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Using Low Code to Overcome Day-to-Day Complexities

Anthony Abdulla
Pega

The last two years have accelerated massive changes in how we work, do business, and engage with customers. According to Pega research, nearly three out of four employees (71%) feel their job complexity continues to rise as customer demands increase, and employees at all levels feel overloaded with information, systems, and processes that make it difficult to adapt to these new challenges and meet their customers' growing needs.

Additionally, more than half (56%) of workers expect the pandemic to accelerate these complexities; however, nearly all (98%) believe that although technology contributes to job complexity, it is also important to their success, presenting a bit of a conundrum.

Despite the promises of digital transformation, many workers aren't experiencing the direct benefits. And with business complexity on the rise, organizations need to urgently address the increasingly complex intersection of technology and the workforce to ensure satisfaction and productivity for years to come. Before redefining a new strategy to reduce business complexity, leaders must first understand the key drivers that are rapidly increasing it. Here are a few areas where organizations can look to make improvements to help their workforce adapt and thrive in today's dynamic working environment.

Manage Information Overload

The volume of available data has outpaced workers' ability to process and use it efficiently. With data pouring in from multiple channels and systems, workers must spend more time pulling data from various sources, deciding the next steps, entering key information manually, and re-routing workflows. Workers spend more time on tedious, manual tasks instead of gaining insights and putting their data to work to build superior experiences for customers and employees. And with the increase of digital-first, remote work following the pandemic, more workers are struggling to find and access the data they need quickly to get their jobs done.

Organizations should be thinking about ways to increase access to information across their organizations and simplify the process of attaining it. Leaders should look to unify systems that triage information in a logical way so workers can tackle tasks efficiently. Layering in automation helps surface critical information across systems and silos that workers need to complete their jobs while providing assistance for lower-level issues. Technology like bots and artificial intelligence can handle lower-level tasks, giving workers more time to focus on higher-level issues that require human intervention and empathic thinking.

Adapt to Rapid Change with Automation and Low Code

With remote and hybrid work adding another level of complexity to the way we've traditionally worked, a host of new challenges have emerged. Workers now need to adapt to new digital tools to communicate and collaborate. This is another area where automation comes in. Coupling operations with technologies like machine learning, predictive and adaptive models, and natural language processing (NLP) to create intelligent workflows that bring together the people and information needed to get work done. They allow workers to tap into data, analyze current needs, provide the best responses at that moment, and self-optimize so you can adapt to unexpected situations with confidence.

Additionally, for firms blindsided by workplace changes triggered by the pandemic, struggling to keep pace with competitors, or feeling lost as to where to start — a collaborative, low-code platform provides excellent change management capabilities. Low code can help empower all of your stakeholders — from business users to citizen developers to pro developers — with tools to adapt workflows as needed and bring together the right people throughout the development process to break down silos and improve communication across teams and disciplines. This means faster innovation and better outcomes for both employees and customers.

Provide Resources to Get the Job Done

When workers can automate the mundane, day-to-day tasks that consume their time, they can shift their focus to more nuanced, innovative work that they were hired to do. However, workers want systems that integrate easily with other technologies, as well as access to better training and technology that's easier to use. In fact, more than a third of workers surveyed in the research above feel their company has increased job complexity compared to five years ago.

Instead of piecemealing projects implemented in isolation, businesses need to take a more unified approach to digital transformation. That means not only addressing complexity with the right tools and technologies but combining them with transparent communication of the organization's strategic business goals, as well as re-skilling and training workers to help them meet (and surpass) the expectations of their roles.

Empowering workers with the tools and training they need to perform their jobs effectively will, in turn, make it possible to supercharge workflows to improve customer and employee satisfaction as well as productivity. The companies that will remain competitive are the ones with the right architecture, approach, and understanding of what digital transformation means and communicate it across their organizations.

Anthony Abdulla is Senior Director of Product Marketing at Pega

The Latest

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ... 

Most production autonomous agents do not run in a vacuum. They run inside cloud infrastructure: virtual machines, containers, pods, managed clusters or private servers. That is where most operations teams start monitoring. Is the VM alive? Is the container running? Did the pod restart? Is memory stable? Is CPU too high? Did the health check pass? Those signals are useful. They tell you whether the shell around the agent is alive. They do not tell you whether the agent inside is actually operational ...

Enterprise IT environments have never been more observable ... Yet many organizations still grapple with outages, lengthy incident resolution cycles, and increasing complexity. Most teams do not suffer from a shortage of data. They struggle to determine what deserves attention and what action to take next ... Enterprise IT operations must move beyond monitoring and visibility. The next stage of maturity is decision operations, an approach that helps teams make faster, better-informed decisions ...

