Automation is supposed to make life easier. But all too often, teams dive headfirst into development only to realize—too late—that their automation doesn’t actually solve the right problem. It’s a frustrating cycle of rework, missed expectations, and bots that break the moment something changes.

So how do you design automations that are scalable, resilient, and deliver real business value—without the headaches?

That’s exactly what ​Matt Stewart, Director in Automation Anywhere’s Community and Learning team, tackled in his recent presentation, From Complex to Conquered: A Design Framework to Deliver Rapid Results. Drawing from real-world experience, Matt introduced a five-stage framework that simplifies automation design and ensures teams focus on business value first—not just automating what already exists.

In this post, we’ll break down his approach, show you why most automation failures start with bad process design, and walk through a real-world example of automation done right.

Why Most Automation Projects Fail (Hint: Bad Process Design)

You’ve seen it happen. An automation project kicks off with excitement—teams are eager, goals are set, and then… it flops. Bots break, rework piles up, and the ROI never materializes. Why? Because most automation failures start with bad process design.

Many teams focus too much on automating what exists instead of asking: Does this process even make sense? If you automate a broken process, all you get is faster inefficiency.

That’s why a strong design framework is crucial. It ensures automations are built for long-term success, not just short-term fixes. It accomplishes this by bringing together all stakeholders and automation team members to speak a common language about the problem that needs solving, and the automation solution Let’s walk through a five-stage approach that simplifies automation design and delivers rapid, scalable results, without the headaches.
 

The Five Stages of Automation Success

Instead of diving straight into a complicated process map, break the design into five core stages that every automation follows:

1. Get Work Item: Where does the work come from? Are you pulling requests from emails, a database, or a mainframe queue? What is the work item? What information comes with that work item?
2. Gather Information: What additional details do you need before making a decision? Think customer records, account history, or policy details.
3. Determine Action: What should happen next? This is where business rules and decision-making come into play.
4. Perform Action: Time to execute. This could be updating a system, submitting a form, or triggering a separate work flow. The action(s) you take is dependent on the decision you made.
5. Output: Where does the completed work go? Does it update a dashboard, send a notification, or feed into another process? What is the downstream impact?

By organizing work into these five stages, teams gain clarity on what’s actually happening, making it easier to spot bottlenecks, dependencies, and opportunities for improvement.
 

How to Avoid Automating Broken Processes

We’ve all heard it: “That’s how we’ve always done it.”

If you automate a process without questioning its purpose, you risk locking in inefficiency. Many existing workflows were designed around outdated systems or temporary fixes that became permanent.

Instead of replicating every step, ask:
What is the actual business goal?
Is this process necessary, or is it work for work’s sake?
Are there dependencies that can be removed or simplified?

A well-designed automation isn’t about mimicking manual work; it’s about redesigning for efficiency.
 

A Real-World Example of Automation Done Right

Let’s take a common scenario: handling customer complaints across multiple teams.

Old Approach: A tangled mess of emails, spreadsheets, and handoffs between five teams, each with its own disconnected process.

New Approach Using the Five-Stage Framework:

1. Get Work Item: A bot monitors the complaint queue and pulls new cases. The complaint case is the work item.
2. Gather Information: The bot retrieves customer details and past interactions from various systems. This is additional information required to determine how to handle the work item.
3. Determine Action: AI (or business rules) suggests the best resolution based on the complaint type, supporting information, and company policies.
4. Perform Action: The bot updates records, triggers refunds (if needed), or escalates complex cases to a human.
5. Output: A summary is logged, and the customer receives a resolution update automatically.

The result? Faster resolutions, fewer errors, and happier customers—without forcing employees to chase down data manually.
 

Ready to See It in Action?

Want to learn how to apply this framework to your automation projects? Watch the full session and discover how to build smarter, scalable automations that actually deliver results.

Watch the Presentation: From Complex to Conquered: A Design Framework to Deliver Rapid Results

​@Matt.Stewart, Director, Community & Learning