The Critical Role of Workload Automation in Achieving Successful Digital Transformation

Dan Twing
EMA

Digital transformation has become a ubiquitous term in today's business landscape, describing the integration of digital technologies and cultural change to achieve organizational objectives. While discussions surrounding digital transformation have been ongoing for over a decade, the progress made across industries and organizations varies. In this blog, we will explore the state of digital transformation and examine the critical role that Workload Automation (WLA) plays in supporting its development and ongoing operations. We will also delve into how organizations are leveraging WLA to drive their digital transformation initiatives.

EMA WEBINAR ON-DEMAND: The Critical Role of Workload Automation in Achieving Successful Digital Transformation

The State of Digital Transformation

Digital transformation is a journey that organizations undertake to embrace the potential of digital technologies. However, the pace of this journey varies across industries and organizations, influenced by factors such as organization size, priorities, technology infrastructure, and talent readiness. Some industries, including e-commerce, digital media, and financial technology, have made significant strides in digital transformation. However, sectors like healthcare and government are still in the early stages of digital adoption.

EMA conducted a global survey of 406 IT and business leaders to understand how workload automation software is changing to adapt to modern IT needs and to support digital transformation. The first chart shows where global enterprise IT and business leaders self-assess the state of digital transformation in their organizations.


The second chart summarizes these results into 5 categories. Most of the enterprise market, 97% of organizations, have initiated digital transformation activities. However, only 21% are considered mature or looking towards the next generation, while the majority (77%) are still in the early stages or just beginning their transformation journey. These findings highlight the wide range of progress and the ongoing nature of digital transformation.


The Role of Workload Automation in Digital Transformation

Workload Automation (WLA) tools play a critical role in supporting the development and ongoing operations of digitally transformed processes. During the development phase, WLA tools streamline and automate various tasks in the software development life cycle, such as code builds, testing, and deployment. By automating these tasks, WLA accelerates development cycles, reduces errors, and enhances code quality.

The survey conducted by EMA sheds light on how organizations are leveraging WLA to support their digital transformation initiatives. The most mentioned use case, as highlighted by 51% of respondents, is the support of DevOps processes. DevOps emphasizes collaboration and integration between development and operations teams, and WLA tools facilitate the automation and orchestration of tasks involved in this process.


Another significant application of WLA in digital transformation is the configuration of cloud and on-premises infrastructure, mentioned by a considerable percentage of respondents. By automating infrastructure provisioning and management, organizations can scale their digital capabilities effectively.

Additionally, WLA plays a crucial role in automating release management, ensuring efficient and error-free deployment of digital solutions. This aspect was highlighted by 28% of respondents in the survey.

Furthermore, organizations are leveraging WLA to orchestrate automation in support of digital transformation. This includes connecting and orchestrating disparate digital transformation capabilities, as mentioned by 33% of respondents. Another 27% use WLA for end-to-end orchestration of digital transformation processes. EMA believes that if developers have a better understanding of WLA orchestration, new applications and digital processes could be completed with reduced developer time.

WLA is a critical component of digital transformation. It not only plays a vital role during the development phase, streamlining and automating various tasks, but also continues to support ongoing operations by automating IT operations tasks, ensuring operational efficiency and reliability. WLA enables scalability and flexibility, allowing digital processes to handle varying workload demands without compromising performance. By leveraging WLA, organizations can accelerate development cycles, improve operational efficiency, and deliver innovative digital processes to customers.

Where to Learn More

If you would like to learn more about the role of WLA, watch this on-demand webinar, The Critical Role of Workload Automation in Achieving Successful Digital Transformation. You can see how those further along the digital transformation journey recognize the need to centrally manage automation, and how organizations with developers who understand and value the role of WLA tend to be farther down the digital transformation path.

Use the player or download the MP3 below to listen to EMA-APMdigest Podcast Episode 1 — Dan Twing, President and COO of EMA, discusses Observability and Automation.

Click here for a direct MP3 download of EMA-APMdigest Podcast Episode 1

Dan Twing is President and COO of Enterprise Management Associates (EMA)

Related Links

Hot Topics

The Latest

The $600 Billion Wake Up Call

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Breaking Down Agentic AI Fragmentation and Complexity with Governance

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud Spend Is Rising and Cloud Optimization Is Key to Funding AI and Protecting Margins

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

Building AI Agents That Build Trust

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

Keys to Building a Partner Ecosystem That Scales

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

AI Deepfakes: Rethinking Trust in the Workplace

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Why ITOps Need Right-Sized AI, Not Bigger Models

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

The End of Reactive DevOps: AI-Driven Observability for Zero-Defect Digital Systems

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

The Critical Role of Workload Automation in Achieving Successful Digital Transformation

Dan Twing
EMA

Digital transformation has become a ubiquitous term in today's business landscape, describing the integration of digital technologies and cultural change to achieve organizational objectives. While discussions surrounding digital transformation have been ongoing for over a decade, the progress made across industries and organizations varies. In this blog, we will explore the state of digital transformation and examine the critical role that Workload Automation (WLA) plays in supporting its development and ongoing operations. We will also delve into how organizations are leveraging WLA to drive their digital transformation initiatives.

