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Resource Center 4 Mistakes that Stop Organizations from Getting RPA Maintenance and Support Right

4 Mistakes that Stop Organizations from Getting RPA Maintenance and Support Right

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25 Year Anniversary

How to get RPA maintenance and support rightRobotic Process Automation (RPA) forever changed the way modern enterprises manage critical business processes. But robots are never a “set it and forget it” investment – and too many initiatives fall apart because companies overlook proactive RPA maintenance and support as the secret to sustainable success.

Many business leaders don’t realize that robots are trained to specific user interfaces and can’t adapt when something unexpected inevitably occurs, such as updates to an external application. Without a proactive support strategy, Forrester reports that 45% of companies suffer bot breakages weekly or more often.

Constant break-fix cycles that continuously pull bots out of production impact costs, productivity, and customer service. They eat into RPA ROI and the program’s broader business objectives. They also cause organizational confidence in the technology to wane.

A well-structured, dedicated RPA maintenance and support team that can guarantee consistent operation of your bots is critical to achieving long-term automation value. Here are the 4 biggest reasons organizations are getting RPA and support maintenance programs wrong – and how you can get it right:

4 common RPA maintenance and support mistakes
 
 
First RPA maintenance and support mistake

Mistake #1: Having the Development Team Also Provide RPA Maintenance and Support

Too many organizations neglect to create a separate RPA support structure, wrongly assuming their developers will act as emergency response teams for their bots.

But ongoing RPA maintenance and support requires a different set of skills than design and implementation. Many also underestimate the amount of support required as their program scales, distracting developers from innovating and slowing their hyperautomation agenda.

Conversely, prioritizing new developments delays maintenance activities – ultimately increasing downtime and costs.

Turnover also tends to increase when high-value resources are forced to perform monotonous support tasks. While UiPath’s 2021 State of the RPA Developer report found that 71% of developers are responsible for RPA maintenance and support, it ranks among their two most-hated tasks.

Segregating your development and support teams boosts automation quality as well. Not only does handing off support force developers to utilize a standardized development framework that anyone can follow, but it also creates a mechanism for quality control before automations go live.

 
 
Second RPA maintenance and support mistake

Mistake #2: Underestimating the RPA Talent Shortage

Unfortunately, building a separate support team in-house faces an imposing obstacle: a severe RPA talent shortage in the U.S.

Automation resiliency requires more than an ability to perform updates, continuous monitoring, and quick troubleshooting. High-quality support teams also have the expertise to improve automated processes by proactively identifying bottlenecks and other issues in new or existing releases.

Inexperienced resources often struggle to support bots effectively, causing weak automation stability and extended outages.

However, the newness of RPA technology combined with the talent shortage makes it difficult to hire quality staff. “Robotic engineer” ranks among the two fastest-growing jobs on LinkedIn, with UiPath reporting that 77% of organizations aim to hire more RPA developers in the next 12 months.

Nearly half of attendants at a recent Auxis webinar, “Building Your UiPath Automation CoE,” cited finding and retaining talent as the main challenge to scaling their RPA programs. Competition for talent familiar with the market-leading UiPath platform is particularly fierce, as UiPath’s popularity prompts more companies to fight for limited resources.

With demand drastically outweighing supply, employers attempting to staff RPA support teams are paying a premium. The tech unemployment rate was 3% in December, compared to 6.7% overall, and emerging technologies like automation accounted for nearly a third of open jobs.

Retention is another critical issue, with 80% of RPA developers actively seeking new jobs or open to new opportunities in the hot job market, UiPath reports. Support teams tend to experience exceptionally high turnover as resources look to leverage their experience into more innovative, higher-paying development roles.

Currently, replacing tech positions costs companies an average 100 to 150% of salary.

 
 
Third RPA maintenance and support mistake

Mistake #3: Relying on Ad-Hoc, Manual Processes with Limited Automation and Scalability

It’s no secret that scaling automation remains a challenge for most enterprises. Building a strong RPA maintenance and support team is integral to getting it right.

But in the excitement of getting bots up and running, ongoing maintenance is often overlooked and suffers from underinvestment.

Without a structured support program, RPA teams are forced into crisis mode when bots break as they scramble to minimize business impact and restore functionality. Getting bots back online quickly is essential to the success of your RPA program as well, preventing frustrated workers from reverting to manual tasks.

