When the pandemic hit, digital transformation was accelerated, and many companies turned to low-code development in the hopes of winning in the new agile world. Technology aficionados have heard the phrase, “Faster, better, cheaper — pick two.” Fortunately, low-code gives you all three and then some, adding “flexible” to the mix. It’s no surprise that low-code development interest is now surging, as it’s quickly becoming a critical component of a modern enterprise technology stack, with estimated low-code development market growth predicted to exceed 25%, growing from $13B in 2020 to $65B in 2027, according to research from Brandessence Market Research.
With their roots in the rapid application development (RAD) tools of the 1990s, low-code platforms are an application development environment that use graphical user interfaces and configuration instead of traditional hand-coded computer programming. Formal software engineering skills are not required to create applications since a visual user interface in combination with model-driven logic is used. This opens the door to a wider range of people who can build apps for the business. With a little training, employees can rapidly create and deploy secure scalable software. One note to remember–low-code is not interchangeable with no-code, which is a subset of technologies aimed at business users primarily working on enhancing their individual productivity.
It’s impossible to argue against the need and demand for low-code in the enterprise. According to Gartner, more than 65% of application development in 2024 will be performed by low-code platforms. That’s a remarkable shift for a software category that did not exist a decade ago. The number of digital applications and services being built is exploding as well. Between 2018 and 2023, more than 500 million apps will be created according to IDC. To put that massive number into perspective, that’s more than the previous 40 years combined. With low-code, companies can rapidly produce applications within a shorter time span and at a fraction of the cost. Some skeptics may point out the lack of available IT resources, but business users can learn low-code development methodologies quickly, typically in less than one month. Clearly the old way of building apps cannot keep pace with today’s digital marketplace.
To help accelerate use of low-code and scale it across the enterprise, process intelligence is a key enabler. You cannot improve how you operate tomorrow if you don’t fully understand how you work today. And most companies truly don’t understand how they operate on a daily basis, especially at the granular user activity level required to automate a process or streamline a workflow. They have limited process understanding. They don’t know how their applications and data interact, and they don’t really understand what their customers expect.
The impact of this gap in process data is well documented. The 70% failure rate of transformation programs is widely reported. McKinsey pegs the cost at nearly $1 trillion annually and noted that 14% of companies have seen a sustained and material improvement in their business. Another study from Gartner noted only 1% of companies have sufficient understanding of their processes to take full advantage of the technology solutions.
Before embarking on a major initiative, a company must map its processes, its systems and its experiences. Today, that necessary level of operational intelligence just does not generally exist in most companies and on top of that it is very difficult to obtain without process intelligence.
Process intelligence was a similar catalyst for robotic process automation (RPA), but the opportunity with low-code is even greater. RPA programs enjoyed massive early uptake, but the challenge was how to scale the initiatives. Companies still struggle getting more than 50 bots deployed. Once any obvious low-hanging fruit is automated, it becomes difficult to identify what to tackle next and how to tackle it. Process intelligence answers those questions to help scale RPA.
With low-code, process intelligence accomplishes that and more. The combination will overtake RPA as the gateway for AI and automation. Low-code is more efficient and scalable than traditional RPA development because it does not operate at the user interface (UI) layer, making the applications more resilient. Additionally, where RPA is traditionally limited to task-based activities, low-code is much more capable of handling sub-process and process level activities, making it much more valuable to the enterprise. Coupling process intelligence with low-code helps steer an enterprise toward a next-generation operation model that is faster, cheaper, better and flexible. It may also fully realize the promise of citizen developers that RPA struggles to achieve.
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