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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

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Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

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

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...