Using Low Code to Overcome Day-to-Day Complexities

Anthony Abdulla
Pega

The last two years have accelerated massive changes in how we work, do business, and engage with customers. According to Pega research, nearly three out of four employees (71%) feel their job complexity continues to rise as customer demands increase, and employees at all levels feel overloaded with information, systems, and processes that make it difficult to adapt to these new challenges and meet their customers' growing needs.

Additionally, more than half (56%) of workers expect the pandemic to accelerate these complexities; however, nearly all (98%) believe that although technology contributes to job complexity, it is also important to their success, presenting a bit of a conundrum.

Despite the promises of digital transformation, many workers aren't experiencing the direct benefits. And with business complexity on the rise, organizations need to urgently address the increasingly complex intersection of technology and the workforce to ensure satisfaction and productivity for years to come. Before redefining a new strategy to reduce business complexity, leaders must first understand the key drivers that are rapidly increasing it. Here are a few areas where organizations can look to make improvements to help their workforce adapt and thrive in today's dynamic working environment.

Manage Information Overload

The volume of available data has outpaced workers' ability to process and use it efficiently. With data pouring in from multiple channels and systems, workers must spend more time pulling data from various sources, deciding the next steps, entering key information manually, and re-routing workflows. Workers spend more time on tedious, manual tasks instead of gaining insights and putting their data to work to build superior experiences for customers and employees. And with the increase of digital-first, remote work following the pandemic, more workers are struggling to find and access the data they need quickly to get their jobs done.

Organizations should be thinking about ways to increase access to information across their organizations and simplify the process of attaining it. Leaders should look to unify systems that triage information in a logical way so workers can tackle tasks efficiently. Layering in automation helps surface critical information across systems and silos that workers need to complete their jobs while providing assistance for lower-level issues. Technology like bots and artificial intelligence can handle lower-level tasks, giving workers more time to focus on higher-level issues that require human intervention and empathic thinking.

Adapt to Rapid Change with Automation and Low Code

With remote and hybrid work adding another level of complexity to the way we've traditionally worked, a host of new challenges have emerged. Workers now need to adapt to new digital tools to communicate and collaborate. This is another area where automation comes in. Coupling operations with technologies like machine learning, predictive and adaptive models, and natural language processing (NLP) to create intelligent workflows that bring together the people and information needed to get work done. They allow workers to tap into data, analyze current needs, provide the best responses at that moment, and self-optimize so you can adapt to unexpected situations with confidence.

Additionally, for firms blindsided by workplace changes triggered by the pandemic, struggling to keep pace with competitors, or feeling lost as to where to start — a collaborative, low-code platform provides excellent change management capabilities. Low code can help empower all of your stakeholders — from business users to citizen developers to pro developers — with tools to adapt workflows as needed and bring together the right people throughout the development process to break down silos and improve communication across teams and disciplines. This means faster innovation and better outcomes for both employees and customers.

Provide Resources to Get the Job Done

When workers can automate the mundane, day-to-day tasks that consume their time, they can shift their focus to more nuanced, innovative work that they were hired to do. However, workers want systems that integrate easily with other technologies, as well as access to better training and technology that's easier to use. In fact, more than a third of workers surveyed in the research above feel their company has increased job complexity compared to five years ago.

Instead of piecemealing projects implemented in isolation, businesses need to take a more unified approach to digital transformation. That means not only addressing complexity with the right tools and technologies but combining them with transparent communication of the organization's strategic business goals, as well as re-skilling and training workers to help them meet (and surpass) the expectations of their roles.

Empowering workers with the tools and training they need to perform their jobs effectively will, in turn, make it possible to supercharge workflows to improve customer and employee satisfaction as well as productivity. The companies that will remain competitive are the ones with the right architecture, approach, and understanding of what digital transformation means and communicate it across their organizations.

Anthony Abdulla is Senior Director of Product Marketing at Pega

The Latest

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ... 

Most production autonomous agents do not run in a vacuum. They run inside cloud infrastructure: virtual machines, containers, pods, managed clusters or private servers. That is where most operations teams start monitoring. Is the VM alive? Is the container running? Did the pod restart? Is memory stable? Is CPU too high? Did the health check pass? Those signals are useful. They tell you whether the shell around the agent is alive. They do not tell you whether the agent inside is actually operational ...

Enterprise IT environments have never been more observable ... Yet many organizations still grapple with outages, lengthy incident resolution cycles, and increasing complexity. Most teams do not suffer from a shortage of data. They struggle to determine what deserves attention and what action to take next ... Enterprise IT operations must move beyond monitoring and visibility. The next stage of maturity is decision operations, an approach that helps teams make faster, better-informed decisions ...