two specific dates, then send this pre-written reply.
Where to Find AI (The Prediction Makers)
AI is the engine for tasks that require judgment, classification, or forecasting. These systems are constantly calculating probabilities based on patterns they've learned from data.
Here are some examples of AI making predictions in a typical workday:
- Spam and Phishing Filters: Your email service doesn't have a list of every spammer in the world. Instead, it analyzes incoming emails and predicts the likelihood that a message is junk based on patterns like suspicious links, unusual wording, or sender reputation.
- Sales Forecasting: A modern CRM tool can analyze your team’s historical data, the number of interactions with a potential client, and the deal size to predict the percentage chance that a deal will close this quarter.
- Grammar and Tone Checkers: When you write a message and a tool suggests a better phrase or flags the tone as "too formal," it's not following a strict grammar rule. It's using Natural Language Processing to predict a more effective way to communicate based on patterns from millions of documents.
Your Turn: Become an AI Detective
Now that you can see the difference, you can apply this framework to your own work. As we challenged you at the end of the webinar, take some time this week to analyze a task or bottleneck in your workflow.
Ask yourself the key diagnostic questions:
Is this task deterministic and rule-based, making it a candidate for automation?
Or
Is it probabilistic and pattern-based, making it a candidate for AI assistance?
Making this distinction is the first practical step to harnessing these powerful technologies. It moves AI from an abstract concept to a tangible tool you can use to work smarter.
But what happens when you combine rule-based action with predictive decision-making? That’s where AI Agents come in. These powerful tools use probabilistic AI to understand and decide, and then trigger automations to execute tasks.
Stay tuned for our next webinar, AI Agents: Where Prediction Meets Action, where we’ll explore how these hybrid systems are becoming the new workforce for modern business.