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Creating Better Employee Experiences and Opportunities for Innovation

Anthony Abdulla
Pega

The challenges businesses have endured over the past few years have created more pressure than ever to digitally transform in order to keep pace with rapid change as well as market competition. A survey from McKinsey found that as a result of the pandemic, companies have accelerated the digitization of their internal operations by three to four years, and the share of digital or digitally enabled products in their portfolios has accelerated by seven years. Those are hugely expedited timelines, and they're placing added stress on existing processes, technologies, and the people behind making businesses run smoothly every day.

Looking ahead, businesses need to place more emphasis on providing employees with the tools they need to thrive and be more efficient while also giving them the opportunity to spend less time wading through mundane, repetitive tasks that can stifle productivity. This means refocusing transformation efforts on creating better employee experiences, making technology more human-centric, and leaning on low-code tools to usher in the next wave of innovation. And while it's no easy task, here are four areas to keep in mind to help guide those technology decisions:

1. Give Employees the Same Experiences as Customers

Typically, when comparing an organization's internal and external apps, there's a massive disconnect. Internal apps tend to be lesser-than or are an attempted (but ineffective) repeat of external apps. Organizations are spending their resources on customer-facing applications, but not investing in internal apps. This can be detrimental to a brand, as they're creating different and conflicting experience variations — while customers are getting great in-app experiences, employees are wasting time-fighting internal applications and figuring out their functionality. Not to mention, all employees are also customers and know what a consumer-grade experience feels like.

Employee-facing applications should provide the same seamless, efficient experiences as their external ones. Quality internal applications result in increased efficiency and innovation and enable employees to spend their time on higher-level tasks that can positively impact customer experiences. Forward-thinking organizations must ensure user experience (UX) is consistently good across applications, creating a better total experience for customers and employees.

2. If It Isn't Broken, Reuse It

It's expensive and extremely labor intensive to build applications from scratch — essentially, reinventing the wheel each time there's a new business need. Unfortunately, that's the approach many organizations currently use. Collectively, organizations need to work toward building processes once and expanding those processes across applications and workflows.

Having a cohesive design system with the same patterns and components in the same place every time benefits users — both internal and external — with more consistent experiences. By implementing reusable templates every time you create a new application or workflow, you get one step closer to eliminating legacy debt and creating cost-efficient way to create new, user-friendly applications — all with the peace of mind of staying within your organization's guardrails. And when that debt starts disappearing, there's room for even more innovation.

3. You Can't Keep Up, and That's Okay

There will always be a new UX flavor of the week/month/year, and many times, organizations fight low code and instead want to build new applications themselves from the ground up. Unless, as an organization, you're able to assemble a giant team of specialized application developers and UX gurus, it's unrealistic to expect the creation of new apps and frameworks every time there's a new industry trend.

This approach will only result in even more legacy debt than you already have. It's time to instead embrace a standard low-code platform, which enables you to outsource a good portion of this work to the technology itself. You can create consistent and innovative applications without a full, dedicated team of developers, and more easily keep up with new trends and market demands. Without the hard coding that previously stifled the speed of adoption, the future of the UX will be much more flexible.

4. Technology Will Pave the Way to Be More Human-Centric

Work has become more complicated since the pandemic — we're distributed across different environments, and we can't continue to just keep building band-aids and bots for these challenges. We need humans to tackle the harder stuff while offloading the uncomplicated tasks. That's where automation comes in — it enables organizations to empower humans to be more effective and focus on important things, such as UX, designing better processes, and true transformation, without the risks that were previously presented with this innovation.

Additionally, if organizations can use data to help make people's jobs better, employees may lean into their data being used in the workplace. This is where we see technologies like task mining, process mining, and process AI come in to help make decisions based on that data to improve employees' experiences. Just as Amazon uses AI to adapt, seemingly on the fly, to massive occurrences of events occurring across its ecosystem, all enterprises can reap the rewards from these technologies that can see deep across and inside their processes. Brands use AI to see where processes are sub-optimal or to tell employees starting a meaningless task that it's a waste of time. We're entering an era of truly understanding what people do and helping them work better.

Low-code can be an engine of innovation — creating new apps, processes, and experiences — both internally and externally, big or small. It gives more people the ability to try new things faster — as well as fail and move on faster — while broadening the scope of who can participate in the process. Low-code technology will help create even more innovation, deliver powerful re-use, and drive rapid adoption, and we'll start to see more organizations embrace it. By staying focused on the areas where you can provide value to the humans using your technology, you'll see big gains in the ways work gets accomplished and how new phases of innovation are ushered in.

Anthony Abdulla is Senior Director of Product Marketing at Pega

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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 ...

