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5 Factors Defining Digital Transformation Progress

Malcolm Ross

The widespread move toward digital transformation gained momentum in 2017. Throughout the year, businesses across a wide range of sectors began exploring how they could leverage modern technologies in concert with one another to drive operational excellence and improve customer experiences.

Looking ahead to the rest of 2018 and beyond, it seems like many of the trends that shaped 2017 are set to continue, with the key difference being in how they evolve and shift as they become mainstream.

Five key factors defining the progression of the digital transformation movement are:

1. A Move to Transformation Platforms

The technologies surrounding the move to digital have shifted in recent years, with leading solution providers beginning to create platforms specifically aimed at fostering digital transformation.

CIO Magazine reported that these types of technologies will gain more prominence in the coming year. Businesses will look for help as they dig deep into transformation, and many will want proven solutions that can drive gains. As organizations make such moves, they don't need basic out-of-the-platform functionality.

Instead, platforms must be:

■ Agile enough to adjust to changing demands.

■ Adaptable enough to orient themselves to specific industry requirements.

■ Flexible enough to work within existing IT frameworks.

Mature cloud platforms can meet these kinds of demands, and such solutions are set to usher in the next era of digital transformation.

2. APIs Will Focus On Platforms

With platforms becoming more prominent alongside digital transformation's rise, many organizations are finding that rolling out APIs for each app is not nearly as valuable as simply creating APIs that empower apps to integrate with platforms, a Gartner report explained.

David Cappuccio, VP and Distinguished Analyst at Gartner, explained that this API shift is changing how organizations manage projects.

"Ensure that your organization takes an 'API first' approach, designing APIs based on the requirements of your organization's ecosystem," said Cappuccio. "APIs designed in this way can be mapped to internal technology infrastructure. This approach is more effective than simply generating APIs based on existing infrastructure and data models."

3. Customer Journeys are Shifting

The importance of optimizing customer journeys was among the driving factors behind the move to transform around digital. Businesses realized consumers used such a varied range of devices and services to interact with them that they needed to rethink customer services. Instead of simply equipping a support team with software, companies tried to understand how customers interact with them, what problems come up along the way and how processes and data can be coordinated more effectively.

However, these early moves to focus on customer journeys tended to emphasize getting a consumer from an initial contact to a specific destination. The journey needed to be good, but the emphasis was still to get customers to a certain destination as efficiently as possible.

CIO reported that companies will need to start moving to build digital transformation into the core of their operations. Because of this, many companies will end up completely revisiting their operational and technology ecosystems so they are inherently agile. With greater flexibility in place, companies are focusing less on providing actual products and more on the skills and functionality that comes with creating a strong customer journey.

The old idea that life is about journeys, not destinations, is hitting the business world. Instead of creating customer journeys that lead users to a destination, companies are using the journeys to drive the business.

Digitally transformed interactions with clients can:

■ Create loyalty by making it easy to interact with a brand.

■ Establish trust through personal connections.

■ Generate natural opportunities for upsells and similar revenue gains through relationship building.

Turning the customer journey into a product requires a tremendous degree of organizational cohesion, and the platform systems mentioned earlier play a key role in connecting operations across disparate user groups.

4. The Divide Between IT and Business is Disappearing

The early days of digital transformation were marked by a sense of misalignment between technology teams and business units. Application development platforms, which often serve as central digital transformation hubs, are blurring the lines between these groups, creating an operational climate where IT and business increasingly work as one.

This isn't to say that specific, refined business and technology skillsets will stop being valuable. Instead, the issue is that so many organizational silos are breaking down that companies can no longer afford to segregate these user groups and assume they will still work well together. In action, business teams aren't depending on just technology; they are often driving IT change and taking control of development with low-code platforms. At the same time, tech users aren't doing just backend work; they're recognizing how emerging solutions can drive valuable business gains and are turning into consultants.

As digital transformation matures, success hinges on breaking down the barriers between IT and business units.

5. Emerging Technologies Taking Hold

Artificial intelligence, robotic process automation and related technologies began to gain ground in 2018 as the internet of things exploded and businesses began to embrace the benefits that intelligent machines can offer. As digital transformation strategies mature, modern business will likely be defined by these types of solutions.

AI, for example, is increasingly being embedded into cloud solutions so it can operate in the backend of platforms. It is used for chatbots and personal assistants that make complex tasks easier. In short, it lets people interact with technology like they would another person.

RPA is similarly important, though it is often instrumental in the backend, completing the complex tasks that no longer require human intervention.

These technologies emphasize an underlying trend in digital transformation: It isn't just about bringing people and technologies together; it's about empowering the best parts of human creativity and relationship building by eliminating mundane tasks that have long held workers back.

Digital transformation is evolving. Is your business ready?

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 ...

