Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last year, a client asked me to help them implement robotic process automation for their customer support workflows. On paper, it looked perfect—automate repetitive tasks, reduce human error, save costs. The usual RPA pitch that consultants love to sell.
Three months and $50K later, they had a system that broke every time their CRM updated, required constant maintenance, and actually slowed down their support team. The "time savings" never materialized, and they ended up with more complexity than before.
This isn't an isolated case. After working with multiple startups and SaaS companies on automation projects, I've seen the same pattern repeatedly: RPA promises get oversold, and the hidden costs get buried until it's too late.
The automation industry won't tell you about the maintenance nightmares, the integration failures, or the human costs of getting RPA wrong. But I will.
Here's what you'll discover in this playbook:
Why most RPA implementations fail within 12 months
The real cost structure that vendors hide from you
When to choose alternative automation approaches
My framework for evaluating RPA vs other solutions
How to spot red flags before investing in RPA
If you're considering RPA for your business, read this first. It might save you months of headaches and thousands in sunk costs.
Industry Reality
What the RPA industry won't tell you
Walk into any business automation conference, and you'll hear the same RPA success stories on repeat. Vendors showcase impressive ROI calculations, automation consultants promise 80% time savings, and case studies highlight companies saving millions through robotic process automation.
The standard industry pitch follows a predictable pattern:
Identify repetitive tasks - Find manual processes that eat up employee time
Deploy software robots - Configure bots to mimic human actions
Scale across departments - Expand automation to multiple workflows
Measure efficiency gains - Calculate time and cost savings
Reinvest savings - Use freed resources for strategic work
This narrative exists because it sells expensive consulting engagements and enterprise software licenses. RPA vendors have built billion-dollar businesses on the promise that automation equals instant productivity gains.
The conventional wisdom assumes that mimicking human actions through software is the most efficient path to automation. It treats RPA as a universal solution that works regardless of your existing tech stack, team size, or business complexity.
But here's where this approach falls apart in practice: RPA is fundamentally a band-aid solution. Instead of fixing broken processes, it automates broken processes. Instead of integrating systems properly, it creates a fragile layer of screen-scraping robots that break when anything changes.
The industry rarely discusses the maintenance burden, the integration nightmares, or the fact that most RPA projects fail to deliver promised ROI. They definitely don't mention that for many businesses, simpler automation approaches deliver better results at a fraction of the cost.
This conventional wisdom persists because it serves the vendors, not the customers. The reality is messier, more expensive, and far less magical than the sales presentations suggest.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When that client first approached me about RPA, I was honestly excited. They were a growing B2B SaaS company with about 50 employees, processing hundreds of customer support tickets daily. Their team was spending hours on repetitive tasks—updating CRM records, routing tickets, sending follow-up emails.
The client had been pitched by three different RPA vendors, each promising 60-70% time savings on their support workflows. The numbers looked compelling: if they could automate 5 hours of daily manual work, that's 25 hours per week saved. At $25/hour, that's over $30K annually in labor cost savings.
Their specific pain points seemed perfect for RPA:
Manual data entry between their helpdesk and CRM systems
Repetitive ticket routing based on keywords and customer tiers
Follow-up email sequences that required copying data across platforms
Weekly reporting that involved exporting data from multiple tools
What made this case particularly interesting was their tech stack situation. They were using a mix of tools that didn't integrate well—Zendesk for support, HubSpot for CRM, Slack for internal communication, and a custom billing system. Building native integrations would have required significant development resources they didn't have.
I started with the conventional RPA approach. We mapped their workflows, identified automation opportunities, and selected a popular RPA platform. The initial setup took about 6 weeks and cost around $35K including software licenses and configuration.
For the first month, everything seemed to work. The bots were processing tickets, updating records, and sending emails. The team was excited about the "magic" of watching their computers work automatically.
Then reality hit. Their helpdesk updated its interface. The bots broke. HubSpot changed a field name. The bots broke again. A new employee used slightly different terminology in ticket responses. More breakage.
What we thought would be a set-it-and-forget-it solution became a high-maintenance nightmare. Every small change in their systems required bot reconfiguration. The "time savings" were quickly eaten up by maintenance overhead and troubleshooting failed automations.
Here's my playbook
What I ended up doing and the results.
After watching the RPA implementation struggle for three months, I knew we needed a different approach. The core problem wasn't the technology—it was that we were trying to automate broken processes instead of fixing them.
Instead of continuing with screen-scraping robots, I proposed what I call a "platform-first automation strategy." Rather than mimicking human actions, we focused on connecting their tools through APIs and webhooks.
Step 1: API-First Integration Assessment
I audited every tool in their stack to identify native integration possibilities. Zendesk has robust webhooks, HubSpot has excellent API documentation, and their billing system had basic webhook support. Instead of bots clicking through interfaces, we could have systems talk directly to each other.