EMA WEBINAR ON-DEMAND: The Critical Role of Workload Automation in Achieving Successful Digital Transformation

The State of Digital Transformation

Digital transformation is a journey that organizations undertake to embrace the potential of digital technologies. However, the pace of this journey varies across industries and organizations, influenced by factors such as organization size, priorities, technology infrastructure, and talent readiness. Some industries, including e-commerce, digital media, and financial technology, have made significant strides in digital transformation. However, sectors like healthcare and government are still in the early stages of digital adoption.

EMA conducted a global survey of 406 IT and business leaders to understand how workload automation software is changing to adapt to modern IT needs and to support digital transformation. The first chart shows where global enterprise IT and business leaders self-assess the state of digital transformation in their organizations.


The second chart summarizes these results into 5 categories. Most of the enterprise market, 97% of organizations, have initiated digital transformation activities. However, only 21% are considered mature or looking towards the next generation, while the majority (77%) are still in the early stages or just beginning their transformation journey. These findings highlight the wide range of progress and the ongoing nature of digital transformation.


The Role of Workload Automation in Digital Transformation

Workload Automation (WLA) tools play a critical role in supporting the development and ongoing operations of digitally transformed processes. During the development phase, WLA tools streamline and automate various tasks in the software development life cycle, such as code builds, testing, and deployment. By automating these tasks, WLA accelerates development cycles, reduces errors, and enhances code quality.

The survey conducted by EMA sheds light on how organizations are leveraging WLA to support their digital transformation initiatives. The most mentioned use case, as highlighted by 51% of respondents, is the support of DevOps processes. DevOps emphasizes collaboration and integration between development and operations teams, and WLA tools facilitate the automation and orchestration of tasks involved in this process.


Another significant application of WLA in digital transformation is the configuration of cloud and on-premises infrastructure, mentioned by a considerable percentage of respondents. By automating infrastructure provisioning and management, organizations can scale their digital capabilities effectively.

Additionally, WLA plays a crucial role in automating release management, ensuring efficient and error-free deployment of digital solutions. This aspect was highlighted by 28% of respondents in the survey.

Furthermore, organizations are leveraging WLA to orchestrate automation in support of digital transformation. This includes connecting and orchestrating disparate digital transformation capabilities, as mentioned by 33% of respondents. Another 27% use WLA for end-to-end orchestration of digital transformation processes. EMA believes that if developers have a better understanding of WLA orchestration, new applications and digital processes could be completed with reduced developer time.

WLA is a critical component of digital transformation. It not only plays a vital role during the development phase, streamlining and automating various tasks, but also continues to support ongoing operations by automating IT operations tasks, ensuring operational efficiency and reliability. WLA enables scalability and flexibility, allowing digital processes to handle varying workload demands without compromising performance. By leveraging WLA, organizations can accelerate development cycles, improve operational efficiency, and deliver innovative digital processes to customers.

Where to Learn More

If you would like to learn more about the role of WLA, watch this on-demand webinar, The Critical Role of Workload Automation in Achieving Successful Digital Transformation. You can see how those further along the digital transformation journey recognize the need to centrally manage automation, and how organizations with developers who understand and value the role of WLA tend to be farther down the digital transformation path.

Use the player or download the MP3 below to listen to EMA-APMdigest Podcast Episode 1 — Dan Twing, President and COO of EMA, discusses Observability and Automation.

Click here for a direct MP3 download of EMA-APMdigest Podcast Episode 1

Dan Twing is President and COO of Enterprise Management Associates (EMA)

Related Links

Hot Topics

The Latest

The $600 Billion Wake Up Call

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Breaking Down Agentic AI Fragmentation and Complexity with Governance

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud Spend Is Rising and Cloud Optimization Is Key to Funding AI and Protecting Margins

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

Building AI Agents That Build Trust

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

Keys to Building a Partner Ecosystem That Scales

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

AI Deepfakes: Rethinking Trust in the Workplace

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Why ITOps Need Right-Sized AI, Not Bigger Models

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

The End of Reactive DevOps: AI-Driven Observability for Zero-Defect Digital Systems

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...