Every week, a potential client asks us some version of the same question: 'Should we use RPA or AI?' The answer is almost always: it depends on the process. And often, the best solution combines both.
After years of deploying automation systems across industries, we've developed a clear framework for making this decision. Here's how we think about it.
Understanding the Spectrum
RPA (Robotic Process Automation) and AI automation aren't competing technologies — they're different tools on a spectrum. RPA excels at structured, rule-based tasks with predictable inputs and outputs. AI automation handles unstructured, variable tasks that require interpretation or judgment.
Think of it this way: if you can write the logic as an if/then flowchart, RPA is your tool. If a human needs to 'read' something, 'understand' context, or 'decide' something ambiguous, that's where AI comes in.
When RPA Is the Right Choice
RPA shines when the process is: repetitive and high-volume, follows a consistent set of rules, operates on structured data (forms, spreadsheets, database records), and interfaces with legacy systems that lack APIs.
Classic RPA use cases we've deployed: data entry between two systems that don't integrate, invoice processing where the format is standardized, report generation from multiple data sources, user account provisioning across multiple platforms, and reconciliation tasks comparing records across systems.
The ROI on pure RPA is typically fast and predictable. If a human is spending 20 hours a week copying data between systems, an RPA bot eliminates that cost immediately. There's no model to train, no dataset to curate, no probabilistic behavior to manage.
When AI Automation Is the Right Choice
AI automation is necessary when the process involves: unstructured data (emails, PDFs, images, natural language), variability in format or content, judgment calls that require 'understanding,' or pattern recognition across large datasets.
Examples from our deployments: classifying incoming support emails by intent and urgency, extracting data from invoices that arrive in 50 different formats, analyzing contracts to flag non-standard clauses, predicting inventory shortfalls based on historical patterns, and generating personalized responses to customer inquiries.
AI automation has a longer setup time and requires ongoing monitoring, but it handles complexity that RPA simply cannot.
The Hybrid Approach: Where the Real Value Lives
Our most impactful deployments combine both. The pattern looks like this: AI handles the interpretation layer (reading, classifying, extracting), and RPA handles the action layer (data entry, system navigation, file management).
For example, in an invoice processing pipeline: AI reads the invoice (regardless of format), extracts key fields (vendor, amount, line items, dates), and classifies the expense category. Then RPA takes those structured outputs and enters them into the accounting system, routes for approval, and archives the document.
Neither technology alone solves the full problem. AI without RPA leaves you with classified data and no action. RPA without AI requires rigid, standardized inputs that rarely exist in the real world.
Our Decision Framework
When evaluating a process for automation, we ask five questions:
1. How structured is the input? If highly structured (forms, CSVs, database records), lean toward RPA. If unstructured (emails, PDFs, images), you need AI.
2. How variable is the logic? If the decision tree is fixed and finite, RPA handles it. If the rules change based on context or require interpretation, AI is needed.
3. What's the volume? High-volume, low-complexity processes favor RPA for fast ROI. Lower-volume, high-complexity processes justify the investment in AI.
4. What's the cost of errors? If errors are expensive (healthcare, finance, legal), you want the determinism of RPA where possible, with AI limited to areas where it's validated.
5. Does the process cross system boundaries? RPA is excellent at bridging systems without APIs. AI adds intelligence to what happens between those bridges.
The Implementation Order
We typically recommend automating in this order: start with pure RPA for the quick wins (high volume, structured, rule-based), then add AI to the processes that RPA can't handle alone, and finally build hybrid pipelines that combine both for end-to-end automation.
This phased approach delivers ROI quickly while building organizational confidence in automation before tackling the more complex AI-powered processes.
Common Mistakes to Avoid
Over-engineering with AI when RPA suffices. If the process follows clear rules and structured data, deploying an AI model adds complexity, cost, and unpredictability for no benefit.
Under-investing in AI when the process demands it. Trying to force-fit RPA onto unstructured processes leads to brittle systems that break on every edge case.
Ignoring the maintenance burden. RPA bots break when UIs change. AI models drift when data distributions shift. Budget for ongoing maintenance, not just initial deployment.
The 2026 Shift: Agentic Automation
It's worth noting that the line between RPA and AI is blurring fast. Major RPA platforms like UiPath are pivoting to what they call 'agentic automation' — combining traditional deterministic bots with AI agents that can reason, plan, and execute complex workflows. This convergence validates the hybrid approach we've been recommending: the future isn't RPA or AI, it's orchestration layers that deploy the right capability for each step in a process.
The right automation approach isn't about choosing a technology — it's about understanding the process. Map the process first, then select the tool that fits.