Core RPA components, when heavily augmented by advanced AI (Artificial Intelligence), can provide reliable, accelerated, and efficient sales order automation. While unstructured data often proves an insurmountable obstacle for RPA alone, it can be deployed as an effective solution to automate repetitive tasks, such as: identifying duplicate data, processing HR information, storing employee data, consistency checks, or copying customer information. Merging this kind of robotic process automation with sophisticated AI, better suited to more complex and nuanced tasks, is the optimal solution to closing the sales order automation gap.
Every year in North America, $7.37 trillion of B2B sales orders are still keyed in manually. This costly and time-intensive process creates costs, detracts from customer service – something a company’s reputation depends on – and harms cash flow. Attempting to capitalize on this area of opportunity and allured by the prospect of RPA integrating seamlessly with legacy systems, many business leaders implement it to automate manual processes and increase employee productivity.
In the context of sales orders, the idea is this: robotic process automation uses rules-based processes to take care of time-consuming and repetitive manual tasks. This leaves tasks requiring a human touch, such as vital recruitment processes, addressing errors with payment and employee dissatisfaction, or focusing on an enhanced customer experience in the hands of a human employee; data entry, payroll-related transactions, extracting customer information, or other time-consuming tasks are handled by the RPA technology, without the need for human intervention.
But in reality, RPA encounters frequent issues when it attempts to tackle anything more complicated than the most linear and sequential, ‘yes’ ‘no’ ‘either’ ‘or’ processes. Automated customer care systems, for instance, are an attractive proposition – but when facilitated by RPA alone, they often prove to be inadequate in fulfilling the high service level demands of the customer. Such ineffective solutions leave gaps that can damage customer experiences and hurt revenue.
RPA alone is not an answer to automated sales order processing. However, RPA working in conjunction with purpose-engineered AI can be. In this blog post, we’ll consider what RPA can do, how it works, its disadvantages, and how it can blend with AI to achieve a more fulfilling sales order automation process.
Many enterprises seek to graduate from manual sales orders or invoice processing to a more automated approach. They turn to automation to reduce their order cycle times, eliminate errors, take their customer service representatives (CSRs) away from low-value, data entry, or repetitive tasks, and re-allocate them to activities that can improve customer satisfaction. Pursuing this digital transformation, many companies look to RPA implementations.
In general, RPA has many practical uses and carries the potential for significant business advantages, including reduced operational costs, enhanced data accuracy, increased job satisfaction, or a boost in customer satisfaction.
The growth of the RPA market is indicative of its momentum. More than 85% of major enterprises worldwide are leveraging RPA somehow. According to Grand View Research, the RPA industry is forecast to reach a net worth of $11 billion by 2027, expanding at a CAGR rate of 34%. With this upward trend and increasing uptake, it’s no surprise that many businesses assume RPA will translate well to sales order processing.
If so much money is being spent on RPA, which can automate sales processes, it must be a smart business choice.
However, things aren’t that simple. RPA use is effective with simple business processes involving straightforward, linear logic. As you add complexity, the disadvantages of RPA come into sharp focus.
The practicalities and limitations of RPA stem from how it works. RPA technology is set up in a way that makes it an ideal solution to automating mundane, repetitive tasks. Still, its relative simplicity prevents the automation of more nuanced business activities, limiting what the software can achieve.
Forrester defines an RPA tool as one that possesses three essential criteria: it must have a low-code functionality in its scripts, it must be capable of integrating with various applications, and it must have built-in configuration, monitoring, and security facilities.
With the ability to integrate with legacy systems and access the data within them, RPA can reliably mimic the actions of a human employee. This means it can accurately carry out routine, sequential tasks that proceed along the lines of unvarying business logic.
However, though the low-code functionality of RPA makes it somewhat easier to work with than other automation software suites, it effectively holds the system back. A relative sparsity of code may be simpler to work with – in theory, but it lacks the depth necessary to ‘understand’ more complex business processes, such as those involving unstructured data – a common feature of real-world sales order documents.
Factor in the technical debt accrued by RPA software, which requires human intervention when scripts fail, and the technology encounters issues. An organization that has implemented RPA alone runs into several potential pitfalls.
According to Ernst & Young, up to 50% of RPA projects fail. Why? Primarily because enterprises try to bring RPA to bear on processes for which RPA isn’t suited. As Forbes explains:
“RPA automates manual, human processes that are highly repetitive (i.e., ‘robotic’). The most common example is data entry or management in one form or another. RPA dramatically accelerates output in these scenarios while eliminating errors and reducing costs… However, RPA is not a silver bullet in digital transformation. At this stage in its maturation, many available tools do not handle complexity well.”
Sales order processing is complex, with subtle business logic critical for processing. The variables change. Confronted with complex business processes like this, the limitations of RPA start to rear up:
While RPA manages to automate repetitive, fixed tasks with inputs or data formats that stay consistent, RPA doesn’t work well with unexpected or frequent changes in its code integration. (This is why McKinsey senior partners have seen several robotics programs badly delayed.)
New business rules are constantly applied when processing sales orders, which involves many updates and changes of often complex business rules. The platforms on which RPA bots interact usually change, and the necessary flexibility is challenging to configure into the bot. The bots are unable to meet the update needs without reconfiguration.
With the problems with reconfiguring RPA bots, companies are forced to allocate staff to protect purchase order fulfillment. Teams spend so much time servicing bots that they could have manually processed the task quicker.
As bots continue to break and fail, an organization faces a running battle to keep the RPA system properly functioning. This may result in even more technical debt as companies struggle to assign staff resources outside standard business hours.
Manual sales order processing was poor for productivity, but an inefficient RPA system that needs constant supervision doesn’t help. The work gets transferred to the implementation team.
None of this is to say that there is no role for RPA – much like optical character recognition (OCR), it can work in tandem with other software to extract and handle data efficiently. With Conexiom, RPA and AI are brought together to cover the more specific and challenging parts of sales order processing.
Because RPA works well for repetitive and straightforward processes, some parts of sales order processing can be accomplished with RPA. This is why the Conexiom solution does contain some RPA components. These components handle a subset of less complicated processes – processes RPA bots are well-suited to.
However, more advanced forms of automation can kick in when the platform constantly updates its rules and codes to accommodate complex order requirements and complex business logic. An AI platform can augment core RPA components to achieve touchless sales order automation software. These AI parts can learn new and complex business rules, make exceptions, and self-correct errors over time.
In this way, AI and RPA working in harmony create the ideal platform for truly touchless sales order automation. RPA can automate all the rule-based tasks, and via machine learning, AI can teach itself to handle more nuanced, business-critical activities where RPA falls short. This results in a more successful, holistic digital transformation, where RPA and AI coexist, play to their respective strengths, and support each other, laying the groundwork for a robust and reliable sales order platform.
If you’d like to learn more about Conexiom’s purpose-built platform for sales, automate your order-to-cash process and start your company’s automation journey, contact us today to request a free demo.