Source code, or simply “code” for short, provides the building blocks of software and computing programming. It is the language humans use to build, control, operate and communicate with computer systems. The individuals who have been responsible for writing code (i.e. software engineers, software developers, and computer programmers) are what we colloquially call “coders.” 

For decades, organizations have relied on coders to build and manage their digital ecosystems. From implementing and securing large-scale enterprise systems to developing new digital, customer-facing products. There are few roles more important to a company’s overall business success than that of the coder.  

Given the extensive training and unique skill set required, it should be no surprise that there is a global shortfall of qualified tech talent. This shortage is especially acute when it comes to more cutting-edge skill sets such as data analytics, AI development and blockchain engineering. To make matters worse, demand for software developers and similar roles is projected to increase by 21% by 2028.

The lack of skilled talent available to operationalize the latest wave of automation and technology has long-reaching effects. Not only does it increase the likelihood of employee burnout and delay digital production pipelines, it also curtails general growth. 

Low-code and no-code solutions are one proposed way, of many, to reduce an organization’s reliance on skilled talent to build and operationalize software programs. Low-code solutions are software development platforms that require little to no coding in order to build applications and processes. Instead of relying on traditional code-based programming techniques, low-code platforms allow users to design and deploy applications through graphical user interfaces and configuration tools.  

By providing regular business users the ability to automate routine, repeatable business processes virtually on their own, software engineers and other technical talent can focus on more high value activities such as developing new customer-facing products and managing large-scale enterprise systems. 

In addition - as pretty much anyone can use them - low-code/no-code tools are far easier to scale than traditional enterprise systems.  

What is Low-Code Automation?

As the names suggest, low-code automation (LCA) tools are business process automation (BPA) tools that require only minimal coding to function. In other words, even a person with limited coding abilities could use these tools to automate routine and repeatable business processes. 

As low-code automation platforms often still require some coding knowledge to operationalize, these tools are frequently leveraged by:

• Technical personnel looking to expedite the BPA process. Even though they may have extensive coding experience, it’s simply easier, quicker and more effective to use out-of-the-box LCA tools 
• Teams of technical and non-technical users. For example, a business user might design or “build” an automated workflow using a drop & drag interface while a systems engineer will write and execute the automation script. 

What is No-Code Automation?

No-code automation platforms require no coding knowledge to implement. Similar to low-code platforms, business and technical users automate processes via a graphical, drag & drop interface. The only difference is that absolutely no coding knowledge is required.

Whether leveraged by a business user or systems engineer looking to save time, the ultimate goal of no-code platforms is to bypass the traditional IT development process entirely. This means less time, money and resources are required to automate business processes. 

Though, in the past, no-code and even low-code solutions were really only suitable for automating very simple tasks, this is rapidly changing. With the emergence of RPA, AI and other next generation automation tools, low-code and no-code solutions are emerging as powerful, leading-edge enablers of digital transformation. In fact, according to a recent Forrester report, the low-code automation market was worth $4 billion annually in 2019 and is forecasted to hit $21.2 billion by 2022.

In addition, according to Gartner’s Low-Code Development Technologies Evaluation Guide from 2019, 75% of large enterprises will use at least four low-code development tools for IT application development and citizen development by 2024.

How Does Low-Code and No-Code Automation work?

Simply put, low-code/no-code tools use AI, RPA and ML to automate the automation process. As Laura Stotler of Future of Work News puts it, many popular low-code platforms “use AI, automation and business process management to streamline development of those very same technologies.”

According to Outsystems, low-code development platforms typically consist of at least:

• A visual IDE: An environment for visually defining the UIs, workflows, and data models of your application and, where necessary, adding hand-written code.
• Connectors to various back-ends or services: Automatically handles data structures, storage, and retrieval.
• Application lifecycle manager: Automated tools for building, debugging, deploying, and maintaining the application in test, staging, and production.

Low-Code RPA

Robotics process automation (RPA) is one of the most promising new solutions out there. By automating structured, repetitive processes, RPA dramatically reduces low value work and enables employees to focus on more high-value, strategic activities. If properly utilized, RPA not only increases efficiency but can also save companies a lot of money. 

The promise of RPA has not been lost on the business world. In fact, the global RPA market size is expected to reach USD 25.56 billion by 2027, according to a new report by Grand View Research, expanding at a CAGR of 40.6% over the forecast period. 

However, RPA is not without its risks. To start, RPA bots can be very expensive to implement and maintain. Unless the RPA solution is scaled across the enterprise and widely utilized, the likelihood of delivering meaningful ROI is low. Secondly, RPA can be very “brittle.” Even minor changes to data formats, business requirements, or user interfaces can cause a bot to malfunction or break down entirely.

By simplifying and democratizing the RPA development process, low-code RPA solutions are emerging as potent solutions to these challenges. 

Similar to other low-code and no-code applications, low-code/no-code RPA solutions enable non-technical users to model RPA workflows using a graphical user interface (GUI). Though some coding knowledge is often still required to deploy them, low-code RPA platforms certainly accelerate the RPA production cycle and allow business users to contribute to this process thereby reducing the risk of low user adoption. To see this process in action, watch the short video demo of Automation Anywhere’s low code tool below:

In addition, low code tools make it easier for anyone, not just an RPA developer, to update or modify bots. This eliminates the need to essentially re-build RPA bots in response to every minor change in its operating environment.

