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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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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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In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

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 ...