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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

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

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

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...