Software developers have been one of the most challenging jobs to fill in the U.S. for nearly ten years, so it’s no surprise that the developer shortage is set to escalate, with new figures showing a 35% shortfall by 2025.
With analysts predicting that as much as 90% of organizations will be going digital and deploying robotic process automation (RPA) by the end of 2022, this talent gap can significantly impact operational efforts, hiring processes, and growth efforts in every industry.
According to one study, it takes 50% longer to hire talent for tech roles than other positions, and, on average, it takes 66 days to hire the right fit.
IT leaders must figure out how to handle developer talent challenges to ensure their intelligent automation initiatives are not stalled or derailed.
What are software developers, and why is there a shortage?
Software developers are the masterminds behind computer programs and systems. In the realm of intelligent automation, they integrate and manage capture solutions. The solution then gets direct business-critical data from customer communications. Automation then automatically classifies, extracts, validates, and directs business solutions.
Other automation then is focused on identifying, creating, and improving operational processes.
Most of their work focuses on writing code and overseeing and monitoring systems and applications.
Business leadership and knowledge workers depend on the work software engineers do to have access to the large amounts of data within content and processes to be able to discover patterns and insights that can improve customer experiences and better business outcomes.
Technology constantly evolves, which usually leads to increased demand for software developers, but there currently isn’t enough talent.
The widely reported software developer shortage has a considerable impact on enterprises – ranging from overwhelming workloads and halting innovation to not keeping pace with competitors.
Additionally, building intelligent automation projects takes time, often several months to more than a year. While it varies from workflow and complexity of the business process, the time it takes to build and monitor after implementation can be resource-consuming.
One telecommunications company we recently engaged with had 80 bots running continuously, with 45 people managing them. It is quite possible to reduce that to one person.
Automating the automated
Learning to code is similar to learning new languages, but what if you could add code within the enterprise as fast and easy as adding a skill to Alexa to turn on the lights? What if your automation could create and improve other automation?
RPA bots could be the best area to start with this concept, but automating automation can be applicable to almost everything.
For example — automatically capturing, classifying, and distributing customer content during onboarding or account opening ensures error-free. Think verification of data, making it available for business processes.
We’ve heard of building code that can code, and the same concept could be applied to automation that can monitor, understand, and create another automation within a business process.
Then, imagine taking a step further and implementing self-healing automation. Once you create automation, you can continuously monitor it to see how it’s working with process intelligence.
If it’s not working well, you can create alerts that take action and trigger another automation to fix the broken automation. Ultimately, you would make automation that can repair itself.
The self-healing solution can create a cycle where developers are no longer delegated to mundane tasks and have more time to use their creativity to identify new innovation opportunities within the company.
The future of developers demands a new strategy
.Digital transformation has always focused on making processes easier for the business side. IT professionals have been used to manage new, complex technologies and keep them running.
No, and low code
To address the developer scarcity while meeting innovation demands, leaders need to turn to low-code and no-code (LCNC) platforms to make it easier for business users to become citizen developers and be empowered to quickly design, train, and deploy skills to intelligent automation platforms.
In fact, Gartner estimates that by 2024, 75% of large enterprises will have four or more low-code development tools for IT application development and citizen development initiatives.
A growing area within LCNC platforms is adding content intelligence skills to RPA.
The content intelligence skills are added to other automation platforms that enable it to understand, extract and classify content without needing an expert in machine learning.
For example, an accounts payable analyst could add a pre-trained invoice processing skill to enable the bot to read and understand fields within invoices. In addition, pre-trained skills for different document types are now becoming easily accessible from digital marketplaces and can be trained and deployed within days vs. months.
Knowledge workers can be more hands-on with LCNC platforms and get insights from documents to increase productivity and improve operational efficiency.
To illustrate this concept, picture an office worker who uses copy-and-paste from one document or system to another or clicks the same area on a screen dozens and maybe even hundreds of times a day. Copy and paste is a repetitive, mundane routine that is ripe for mistakes.
Imagine a message pops up on the screen from a bot that recommends automating that task? Then an alert would tell the worker when a bottleneck occurs. When automation is on board, the bot will recommend a different workflow to avoid future delays or deviations.
Automated automation and self-healing automation work in tandem to keep the worker’s tasks and overall business processes operating efficiently.
Automation is usually implemented when the business user initiates the automation — not a developer.
As the developer shortage continues and organizations seek to keep a competitive edge in an ever-growing digital world, they must embrace more accessible and more innovative ways of achieving intelligent automation.
Adapt quickly for the digital transformation
Leveraging low-code/no-code platforms with the necessary cognitive skills will help you achieve automating the automated and adapt quickly to meet the rapid, continuous changes in digital transformation.
Image Credit: Christina Wocintechchat; Unsplash; Thank you!