The Shortcomings of Modern Automation
Modern automation platforms like SaaS, RPA & Low-Code/No-Code promised transformation but failed to deliver, causing new problems instead.
In one of my previous articles, AI is Eating the Jobs, I explored how agentic AI workflows are rapidly replacing entire teams, ushering in a new era of automation that is as transformative as it is disruptive. This is not a new story—automation has been reshaping industries since the Industrial Revolution, from the assembly line to the rise of computers. More recently, trends like Software-as-a-Service (SaaS), Robotic Process Automation (RPA), and Low-Code/No-Code platforms promised to streamline workflows and make businesses more efficient.
The goal of automation has always been to do more with less—but today’s tools aren’t solving problems; they’re creating new ones.
While SaaS platforms became ubiquitous as the backbone of modern business operations, they still depend heavily on human operators to configure, manage, and extract value. RPA and Low-Code/No-Code (LCNC) systems followed, aiming to push automation even further, but they have struggled to live up to their promise. Instead of enabling businesses to scale seamlessly, these tools have left many companies grappling with inefficiency, high maintenance costs, and limited flexibility.
The Shortcomings of SaaS
SaaS platforms revolutionized how businesses use software, offering flexibility and scalability without the headaches of traditional installation or maintenance. Tools like Salesforce, Slack, and HubSpot became the backbone of modern business operations, promising streamlined workflows and improved collaboration.
These tools often fall short when applied to internal automation. They rely on users to manually configure workflows, integrate systems, and monitor operations. For instance, CRMs require sales teams to spend hours inputting and maintaining data, while managers must constantly optimize workflows for reporting and lead tracking. This effort shifts the workload rather than removing it.
Instead of automating work, SaaS often turns employees into operators.
Another issue is fragmentation. Most businesses depend on multiple tools for different functions—one for project management, another for customer support, and yet another for HR. These systems rarely integrate seamlessly, forcing employees to toggle between them and manually move data.
Tool overload adds to the complexity. HR teams might use separate platforms for payroll, recruiting, and employee engagement, each with its own setup and maintenance needs. This patchwork of disconnected workflows creates inefficiencies and demands constant oversight, leaving businesses far from the streamlined operations they envisioned.
The promise of SaaS was to simplify, but its reality often ties automation to human effort.
The Broken Promises of RPA
Robotic Process Automation (RPA) emerged in the 2000s as a solution for handling repetitive workflows. Companies like UiPath, Automation Anywhere, and Blue Prism built multi-billion-dollar businesses on the idea that bots could mimic human actions—clicks, keystrokes, and form fills—to complete tasks like data entry, invoice processing, and customer service triage.
RPA promised to simplify workflows, but in reality, it often creates more work than it saves.
The problem lies in how RPA operates. Bots follow rigid scripts, navigating software interfaces based on predefined rules. These systems work well when processes remain static, but the moment something changes—a form layout updates, a button is renamed, or a vendor introduces a new invoice format—the bot fails. Companies then spend valuable time and resources fixing broken automations.
Take customer service as an example. While bots can triage basic tickets, they often misclassify complex requests, leading to longer resolution times and frustrated users. Similarly, RPA systems used for data migrations frequently stumble on edge cases, requiring constant human intervention.
These issues expose RPA’s fundamental flaw: it doesn’t understand the work—it merely mimics it. This brittleness has left many businesses questioning whether RPA is worth the investment, especially when ongoing maintenance costs often exceed initial implementation savings.
The Frustrations of Low-Code/No-Code
Low-Code/No-Code (LCNC) tools—offered by companies like Retool, Airtable Apps, and Microsoft Power Apps—were designed to democratize software development. Their promise was simple: empower non-technical users to build custom workflows without relying on developers.
For simple use cases, LCNC tools work well. Users can quickly create dashboards, build forms, or design approval workflows. But as workflows grow in complexity, these platforms struggle. Many “no-code” tools require users to write scripts for advanced functionality, undermining the very premise of no-code. Meanwhile, more developer-centric platforms, like Retool, are inaccessible to non-technical teams, leaving businesses stuck between two extremes.
LCNC platforms claim to lower the barrier to entry, but they often introduce new bottlenecks instead—ironically requiring the very expertise they were meant to replace.
Another major issue is the lack of opinionated defaults. Most LCNC platforms provide blank canvases, requiring users to define everything themselves—from data models to integrations. This leads to inconsistent implementations across teams, inefficiencies, and overreliance on technical support.
For example, an HR team might use an LCNC platform to build a performance review tracker, but without predefined templates or best practices, the result is often clunky, hard to maintain, and unable to scale. Similar problems arise when customer support teams attempt to create integrated dashboards without clear guidance.
What the Next Generation of Automation Should Look Like
To truly transform how work gets done, the next generation of automation tools must combine the adaptability of horizontal platforms with the specificity of vertical solutions. Generative AI makes this possible by enabling tools that are both flexible and deeply integrated.
Next-generation automation must be AI-native, opinionated, and seamlessly integrated into enterprise systems.
An ideal platform would:
Leverage AI for workflow creation: Users describe their needs in plain language, and the platform generates a working prototype.
Provide opinionated frameworks: Prebuilt templates and best practices guide users, reducing complexity and ensuring consistency.
Ensure seamless integration: Robust connections to systems of record like CRMs, ERPs, and HR tools ensure enterprise readiness.
Offer enterprise-grade governance: Built-in role-based access controls (RBAC), audit logs, and monitoring tools provide visibility and control.
In upcoming issues, we’ll dive deeper into how to design agentic AI workflows that deliver on this vision.