5 Factors Defining Digital Transformation Progress

Malcolm Ross

The widespread move toward digital transformation gained momentum in 2017. Throughout the year, businesses across a wide range of sectors began exploring how they could leverage modern technologies in concert with one another to drive operational excellence and improve customer experiences.

Looking ahead to the rest of 2018 and beyond, it seems like many of the trends that shaped 2017 are set to continue, with the key difference being in how they evolve and shift as they become mainstream.

Five key factors defining the progression of the digital transformation movement are:

1. A Move to Transformation Platforms

The technologies surrounding the move to digital have shifted in recent years, with leading solution providers beginning to create platforms specifically aimed at fostering digital transformation.

CIO Magazine reported that these types of technologies will gain more prominence in the coming year. Businesses will look for help as they dig deep into transformation, and many will want proven solutions that can drive gains. As organizations make such moves, they don't need basic out-of-the-platform functionality.

Instead, platforms must be:

■ Agile enough to adjust to changing demands.

■ Adaptable enough to orient themselves to specific industry requirements.

■ Flexible enough to work within existing IT frameworks.

Mature cloud platforms can meet these kinds of demands, and such solutions are set to usher in the next era of digital transformation.

2. APIs Will Focus On Platforms

With platforms becoming more prominent alongside digital transformation's rise, many organizations are finding that rolling out APIs for each app is not nearly as valuable as simply creating APIs that empower apps to integrate with platforms, a Gartner report explained.

David Cappuccio, VP and Distinguished Analyst at Gartner, explained that this API shift is changing how organizations manage projects.

"Ensure that your organization takes an 'API first' approach, designing APIs based on the requirements of your organization's ecosystem," said Cappuccio. "APIs designed in this way can be mapped to internal technology infrastructure. This approach is more effective than simply generating APIs based on existing infrastructure and data models."

3. Customer Journeys are Shifting

The importance of optimizing customer journeys was among the driving factors behind the move to transform around digital. Businesses realized consumers used such a varied range of devices and services to interact with them that they needed to rethink customer services. Instead of simply equipping a support team with software, companies tried to understand how customers interact with them, what problems come up along the way and how processes and data can be coordinated more effectively.

However, these early moves to focus on customer journeys tended to emphasize getting a consumer from an initial contact to a specific destination. The journey needed to be good, but the emphasis was still to get customers to a certain destination as efficiently as possible.

CIO reported that companies will need to start moving to build digital transformation into the core of their operations. Because of this, many companies will end up completely revisiting their operational and technology ecosystems so they are inherently agile. With greater flexibility in place, companies are focusing less on providing actual products and more on the skills and functionality that comes with creating a strong customer journey.

The old idea that life is about journeys, not destinations, is hitting the business world. Instead of creating customer journeys that lead users to a destination, companies are using the journeys to drive the business.

Digitally transformed interactions with clients can:

■ Create loyalty by making it easy to interact with a brand.

■ Establish trust through personal connections.

■ Generate natural opportunities for upsells and similar revenue gains through relationship building.

Turning the customer journey into a product requires a tremendous degree of organizational cohesion, and the platform systems mentioned earlier play a key role in connecting operations across disparate user groups.

4. The Divide Between IT and Business is Disappearing

The early days of digital transformation were marked by a sense of misalignment between technology teams and business units. Application development platforms, which often serve as central digital transformation hubs, are blurring the lines between these groups, creating an operational climate where IT and business increasingly work as one.

This isn't to say that specific, refined business and technology skillsets will stop being valuable. Instead, the issue is that so many organizational silos are breaking down that companies can no longer afford to segregate these user groups and assume they will still work well together. In action, business teams aren't depending on just technology; they are often driving IT change and taking control of development with low-code platforms. At the same time, tech users aren't doing just backend work; they're recognizing how emerging solutions can drive valuable business gains and are turning into consultants.

As digital transformation matures, success hinges on breaking down the barriers between IT and business units.

5. Emerging Technologies Taking Hold

Artificial intelligence, robotic process automation and related technologies began to gain ground in 2018 as the internet of things exploded and businesses began to embrace the benefits that intelligent machines can offer. As digital transformation strategies mature, modern business will likely be defined by these types of solutions.

AI, for example, is increasingly being embedded into cloud solutions so it can operate in the backend of platforms. It is used for chatbots and personal assistants that make complex tasks easier. In short, it lets people interact with technology like they would another person.

RPA is similarly important, though it is often instrumental in the backend, completing the complex tasks that no longer require human intervention.

These technologies emphasize an underlying trend in digital transformation: It isn't just about bringing people and technologies together; it's about empowering the best parts of human creativity and relationship building by eliminating mundane tasks that have long held workers back.

Digital transformation is evolving. Is your business ready?

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 ...