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Maximizing Your Automation Investments: 3 Steps IT Leaders Should Take

Daniel Meyer
Camunda

With an accelerated push toward digital transformation, organizations everywhere are trying to find ways to work smarter, not harder. A key component of this new model is finding ways to automate business processes — freeing up employees to focus on more strategic, valuable work and improving customers' experiences. Today's enterprise IT leaders have many options to help drive automation initiatives — from digital process automation and artificial intelligence (AI) to enterprise content management and robotic process automation (RPA).

Armed with these tools, organizations can simply replace the hand weavers with automated looms … right? Not so fast; automation done well requires a strategic vision and holistic approach.

After all, modernizing your technology is not the same thing as modernizing your organization. The variety of automation technologies available today have served to obscure this basic principle, and often, IT leaders rush to implement technology without fully understanding the impact it will have on their organization. Despite good intent, automating business processes quickly and without a strategic vision will not yield the desired long-term success or transformation.

This is why it's important to focus on the difficult work of organizational transformation. However, this can be particularly challenging, simply because people are both risk averse and resistant to change. The way things have "always been done" becomes the default position, and no one wants to take the risk of doing things differently. Only by breaking this down into incremental steps can organizations hope to succeed with their business transformations. And while technology isn't a substitute for organizational change, automation can help drive such change — as long as it is implemented properly.

As IT leaders navigate their digital transformation journey, they should focus on maximizing their investments by resisting the urge to immediately deploy automation across their entire organizations' architecture. Instead, start small.

1. Generate a bigger impact long term with software development

The worldwide market for technology that enables hyper automation will reach $596.6 billion in 2022, according to the latest forecast from Gartner. This is up from $481.6 billion in 2020 and a projected $532.4 billion this year. This is largely due to business and IT leaders looking to further build their digital footprints as competition drives all to increase efficiency, efficacy and business agility.

The biggest mistake companies can make when they move to more automation is not investing in software development, and especially in failing to bring development skills in-house. To successfully implement automation, they must establish technology layers that can orchestrate all the different elements of a business process — be it humans, RPA bots, API endpoints, AI-powered decision making and more. Orchestration across multiple endpoints is imperative for successful hyper automation.

2. Prioritize hands-on experience with a single project first

While most IT leaders will look to define the "picture perfect" architecture right from the start, the truth is, the most effective approach when integrating process automation is to focus on smaller projects first. Gaining hands-on experience with a single project offers a buffer for IT teams to learn the ins and outs of a new system. In a more digestible setting, you can identify the return on investment from leveraging automation before you roll this project out across your entire organization.

What may seem like a tedious step at first will ultimately lead to significant time and cost savings. By giving your developer team this grace period to immerse themselves in the software, you've set them up for success. As a result, they now have the opportunity to harness the skills required to implement, monitor and fix tasks across the entire organization.

3. Don't try to future-proof your entire architecture right out of the gate

Processes are the algorithms that define the inner workings of any organization, and successful businesses are masters in optimizing these algorithms. They achieve this by automating business processes that are designed precisely to their needs, by improving continuously as they gain more insights over time and by relentlessly leaving no part of a process across people, systems, or devices behind. By starting small, you provide a confined space where developers can make errors and adapt so that when it's time for broader implementation, these errors aren't repeated.

With this approach, organizations can focus on mastering their new system, and avoid complications they otherwise would have missed had they rushed to deploy automation too quickly and scaled it too widely. For process automation to truly transform a business, IT leaders will need to reinvent their process automation initiatives with more open, agile and scalable approaches — with the potential to automate any process, anywhere.

Daniel Meyer is CTO of Camunda

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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 ...

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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 ...

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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 ...

Maximizing Your Automation Investments: 3 Steps IT Leaders Should Take

Daniel Meyer
Camunda

With an accelerated push toward digital transformation, organizations everywhere are trying to find ways to work smarter, not harder. A key component of this new model is finding ways to automate business processes — freeing up employees to focus on more strategic, valuable work and improving customers' experiences. Today's enterprise IT leaders have many options to help drive automation initiatives — from digital process automation and artificial intelligence (AI) to enterprise content management and robotic process automation (RPA).

Armed with these tools, organizations can simply replace the hand weavers with automated looms … right? Not so fast; automation done well requires a strategic vision and holistic approach.

After all, modernizing your technology is not the same thing as modernizing your organization. The variety of automation technologies available today have served to obscure this basic principle, and often, IT leaders rush to implement technology without fully understanding the impact it will have on their organization. Despite good intent, automating business processes quickly and without a strategic vision will not yield the desired long-term success or transformation.

This is why it's important to focus on the difficult work of organizational transformation. However, this can be particularly challenging, simply because people are both risk averse and resistant to change. The way things have "always been done" becomes the default position, and no one wants to take the risk of doing things differently. Only by breaking this down into incremental steps can organizations hope to succeed with their business transformations. And while technology isn't a substitute for organizational change, automation can help drive such change — as long as it is implemented properly.

As IT leaders navigate their digital transformation journey, they should focus on maximizing their investments by resisting the urge to immediately deploy automation across their entire organizations' architecture. Instead, start small.

1. Generate a bigger impact long term with software development

The worldwide market for technology that enables hyper automation will reach $596.6 billion in 2022, according to the latest forecast from Gartner. This is up from $481.6 billion in 2020 and a projected $532.4 billion this year. This is largely due to business and IT leaders looking to further build their digital footprints as competition drives all to increase efficiency, efficacy and business agility.

The biggest mistake companies can make when they move to more automation is not investing in software development, and especially in failing to bring development skills in-house. To successfully implement automation, they must establish technology layers that can orchestrate all the different elements of a business process — be it humans, RPA bots, API endpoints, AI-powered decision making and more. Orchestration across multiple endpoints is imperative for successful hyper automation.

2. Prioritize hands-on experience with a single project first

While most IT leaders will look to define the "picture perfect" architecture right from the start, the truth is, the most effective approach when integrating process automation is to focus on smaller projects first. Gaining hands-on experience with a single project offers a buffer for IT teams to learn the ins and outs of a new system. In a more digestible setting, you can identify the return on investment from leveraging automation before you roll this project out across your entire organization.

What may seem like a tedious step at first will ultimately lead to significant time and cost savings. By giving your developer team this grace period to immerse themselves in the software, you've set them up for success. As a result, they now have the opportunity to harness the skills required to implement, monitor and fix tasks across the entire organization.

3. Don't try to future-proof your entire architecture right out of the gate

Processes are the algorithms that define the inner workings of any organization, and successful businesses are masters in optimizing these algorithms. They achieve this by automating business processes that are designed precisely to their needs, by improving continuously as they gain more insights over time and by relentlessly leaving no part of a process across people, systems, or devices behind. By starting small, you provide a confined space where developers can make errors and adapt so that when it's time for broader implementation, these errors aren't repeated.

With this approach, organizations can focus on mastering their new system, and avoid complications they otherwise would have missed had they rushed to deploy automation too quickly and scaled it too widely. For process automation to truly transform a business, IT leaders will need to reinvent their process automation initiatives with more open, agile and scalable approaches — with the potential to automate any process, anywhere.

Daniel Meyer is CTO of Camunda

Hot Topics

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 ...