The understand, simplify, automate (USA) framework has long been critical for robotic process automation (RPA), but its value has only grown as AI raises the pressure to move fast. Organizations that skip straight to automation, whether with bots or AI agents, get the same result: faster failure. Following the USA framework’s three steps keeps your technology decisions grounded in the process work that makes automation succeed.

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Who This Is For

Operations, IT, and leaders at midsized and large enterprises who are under pressure to move fast on AI or automation but want a decision framework that helps them build the right AI and RPA solutions.

In Brief

• The understand, simplify, automate (USA) framework applies to any automation technology, not just RPA. Whether you’re implementing a bot, an AI agent, or an enterprise resource planning (ERP) workflow, the sequence is the same: Understand the process, simplify it, then decide how to automate it.
• The most common automation mistake isn’t choosing the wrong technology. It’s choosing technology before understanding the process. According to a RAND Corporation analysis of 2,400+ enterprise AI initiatives, 80 percent of AI projects fail to deliver business value, twice the failure rate of standard IT projects.
• Process simplification is math as much as it is best practice. A 20-step process running at 95 percent accuracy per step only succeeds 36 percent of the time end-to-end. Reduce the process to five steps, and the success rate rises above 75 percent.
• RPA and AI agents aren’t interchangeable. RPA is the right choice for deterministic, rule-based processes. AI agents handle variability and judgment but at higher cost and lower predictability.
• At Centric Consulting, we pair the USA framework with a continuous improvement structure to make sure automation decisions compound over time rather than quietly unravel.

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The understand, simplify, automate (USA) framework is a process-first approach to automation that applies to any technology, whether it’s a traditional RPA bot, an AI agent, or whatever comes next. Before you automate any process, you must:

• Understand how the process works in practice, not theory
• Simplify by removing unnecessary steps
• Automate after choosing the right technology, but only once you’ve completed the first two steps

If your organization is moving toward AI with the same urgency it once brought to RPA, you’re probably naming the technology first and asking process questions later. However, given the pace of AI, this approach is not ideal. That’s because most organizations are not yet ready for AI at scale. For example, according to a 2026 AI readiness survey by Lucid, only 16 percent of employees say their organization’s workflows are extremely well-documented, and 61 percent report their AI strategy is only “somewhat” or “not at all” aligned with their operational capabilities. So, while the USA framework was built in the context of RPA, its discipline and technology-agnostic approach makes it the foundation of how we approach enterprise automation at Centric Consulting. Here’s why it matters more now than ever.

How to Apply the USA Framework to Any Automation Technology

Seven years ago, “just RPA it” was the answer to every operational headache. Today, the same instinct is playing out with AI. The technology has changed, but the mistake hasn’t: Name the tool first, ask process questions later. RPA taught us this lesson the hard way. Organizations that jumped straight to bot development without understanding or simplifying their processes first ended up automating broken workflows, creating technical debt, and managing workarounds that compounded over time. Companies are making the same mistake with AI today. According to a RAND Corporation analysis of more than 2,400 enterprise AI initiatives, 80 percent of AI projects fail to deliver their intended business value, a failure rate twice that of standard IT projects. However, AI moves faster than RPA, which means a poorly understood process now produces bad results at greater speed and scale. The USA framework doesn’t tell you which technology to use. It tells you how to make that decision well, regardless of which technology you select. Let’s take a closer look at each component of the USA framework: understand, simplify, automate.

Understand

You can’t simplify or automate what you don’t fully understand. That sounds obvious, but in practice, the understand step is the one most likely to get rushed when there’s pressure to move fast. Understanding a process means documenting how it’s executed today, not how it’s supposed to work on paper. Ask:

• Who touches it?
• Where does it break?
• What are the exceptions?
• Where do handoffs create delays?

This discovery work — including creating a process definition document that captures the flow, target systems, and assumptions before any development begins — reveals whether automation is even the right answer, and if so, what kind of automation is best. What you find during discovery also shapes the technology conversation. Today, that conversation is more complex than it used to be. You’re no longer deciding whether to automate or not. You’re choosing between RPA, AI agents, workflow tools, citizen developer friendly low-code platforms, or some combination of all of them. In many cases, the answer lives in your existing tech stack: Microsoft, Salesforce, SAP, and most major enterprise platforms now have AI and automation capabilities built in. Understanding your process first tells you which of those tools is the right fit, whether that’s a deterministic bot for a rules-based task, an AI agent for something that requires some judgment, or RPA running alongside AI to keep outputs controlled and auditable. The process dictates the technology, not the other way around. This discovery work is also where AI can play a more immediate role as a tool for accelerating the analysis itself. “AI can help you along the way for opportunity identification, analysis of data, metric gathering, and pain points and areas for simplification. AI can help along those portions and then be your potential technology to implement,” says Nick Rahn, an architect for our national operational excellence practice and expert in RPA. Using AI to understand a process is very different from using AI to automate one. The first is due diligence. The second comes later after you’ve done the harder work of simplification.