Creating Better Employee Experiences and Opportunities for Innovation

Anthony Abdulla
Pega

The challenges businesses have endured over the past few years have created more pressure than ever to digitally transform in order to keep pace with rapid change as well as market competition. A survey from McKinsey found that as a result of the pandemic, companies have accelerated the digitization of their internal operations by three to four years, and the share of digital or digitally enabled products in their portfolios has accelerated by seven years. Those are hugely expedited timelines, and they're placing added stress on existing processes, technologies, and the people behind making businesses run smoothly every day.

Looking ahead, businesses need to place more emphasis on providing employees with the tools they need to thrive and be more efficient while also giving them the opportunity to spend less time wading through mundane, repetitive tasks that can stifle productivity. This means refocusing transformation efforts on creating better employee experiences, making technology more human-centric, and leaning on low-code tools to usher in the next wave of innovation. And while it's no easy task, here are four areas to keep in mind to help guide those technology decisions:

1. Give Employees the Same Experiences as Customers

Typically, when comparing an organization's internal and external apps, there's a massive disconnect. Internal apps tend to be lesser-than or are an attempted (but ineffective) repeat of external apps. Organizations are spending their resources on customer-facing applications, but not investing in internal apps. This can be detrimental to a brand, as they're creating different and conflicting experience variations — while customers are getting great in-app experiences, employees are wasting time-fighting internal applications and figuring out their functionality. Not to mention, all employees are also customers and know what a consumer-grade experience feels like.

Employee-facing applications should provide the same seamless, efficient experiences as their external ones. Quality internal applications result in increased efficiency and innovation and enable employees to spend their time on higher-level tasks that can positively impact customer experiences. Forward-thinking organizations must ensure user experience (UX) is consistently good across applications, creating a better total experience for customers and employees.

2. If It Isn't Broken, Reuse It

It's expensive and extremely labor intensive to build applications from scratch — essentially, reinventing the wheel each time there's a new business need. Unfortunately, that's the approach many organizations currently use. Collectively, organizations need to work toward building processes once and expanding those processes across applications and workflows.

Having a cohesive design system with the same patterns and components in the same place every time benefits users — both internal and external — with more consistent experiences. By implementing reusable templates every time you create a new application or workflow, you get one step closer to eliminating legacy debt and creating cost-efficient way to create new, user-friendly applications — all with the peace of mind of staying within your organization's guardrails. And when that debt starts disappearing, there's room for even more innovation.

3. You Can't Keep Up, and That's Okay

There will always be a new UX flavor of the week/month/year, and many times, organizations fight low code and instead want to build new applications themselves from the ground up. Unless, as an organization, you're able to assemble a giant team of specialized application developers and UX gurus, it's unrealistic to expect the creation of new apps and frameworks every time there's a new industry trend.

This approach will only result in even more legacy debt than you already have. It's time to instead embrace a standard low-code platform, which enables you to outsource a good portion of this work to the technology itself. You can create consistent and innovative applications without a full, dedicated team of developers, and more easily keep up with new trends and market demands. Without the hard coding that previously stifled the speed of adoption, the future of the UX will be much more flexible.

4. Technology Will Pave the Way to Be More Human-Centric

Work has become more complicated since the pandemic — we're distributed across different environments, and we can't continue to just keep building band-aids and bots for these challenges. We need humans to tackle the harder stuff while offloading the uncomplicated tasks. That's where automation comes in — it enables organizations to empower humans to be more effective and focus on important things, such as UX, designing better processes, and true transformation, without the risks that were previously presented with this innovation.

Additionally, if organizations can use data to help make people's jobs better, employees may lean into their data being used in the workplace. This is where we see technologies like task mining, process mining, and process AI come in to help make decisions based on that data to improve employees' experiences. Just as Amazon uses AI to adapt, seemingly on the fly, to massive occurrences of events occurring across its ecosystem, all enterprises can reap the rewards from these technologies that can see deep across and inside their processes. Brands use AI to see where processes are sub-optimal or to tell employees starting a meaningless task that it's a waste of time. We're entering an era of truly understanding what people do and helping them work better.

Low-code can be an engine of innovation — creating new apps, processes, and experiences — both internally and externally, big or small. It gives more people the ability to try new things faster — as well as fail and move on faster — while broadening the scope of who can participate in the process. Low-code technology will help create even more innovation, deliver powerful re-use, and drive rapid adoption, and we'll start to see more organizations embrace it. By staying focused on the areas where you can provide value to the humans using your technology, you'll see big gains in the ways work gets accomplished and how new phases of innovation are ushered in.

Anthony Abdulla is Senior Director of Product Marketing at Pega

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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 ...