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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

Hot Topics

The Latest

In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...