RPA vs. AI vs. Actually Getting Work Done: A London Business Guide

Last week, a London agency owner told me he was evaluating “RPA solutions” to automate his client onboarding process. When I asked what that meant, he admitted he wasn’t entirely sure—but a consultant had convinced him it was essential for staying competitive.

After spending months experimenting with various automation approaches, from AI development tools to simple workflow improvements, I can give you the honest breakdown most consultants won’t: sometimes the best “automation” solution is just organizing your work better. This is especially true if you’re already spending thousands on failing AI experiments.

Here’s what these terms actually mean and when each approach makes sense for London SMBs.

What These Buzzwords Actually Mean

RPA (Robotic Process Automation): Software that mimics human actions on computer interfaces. Think of it as a robot clicking buttons and filling forms exactly like a person would, but faster and without breaks.

AI Automation: Using artificial intelligence to make decisions and handle tasks that require understanding context, like analyzing emails or generating responses. But before jumping in, consider whether you should use ChatGPT or build your own solution.

Actually Getting Work Done: Improving your processes through better organization, clear communication frameworks, and simple tools—often more effective than either RPA or AI.

When RPA Makes Sense (Spoiler: Rarely for SMBs)

The Sweet Spot: RPA works best for high-volume, repetitive tasks with clear rules. Think copying data between systems thousands of times daily.

The Reality: Most London SMBs don’t have the volume to justify RPA complexity. Setting up RPA requires significant upfront investment, technical expertise, and ongoing maintenance.

Example: If you’re manually entering 50 invoices daily into three different systems, RPA might help. If you’re handling 5 invoices daily, improving your process organization will deliver better results faster.

The Hidden Costs:

• Initial setup and configuration (often months)
• Maintenance when any system interface changes
• Technical expertise to manage and troubleshoot
• Break-fix scenarios when automation fails

Bottom Line: Unless you’re doing the same computer task hundreds of times weekly, RPA is probably overkill.

When AI Actually Helps

The Reality Check: After experimenting with various AI tools, I’ve learned they work best as thinking assistants, not task replacements.

Where AI Delivers Value:

• Content Generation: ChatGPT can draft emails, reports, and documentation faster than starting from scratch
• Analysis and Insights: AI can spot patterns in data or help brainstorm solutions to complex problems
• Development Acceleration: Tools like Cursor can speed up coding, though they require domain expertise to use effectively

Where AI Disappoints:

• Complex Decision Making: AI needs significant guidance for nuanced business decisions
• Reliable Task Execution: My experiments with local AI models often produced repetitive, unusable output
• Understanding Context: AI tools work best with clear, specific instructions rather than vague requests

Real Example: When building the childminder app, AI development tools helped write code faster, but I still needed to understand the problem, guide the implementation, and test the results. AI amplified my capabilities rather than replacing them.

Actually Getting Work Done: The Overlooked Alternative

The Surprise: Often, the biggest productivity gains come from better organization, not automation technology.

Based on experience with remote collaboration challenges, here’s what actually moves the needle for most London SMBs:

Clear Communication Frameworks: Establishing structured ways for team members to share information reduces the miscommunication that typically costs distributed teams significant time.

Simple Process Documentation: Writing down how tasks should be completed eliminates the back-and-forth that wastes hours weekly.

Strategic Tool Selection: Using existing tools more effectively often delivers better results than implementing new automation systems.

Example: Instead of building an RPA system to handle client updates, create a simple template and clear process for generating reports. The childminder app I built focused on this approach—structure the input (photos + simple taps) to generate consistent output (professional reports).

The Decision Framework

Choose RPA when:

• You’re doing the same computer task 100+ times weekly
• The task involves moving data between systems that don’t integrate
• You have technical expertise to maintain the automation
• The volume justifies the setup complexity

Choose AI when:

• You need help with thinking, analysis, or content creation
• You want to accelerate existing expertise (not replace it)
• You can provide clear guidance to the AI tools
• You’re comfortable with imperfect results that need human review

Choose Process Improvement when:

• Your team wastes time on unclear handoffs or communication
• You’re doing manual work that could be organized better
• You need immediate improvements without technical complexity
• You want reliable, predictable results

Real-World Application

Case Study: The childminder reports problem could have been “solved” with:

• RPA approach: Build automation to extract data from multiple sources and populate report templates (months of setup, complex maintenance)
• AI approach: Use AI to write detailed reports from scratch (inconsistent quality, requires significant prompting)
• Actual solution: Structure simple inputs (photos + taps) to generate consistent outputs (3-minute reports vs. 30-minute manual work)

The actual solution focused on organizing the work better rather than adding technological complexity.

Your Next Move

Before investing in RPA or AI automation, ask yourself:

1. Could better organization solve this problem faster and cheaper?
2. Am I trying to automate a process that’s poorly defined to begin with?
3. Do I have enough volume to justify the complexity?
4. What happens when the automation breaks?

These are part of the essential questions you should ask before buying any AI tool.

Most London SMBs get better results from improving their processes first, then selectively adding technology where it genuinely helps. Meanwhile, your competitors might be getting this balance right - but that doesn’t mean you should rush into the wrong solution.

If you’re unsure whether your business needs RPA, AI, or just better organization, book a practical automation assessment. We’ll cut through the buzzwords and focus on what actually improves your operations.

QVXX helps London SMBs make smart automation decisions. We focus on what delivers results, not what sounds impressive in sales presentations.