Creating a robust business case for RPA (Robotic Process Automation) is an essential initial step in assuring more resources for your RPA initiative, much like any significant technological investment.
Despite RPA’s presence in the market for some time and increased use for enterprise-level automation, it’s often overlooked or not allocated a sufficient budget.
In this article, we’ll delve into a pivotal aspect of formulating your RPA strategy: establishing an effective RPA budget.
Key RPA Budget Components
RPA budgeting transcends mere calculation of software costs. This excellent article by Olivia Pan Chong highlights that the direct expenses associated with an RPA bot fall into three main categories: labor costs, software expenses, and infrastructure charges. A summary of each component would look like this:
- Software licensing: this varies depending on whether you choose cloud-based or on-premises solutions, with each having its unique cost structure;
- Development and maintenance: costs for bot creation, monitoring and ongoing support for business areas;
- Infrastructure requirements: critical for on-premises deployments, encompassing server costs and related hardware;
- Training and change management: often overlooked, these are crucial for ensuring smooth RPA integration into existing workflows.
How to accurately estimate RPA costs?
Accurately predicting RPA expenses and returns is crucial for presenting a compelling business case to management and securing necessary funding.
Regardless of whether the work is outsourced or managed in-house, diverse operational models exist for RPA bot development and maintenance, which makes it essential to assess departmental activities and pinpoint potential processes to automate.
This assessment in collaboration with business areas not only sets an appropriate RPA budget but also highlights the technology’s potential benefits, like cost savings, quality improvements, reduced time and errors, better SLA fulfillment, and increased customer satisfaction.
Measuring ROI and quantifying savings
There are different methods and metrics used to measure potential resource savings and potential performance with RPA. Here are my recommendations:
1. Comparing bot vs. human performance and cost
Comparing the performance of bots versus humans is a key aspect in evaluating RPA’s return on investment. This comparison should include the cost savings from using bots against the strategic benefits of reallocating human employees to higher-level tasks.
For instance, the contrast between an employee’s annual cost and a bot’s price often highlights significant financial savings. However, the goal isn’t to replace human workers, but to enhance overall productivity by assisting them with digital tools.
In order to assess this, your team can evaluate the expected reduction in Full-Time Equivalent (FTE) post-automation (an employee’s scheduled hours divided by the employer’s hours for a full-time workweek).
Remember to factor in the often-underestimated design phase labor and resources, as well as the intensive monitoring period following bot deployment. Maintenance activities will also vary based on several factors, including process complexity and bot quality.
There are two more dimensions to consider about cost savings: money saved from preventing human errors in the scalable process and eliminating fines and extra costs from overdue documents and payments, as your automations won’t lose deadlines.
Beyond just cost savings, consider customer speed and satisfaction factors such as support tickets reduction from faster response, improvements on NPS and sales that need fast document check and data fulfilling (like approving a credit), while planning for continuous training and improvement to maximize RPA’s effectiveness.
2. Building advanced orchestration reports and dashboards
Creating sophisticated orchestration reports provides critical insights into high-scale operations and helps CoE teams quickly identify and address operational bottlenecks. Ensure that all automations, whether crafted via intuitive drag-and-drop interfaces or more complex coding, align with the organization’s policies and best practices.
Modern RPA tools like BotCity offer customizable dashboards to measure the ROI of the CoE operations, number of automations in production, hours saved, total cost savings, and more, translating complex data into actionable insights.
That way you can use historical data to predict trends and plan future RPA initiatives, aligning them with business goals.
3. Selecting vendors and predicting robot license needs
Selecting appropriate RPA platforms is crucial for precise cost projections and efficient RPA implementation. Even when partnering with the same vendor, cost implications can vary significantly based on different deployment frameworks or varying process architectures (like unattended, attended, or hybrid).
Understanding the bot allocation and vendor pricing model is vital – whether it’s a per-bot payment system or an option for unlimited bots.
For numerous organizations, underutilized licenses of widely-used low-code platforms lead to unnecessary expenditures. Integrating these platforms with code-centric RPA solutions allows businesses to optimize their return on investment by improving governance and technical team productivity.
This approach ensures that every license, whether for low-code or traditional coding, is fully leveraged.
While user-friendly features such as drag-and-drop interfaces are beneficial, a fundamental understanding of RPA principles and coding remains important to ensure team autonomy and quick
Besides, low-code platforms typically involve corporate licenses per bot or machine. However, with Python RPA, there are no commercial licenses required for development. BotCity, for example, offers an orchestration platform based on a SaaS model, which is pay-as-you-scale, potentially offering up to 80% in savings.
4. Considering Gen-AI and RPA combination
Generative AI applications powered by RPA frameworks are set to transform cost-effectiveness in enterprise intelligent automation. Therefore, having detailed control over every element of both RPA automation and AI processes is vital for fine-tuning generative AI models and ensuring their operation within particular parameters.
Utilizing open-source RPA framework offers the versatility to craft custom algorithms, adapt existing ones, or seamlessly incorporate sophisticated machine learning and AI libraries such as TensorFlow, PyTorch, and various GPT models. This adaptability is essential for tasks that require specialized capabilities in generative AI.
Furthermore, Python RPA’s flexibility in deployment, whether on powerful cloud-based systems or on-premise servers designed for intense computation, makes it a highly adaptable solution for advanced AI applications while ensuring maximum performance.
Need to optimize your 2024 RPA budget?
Strategic planning for an RPA budget is more than just number-crunching; it demands an in-depth comprehension of both present and future business requirements from technology leaders.
Make sure to partner with vendors who not only grasp your specific business area but also provide comprehensive support. In this regard, BotCity stands out with its Maestro platform, revolutionizing code-based orchestration.
BotCity goes beyond mere scheduling and logging of automations. It plays a pivotal role in informing stakeholders, delivering critical insights, and orchestrating high-scale operations effectively.
Recognized by G2 as the RPA platform with the best global customer support, we offer extensive documentation, access to a global forum, and dedicated Slack channels for customer assistance.
Connect with us today to explore how BotCity can transform your RPA journey.