Unfortunately, the diagnostic work required to understand where and why an error occurred on the fly does little to limit downtime.

A dedicated team focused solely on support has the bandwidth to keep up with proactive maintenance tasks as the number of automations grows. They are experts at mitigating the risk of outages by anticipating how system or regulatory changes will impact a process.

Without other distractions, they can also focus on implementing best practices and repeatable processes that enable support services to scale with your RPA journey. For instance, measuring bot health is a key component of proactively minimizing outages and identifying the root cause of issues – tracking metrics like number of incidents per automation, incident types, and evaluation and resolution times.

Exceptional RPA support teams also utilize key technology enablers geared toward moving organizations from reactive to proactive support. For instance, the UiPath platform offers:

Checkpoint: UiPath Suite

UiPath Suite, with mechanisms for clear governance, issue management, tracking open issues, and measuring and reporting SLAs.

Checkpoint: UiPath Insights

UiPath Insights, combining data modeling and analytics that use available business metrics and operational insights to track performance indicators and alert teams to errors.

Checkpoint: Test Suite

Test Suite, enabling continuous, automated testing that resolves defects faster by ensuring every requirement is fully tested before deployment or scheduled runs.

 
 
Fourth RPA maintenance and support mistake

Mistake #4: Treating Every Automation Equally

Business continuity is an RPA support team’s No.1 priority. And some automated processes are more impactful than others to your business.

For instance, a bot that breaks while processing invoices with payables stretching for 90 days causes less pain than a bot that stops processing orders, impacting revenue directly.

Quality support demands a mechanism for assessing automation failures and prioritizing response based on business impact and how urgently a resolution or workaround is needed.

For instance, a good SLA for Priority 1 incidents in the chart below requires a response within 30 minutes and assessment in less than an hour. Requirements for resolution times are typically based on incident type: infrastructure and access incidents can typically be resolved within two hours, while incidents triggered by application updates will depend upon the scope of the change.

Managing outages with impactful processesSource: Achieving Hyperautomation with UiPath Webinar

Since process exceptions are inevitable, setting clear expectations with the business upfront about potential downtime during incidents is integral to a successful RPA journey. It is also important to minimize business pain by developing contingency plans for managing outages with impactful processes.

Nearshore Outsourcing: The Solution for Effective RPA Maintenance and Support

There’s no one-size-fits-all model for a scalable RPA and support maintenance structure. Key factors like organizational size, number of automations, and whether RPA is core to your competencies will determine if an in-house, outsourced, or hybrid approach makes sense.

However, even organizations that keep RPA development in-house are increasingly outsourcing support to providers under their same time zone for the fastest time to benefit and reduced risk.

Native Webinar Recap UiPath Bots

Not surprisingly, more than 60% of businesses are turning to outsourcers and RPA Managed Services companies like Auxis to support their hyperautomation journey, realizing the critical need for time and expertise they don’t possess to safeguard their investment, optimize ROI, and fast-track digital transformation.

Quality RPA implementation firms have the expertise, best practices and technology already in place to keep automations running efficiently and cost-effectively – and can easily scale support as your program grows. RPA nearshore firms can also help you avoid the headache of recruiting and retaining hard-to-find automation resources, delivering consistent access to top talent via a deep pool of high-quality tech resources in Latin America and bigger CoEs that minimize turnover with desirable career paths.

To learn more about how Auxis can help you set up an effective and proactive RPA support model, click here.

A dedicated RPA maintenance and support team under your same time zone sets the course for smooth sailing in your automation journey. But an inability to hire talent or misunderstanding what’s required keeps many enterprises reliant on an ineffective ad hoc model.

3/4/22 8:15 AM

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Eduardo Diquez

Written by

Eduardo Diquez

Eduardo is the Consulting Director of the Intelligent Automation Practice at Auxis, including the ongoing management of its IA Center of Excellence in Costa Rica. With over 10 years of experience in process improvement and finance transformation, Eduardo has a proven track record working with C-level executives in the design and implementation of back office optimization initiatives, with a deep focus on delivering long-term, scalable Automation Programs. Eduardo has successfully delivered over 200 robots to more than 30 different organizations across multiple industries including retail, financial services, hospitality, healthcare, and manufacturing.

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