Market Comparisons

4 Reasons Traditional RPA Often Costs More Than Expected and Why LLMs Change the Game

Kognitos

TL;DR

Traditional RPA programs routinely exceed budget expectations due to four hidden cost drivers: high implementation labor, ongoing maintenance, paying for peak capacity that sits idle, and employee time spent in oversight meetings.
LLM-based automation eliminates the need to learn a separate coding language, enabling business users to build automations in plain English and dramatically broadening the available talent pool.
Kognitos uses LLMs to deliver 10X faster automation builds and on average 5X lower total cost of ownership compared to traditional RPA, making a wider range of business processes financially viable for automation.

Sizeable implementation costs.

Ongoing operations and maintenance Costs

Purchasing for peak capacity (software on the shelf).

Employee Time Required (meetings, monitoring etc.).

Automation programs costing more than anticipated has several negative results (outside of the extra $ spent).

1. ROI on projects disappears
2. Disillusionment with automation sets in, slowing down efforts to expand automation.
3. Many processes remain manual as new costs estimates and higher TCO impact the candidacy of different processes.

Despite these costs, thankfully new technologies like large language models (LLM) and generative AI are now coming online and enabling new automation platforms to solve these challenges. Large Language Models and Generative AI (such as GPT-3) utilize machine learning to enable outputs to be created directly from language, thus eliminating the step of a user having to
“Program” in another coding language. Furthermore, unlike traditional automation, these technologies can learn and improve over time, becoming more resilient, flexible and creative (just like people).

In this 4-Part Blog Series, each of these traditional sources of cost will be evaluated in greater detail with 3 segments:

Deep dive on the cost and how it affects automation today.
Description of how LLMs have made new automation technologies viable that solve this challenge.
Analysis of why these matter to businesses and how it affects organizations in the current macro environment.

Cost 1: Implementation: RPA has long sought to bring automation to the business user to move automation away from the purview of developers and equip subject matter experts with the ability to build automations themselves. Attempts at “Citizen Development” unfortunately have largely fallen short for one primary reason: traditional RPA tools are still very technical. To become a skilled RPA developer still requires weeks or months of training, something the average business user can ill afford. As a result, either RPA developers are hired in house, or consulting firms are used to build automations and to make matters worse, as RPA developers (like most coding skillsets) are in short supply in the market, the cost is high.

Solution 1: LLM Empowered Development

LLMs create a direct link between human thought (expressed in language) and computer code (expressed in coding languages). By creating this direct link, the time and effort required to translate from thought to creation (in the form of art, images, movies, writing or even code) is greatly reduced. Furthermore, the need to take training or “learn” a programming language (code) is reduced or in some cases eliminated all together. This is already happening in more technical forms or programming with tools like “Github Co-Pilot” and is now available in process automation with Kognitos.

Kognitos uses both proprietary and open source LLMs to enable users to build automation on average 10X faster than traditional RPA. Kognitos is entirely built in English, step by step, the same way a person would list out how they perform their work. No new interface or tool needs to be learned, and a far larger pool of talent can comfortably build their own automations.

Building Automation Using LLM

As LLMs are dynamic and learn, Kognitos learns the nuance of an organization’s language, operations and processes over time, making them more resilient and creating “examples” that can be leveraged to short-cut the building of other processes within the same organization in the future.

Why This Matters: Faster Implementations = Lower Costs = Higher ROI = More Viable Automation Candidates

With LLM based automation platforms like Kognitos, businesses can more rapidly develop and deploy automation all while incurring a lower labor cost in the implementation stage. This not only helps COEs build momentum to automation programs and exceed internal goals, but reduces the upfront cost required to launch any automation. Speed of implementations not only result in less labor hours required, but also accelerate the payback period of an automation project. Additionally, if the up-front cost of automation is lower, then more processes within a business may now potentially have enough of an ROI to meet internal thresholds.

As mentioned above, we will cover in three additional blog posts the other costs of automation programs, but the reduction of implementations costs by using LLM based automation is a critical step for expanding automation in a business. LLMs are now making this possible by introducing speed to the implementation process and opening up the labor pool for building automations. Both resulting in on average 5X lower TCO then traditional RPA.