*Above image sourced from https://dzone.com/articles/rpa-low-code-saascots-custom-which-is-right-for-yo 

Low-Code and The Democratization of Artificial Intelligence (AI) 

In addition to representing a new cohort of cost effective and user friendly software development platforms, low-code represents a complete shift in mindset. By enabling non-technical staff to automate processes on their own with little to no IT support, low-code tools enable the democratization of AI. 

Making AI and other advanced automation capabilities such as machine learning accessible to as many people as possible is important for a number of reasons. 

1. It reduces entry barriers for individuals as well as organizations to start experimenting with AI.
2. It reduces the overall costs of implementing AI solutions 
3. It not only increases the speed of adoption, it makes AI solutions better by reducing AI bias

For years, technology thought-leaders have made bold promises about how artificial intelligence (AI) and machine learning (ML) will revolutionize the business world. However, as reported in a July 2020 Wired article, only a small percentage of companies, mostly large ones, are currently leveraging these tools. 

According to the article, only 24.8% of companies with more than 250 employees have invested in some form of AI and, even amongst large fortune 500 companies, adoption rates may be much lower than previously reported in other well-established surveys. In part this is due to the high cost and technical expertise required to build and operationalize AI applications.

According to one report by Azati, the average AI application costs upwards of $15,000 and 20 working days to develop. Additional costs involved with prototyping and maintaining the application can easily drive costs up well into the hundreds of thousands or even millions of dollars throughout its lifespan. 

By reducing the time it takes to develop AI as well as IT’s reliance on manual programming, low-code AI tools can dramatically decrease the total cost of AI development. Though low-code AI programming applications are still very much in their infancy, one area that’s starting to take off is low code chatbot implementation; tools such as Amazon Lex,  Microsoft Azure and IBM Watson that enable non-technical users to build chatbots with minimal IT involvement.  

AI Bias

According to Techtarget, AI bias (a.k.a. machine learning bias), “is a phenomenon that occurs when an algorithm produces results that are systemically prejudiced due to erroneous assumptions in the machine learning process.” 

Some high profile examples of this include the failure of autonomous vehicle software to accurately identify black pedestrians as people or Amazon’s sexist resume scanning tool that downscored any resume that included the word “women’s” (i.e. Women’s STEM club president).

AI bias is caused by two things:

• insufficient training data that fails to fully represent key demographics.
• prejudiced assumptions made during the algorithm development process. In other words, AI models have the tendency to embed human and societal biases and deploy them at scale

Though low-code tools can directly contribute to the first driver, they could help mitigate the second by putting AI development tools in as many hands as possible. Afterall, homogenous teams are significantly more likely to build erroneous AIs than ones that have more diverse representation.

Low-Code Automation Challenges & Pitfalls

Training. Though low-code automation platforms are more user-friendly, they still require a certain level of knowledge and expertise beyond coding skills. First of all, everyone from IT to business users have to be trained to use the tool. In fact, some even require the use of their own proprietary coding language. Secondly, low-code implementations also typically require larger scale reskilling and culture shifts amongst business users. For example, a low-code workflow automation tool is useless in the hands of someone who doesn’t understand what workflows are and/or how to optimize them. Others may have to be convinced to take part in the automation process vs. simply submitting a request to IT.

IT should still lead. Just because low-code/no-code tools give business users more ownership over automation projects doesn’t mean IT leaders and those with technical expertise shouldn’t still oversee the project. Quite the contrary, it’s in fact critical for no-code/low-code automation tools to be incorporated into the larger, enterprise technology ecosystem overseen by IT. 

Not only does an environment where low-code/no-code development goes unchecked lead to 

shadow IT - the use of information technology systems, devices, software, applications, and services without explicit IT department approval - it also increases the likelihood that automation projects will fail in production. 

Limited Flexibility & Process Complexity. Though low-code/no-code solutions are rapidly evolving, they may not be best suited for highly complex, large-scale processes that require significant levels of customizations. As every low-code solution automates processes differently, often using proprietary coding languages and approaches, it can also be extremely difficult to move between vendors. 

Examples of Low and No Code Platforms

• Outsystems 
• Appian 
• Mendix
• Catalytic
• Microsoft Power Apps

Last but not least, tech giants such as Salesforce and Amazon understand that low-code extensions are key to maximizing the ROI of their large-scale, enterprise products. In addition to increasing the efficiency in which users can automate new workflows, they also, as ASUG CEO Geoff Scott explains, “empower end users to understand what they're doing a little bit more, and how they can put all of this technology and data to work in their day-to-day jobs and really help drive even more value for their enterprise applications.” 

With that in mind, these companies are constantly releasing new, low-code add-ons to their enterprise solutions such as SAP’s latest Workflow Management and RPA development tool.

Tell us about your low-code automation experience! Share your perspective via the 3-minute survey below:

Create your own user feedback survey

Or access the survey here: https://www.surveymonkey.com/r/BPRBXRR