Step 2: Zapier as the Integration Layer
Rather than enterprise RPA software, we used Zapier to connect their tools. This meant:
When a ticket arrives in Zendesk, Zapier automatically creates or updates the HubSpot contact
Ticket routing happens based on HubSpot customer data, not keyword parsing
Follow-up sequences trigger from CRM status changes, not manual processes
Reporting data flows into a shared Google Sheet automatically
Step 3: Process Redesign Before Automation
This was the crucial difference. Instead of automating their existing messy workflows, we redesigned them first. We eliminated unnecessary steps, standardized data formats, and created clear handoff points between systems.
For example, their old process required support agents to manually check customer subscription status in three different systems. The new process automatically surfaces this data in the ticket interface through API calls.
Step 4: Gradual Implementation and Testing
Unlike RPA's "big bang" approach, we implemented one workflow at a time. Each integration was tested thoroughly before moving to the next. When something broke, it was easy to identify and fix because each automation was isolated and simple.
Step 5: Team Training and Documentation
The beauty of platform-based automation is its transparency. Team members could see exactly what was happening and make adjustments without technical expertise. We documented every automation in their team wiki with troubleshooting guides.
The total implementation took about 8 weeks—longer than the original RPA project—but delivered much more reliable results. More importantly, the ongoing maintenance burden was minimal because we weren't fighting against system updates or interface changes.
Technical Debt
RPA creates layers of brittle automation that break when underlying systems change, requiring constant maintenance and updates.
Hidden Costs
License fees, maintenance, troubleshooting, and staff training often exceed the initial investment by 200-300% in year one.
Integration Complexity
Most RPA tools don't play well with modern SaaS platforms, creating data silos and workflow bottlenecks.
Team Resistance
Employees often work around RPA bots rather than with them, reducing effectiveness and creating shadow processes.
The results from our platform-first approach were significantly better than the original RPA implementation, though not in the way we initially expected.
Reliability Improvements:
The Zapier-based automations had a 99.2% uptime rate compared to the RPA bots' 73% reliability. When automations did fail, they failed gracefully with clear error messages, not silent breakage.
Cost Comparison:
The platform approach cost $8,000 total (including my consulting time) versus the $50K+ RPA investment. Monthly ongoing costs dropped from $2,400 (RPA licenses + maintenance) to $200 (Zapier professional plan).
Time Savings Reality Check:
Instead of the promised 5 hours daily savings, the working solution delivered about 3 hours of genuine time savings. But these were consistent, reliable savings that didn't disappear when systems updated.
Unexpected Outcomes:
The biggest surprise was improved data quality. Because the new automations used actual API data instead of screen-scraped information, data accuracy improved from about 85% to 98%. This had downstream effects on reporting and customer communication quality.
The team also became more comfortable with automation concepts. Instead of seeing automation as "magic black boxes," they understood how data flowed between their tools and could suggest improvements.
Six months later, they were still using the same automation setup with minimal maintenance. The RPA bots, by contrast, would have required multiple updates and potentially complete reconfiguration as their business grew and tools evolved.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Looking back at this project and others like it, several patterns emerge about why RPA often fails and what works better:
1. Process design beats automation technology
The biggest wins came from simplifying workflows before automating them. No amount of sophisticated robotics can fix fundamentally broken processes.
2. API-first thinking reduces technical debt
When systems can communicate directly, you eliminate the fragile middle layer that RPA creates. Updates and changes become manageable instead of catastrophic.
3. Vendor lock-in is a real cost
Enterprise RPA platforms create dependencies that are expensive to escape. Platform-based automation using tools like Zapier, Make, or custom APIs gives you more flexibility.
4. Team adoption matters more than feature lists
The fanciest RPA capabilities are worthless if your team doesn't trust or understand the automation. Simpler, transparent solutions win in practice.
5. Maintenance overhead is always underestimated
RPA vendors quote implementation costs, not ongoing maintenance. In reality, maintaining screen-scraping bots often costs more than the original automation savings.
6. Scale considerations are backwards
RPA is sold as an enterprise solution, but it often works better for large companies with dedicated automation teams. Small and medium businesses benefit more from simpler integration approaches.
7. The "no-code" promise is misleading
While RPA doesn't require traditional programming, it requires significant technical thinking about workflow logic, error handling, and system integration. The learning curve is steeper than vendors admit.
If I were evaluating automation options today, I'd start with native integrations, move to platforms like Zapier or Make, and only consider RPA for very specific use cases where APIs aren't available and the process is truly stable.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups considering automation:
Start with your existing tool APIs before considering RPA
Use integration platforms like Zapier for connecting SaaS tools
Focus on customer-facing process automation first
Budget 3x the quoted price for true RPA implementation costs
For your Ecommerce store
For e-commerce businesses evaluating automation:
Shopify and major platforms have native automation capabilities
Order processing workflows benefit more from platform integrations
Inventory management automation should use your existing systems' APIs
Customer service automation works better with chatbots than RPA bots