Want to Unlock the Power of Generative AI for Your Business Today

How to Evaluate Why Traditional RPA Costs More Than Expected

Pull 12 months of RPA maintenance data across every bot. Collect actual data: developer hours on break-fix, incident tickets, and failed-transaction counts. Most organizations underestimate RPA cost because it is distributed across multiple cost centers. Consolidating the data produces a number that surprises executive stakeholders.
Categorize cost drivers: selector fragility, exception handling, and orchestration complexity. High-maintenance bots fail for specific reasons. Categorizing them reveals whether the root cause is UI instability (addressable by AI vision), exception volume (addressable by LLM reasoning), or process complexity (addressable by orchestration redesign).
Model the LLM alternative: one-time replacement cost versus ongoing maintenance delta. Build a 3-year model: current RPA licensing plus maintenance versus LLM-based automation licensing plus reduced maintenance. The maintenance delta is where LLM-based automation consistently wins because LLMs handle input variability without selector reconfiguration.
Run a parallel pilot on the 3 highest-maintenance bots. Deploy LLM-based automation in parallel with the RPA bot for 30 days. Count maintenance events for each. Real maintenance comparison on your bots is the only credible evidence for a migration decision.
Present measured cost findings to finance and IT leadership. Frame the findings in annual labor cost and opportunity cost terms. Leaders who approved the RPA investment need cost evidence before approving migration. Measured data from your own environment is more persuasive than analyst reports.

Frequently Asked Questions

Traditional RPA (Robotic Process Automation) is software that automates repetitive, rule-based business processes by mimicking human actions on computer interfaces. It frequently costs more than anticipated due to four key factors: sizeable implementation costs, ongoing operations and maintenance costs, purchasing for peak capacity (software sitting unused), and the employee time required for meetings and monitoring. These hidden costs erode ROI, create disillusionment with automation programs, and cause many processes to remain manual because the total cost of ownership is too high.

Large language models (LLMs) create a direct link between human language and computer code, eliminating the need to learn a separate programming language to build automations. Unlike traditional RPA, LLM-based platforms allow business users to describe processes in plain English, dramatically reducing the specialized developer skills required. This lowers implementation labor costs and broadens the talent pool, resulting in automation platforms like Kognitos delivering on average 5X lower total cost of ownership compared to traditional RPA.

The four primary hidden costs that cause traditional RPA to exceed budget expectations are: (1) sizeable implementation costs driven by the need for skilled RPA developers, (2) ongoing operations and maintenance costs to keep bots running as processes change, (3) purchasing software for peak capacity that sits unused most of the time, and (4) the employee time consumed by meetings, monitoring, and oversight of automation programs. When these costs are fully accounted for, many automation projects lose their anticipated ROI and expansion of automation slows down across the organization.

Traditional RPA tools, despite marketing promises of enabling business users to build automations, remain highly technical in practice. Becoming a skilled RPA developer still requires weeks or months of training, which the average business user cannot afford. This forces organizations to hire in-house RPA developers or engage expensive consulting firms to build automations. Because skilled RPA developers are in short supply in the market, the cost is high, making true citizen development largely unachievable with conventional RPA platforms.

Kognitos uses both proprietary and open-source LLMs to enable users to build automation entirely in English, step by step, the same way a person would describe how they perform their work. No new interface or coding language needs to be learned, opening automation development to a much larger pool of talent. Kognitos enables users to build automations on average 10X faster than traditional RPA, and the platform learns the nuances of an organization's language and processes over time, becoming more resilient and allowing future automations to be built even faster by leveraging prior examples.

Businesses should evaluate total cost of ownership (TCO) rather than just licensing fees, factoring in implementation labor costs, ongoing maintenance, and capacity utilization. LLM-based platforms like Kognitos can reduce TCO by approximately 5X compared to traditional RPA, primarily by lowering implementation labor costs and accelerating deployment speed. Faster implementations shorten the payback period on automation investments and lower the ROI threshold, making a broader set of business processes financially viable candidates for automation. Organizations should also consider how quickly the platform can adapt to process changes without requiring developer intervention.