Simplify

Once you understand your process, the instinct is to start automating. Resist it. Simplification is where organizations that get automation right separate themselves from those that don’t, and it’s the step most likely to be cut when timelines get tight. Consider a process with 20 steps running at 95 percent accuracy per step. According to a breakdown from Towards Data Science, your end-to-end success rate is only 36 percent. Reduce that process to five steps, and your success rate goes above 75 percent. Fewer steps mean fewer failure points, regardless of the technology running them. Stephen Durnin, head of operational excellence and automation at Close Brothers, says, “A manual process that bottlenecks will still bottleneck when automated. It’s important to create flow within the process.” Simplification sometimes reveals that automation isn’t needed at all. In one client engagement, the team came in expecting to implement a technology solution. After we worked through the process with them, the answer turned out to be a process change alone. No bots, no AI. Just a better-designed workflow. That outcome is still a win. When you skip simplification, you risk what we call process debt, or what happens when automation gets layered onto a process that wasn’t ready for it. Teams start working around the automation rather than through it, compounding the original inefficiency. Knowing which processes are the right candidates for automation before you build is one of the best ways to avoid it. Once your process is as lean as it can be, you’re ready for the third step. But the question isn’t simply “Should we automate?” It’s “Automate with what?” Rahn says: “The question is ‘Help me with these processes because of X’ — because my organization grew, or I need to focus elsewhere — and then let the technology be driven by what’s found during the process analysis.”

Automate

That decision of what technology to use for automation involves more variables than it used to. A few questions worth working through include:

• What’s already in your tech stack? Most major enterprise platforms now have automation and AI built into their capabilities. Before adding a new tool, check what you’re already paying for.
• Does the process require consistent, repeatable outputs? If so, RPA is likely the right choice for you. Having the same inputs and outputs every time makes results predictable, auditable, and cost-effective. RPA currently runs at roughly $0.001 per task, while AI agents cost between $0.01 and $0.10 per decision. That gap matters at scale.
• Does the process involve judgment, variability, or unstructured inputs? That’s where AI agents add value, but they come with a higher cost and less predictability at current maturity levels.
• What role does RPA play in managing AI risk? Even when AI is the right tool, RPA can run alongside it to keep outputs controlled and auditable, adding a deterministic layer where consistency matters most.

That process-first approach shaped our work with a Midwest energy company. The team tackled invoice processing and vendor reconciliation within Microsoft Dynamics 365. After simplifying the process for the bot, the solution runs deterministically with controlled inputs, controlled outputs, and no surprises. A similar approach drove our engagement with a mid-market manufacturing client, where a broad finance process assessment is now moving into execution with a mix of process changes and technology implementation. For processes where consistency and auditability matter, RPA remains the more reliable choice. That gap between RPA and AI will narrow, but for now, the decision deserves careful thought rather than assumption. The USA framework provides a repeatable process for making sound automation decisions. After you decide what to automate (and how), establish monitoring and governance to make sure those decisions compound over time rather than quietly unravel.

Make the USA Framework Stick With Monitoring and Governance

Most automation programs that stall do so not because the technology fails but because companies fail to implement a structure to govern, scale, and measure automation as a strategic capability. Monitoring and governance aren’t a fourth step — they’re the operational environment in which the USA framework runs. How much infrastructure you need depends on the technology you’ve chosen. Deterministic processes running on RPA are predictable by design, which keeps oversight manageable. Introduce AI agents, and governance becomes nonnegotiable. Without it, what started as efficiency gains can turn into technical debt, compliance risk, and wasted investment as teams build redundant solutions and inconsistent practices that create security gaps. The most effective approach pairs the USA framework with a continuous improvement structure, whether that’s a dedicated RPA center of excellence, a Lean or Six Sigma team, or both. That structure creates the feedback loop that tells you whether your automations are delivering, where they’re breaking down, and which processes are ready for the next round of simplification. Used that way, the USA framework isn’t a one-time checklist but a discipline you run repeatedly as your processes, organization, and tools continue to evolve.

Conclusion: The RPA Framework That Outlasts the Hype

Every few years, a new technology arrives with the promise of solving operational complexity faster than anything before it. RPA was that technology. Now AI is. The promise is real, but the shortcut isn’t. The organizations that get the most out of automation are the ones that do the unglamorous work first, whether that’s a bot handling invoice reconciliation, an AI agent managing exceptions, or a process redesign that needed no technology at all. These organizations understand the process before they touch it, simplify before they build, and let the problem choose the tool. The USA framework won’t tell you which technology to buy or how fast to move, but it will keep you from automating the wrong thing in the wrong way at the wrong time. When the pressure to move fast has never been higher, that discipline is worth more than any individual tool in your stack. If you’re evaluating where to start or looking to get more out of an existing automation program, our team works with organizations across industries to do exactly that.

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