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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

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ... 

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

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ...