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The Threat Behind Digital Transformation

Jonah Kowall

The age of digital disruption is upon us, as the last decade alone has proven in terms of technology and disruption with the progression of organizations like Uber, Airbnb, and Netflix. We are also on the brink of new disruptions in industry, including banking or payments, insurance, healthcare, construction, packaging, and many more. These not only come from startups disrupting a sector, but companies being able to shift from their existing focus areas to build on new opportunities. They’re driven internally or by creating a new spinoff company into additional areas.

The CxO organization is becoming more concerned with outsiders entering into their markets. In the past, the cost of entry into a new market was significantly higher than it is today. Digital businesses and software-driven business models are changing the playing field, for new entrants to shift into new markets. The cost of experimentation continues to decrease, with lower cost computing models that allow for the rental of resources and software.

Open source plays a key role in both building new applications and creating community leverage; and senior executives are taking notice. IBM’s Global C-Suite Study is a great data set consisting of data collected between January and June 2015. They surveyed 5,247 business leaders from 21 industries in more than 70 countries. The sample comprises 818 CEOs, 643 CFOs, 601 CHROs, 1,805 CIOs, 723 CMOs, and 657 COOs :


Today’s native digital generations prefer to work on digital channels versus in-person channels. This ongoing trend has given rise to improvements in customer service, where interactions are delivered across multiple digital channels, ranging from social channels like Twitter and Facebook to text and voice communications. However, there is still more work to be done to unify these platforms more seamlessly. Technologies such as social, chat, and more recently, bots create the personal touch in a more scalable manner, reducing costs and increasing customer satisfaction. These trends will continually take hold, personalization and fast touch points are valued by today’s users who seemingly have less time than ever before.

The level of patience and complexity involved in making these channels seamless is an increasing challenge with today’s IT complexity. Your customers will not tolerate failure, and expect technology to just work. IBM’s survey data confirms this trend.


This accelerating trend is what will differentiate those businesses who learn to engage in new and differentiated ways across multiple channels. Companies which lead, versus those that follow have very different perspectives on what will likely transpire during a time of disruption.

In Figure 8 below, those indicated as torchbearers see companies who look for another market to expand into being able to enter these markets quickly as innovators in another segment or market. Similarly, these first-mover companies see the need to enter into new or adjacent markets. For this reason you see most companies creating labs or innovation centers. We increasingly see next generation visibility being required for these new and highly agile software systems. This demand is critical for survival, innovation, and growth.

These experiments can only be done promptly when the organization adopts smaller agile teams across the business and technology groups. Breaking up large, and likely slow moving monolithic organizations, software, and systems into smaller units which operate independently. The ability to experimentation and make decisions on their own.

The companies who lead tend to do this far more frequently than those who follow or are laggards. We see this regularly in our customer base, where a large degree of diversity exists in the autonomy within each team. The question remains as to how this will play out with economic changes or political change.


In summary, while this data and analysis confirm many trends, it shows clearly different and increasingly changed thinking as digital becomes the preferred channel for many businesses. The IBM data also informs that decentralized decision making and experimentation are clearly taking hold, but those who lead are in a different place than those who follow. It will be interesting to see how this progresses with IBMs new survey data.

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 ...

The Threat Behind Digital Transformation

Jonah Kowall

The age of digital disruption is upon us, as the last decade alone has proven in terms of technology and disruption with the progression of organizations like Uber, Airbnb, and Netflix. We are also on the brink of new disruptions in industry, including banking or payments, insurance, healthcare, construction, packaging, and many more. These not only come from startups disrupting a sector, but companies being able to shift from their existing focus areas to build on new opportunities. They’re driven internally or by creating a new spinoff company into additional areas.

The CxO organization is becoming more concerned with outsiders entering into their markets. In the past, the cost of entry into a new market was significantly higher than it is today. Digital businesses and software-driven business models are changing the playing field, for new entrants to shift into new markets. The cost of experimentation continues to decrease, with lower cost computing models that allow for the rental of resources and software.

Open source plays a key role in both building new applications and creating community leverage; and senior executives are taking notice. IBM’s Global C-Suite Study is a great data set consisting of data collected between January and June 2015. They surveyed 5,247 business leaders from 21 industries in more than 70 countries. The sample comprises 818 CEOs, 643 CFOs, 601 CHROs, 1,805 CIOs, 723 CMOs, and 657 COOs :


Today’s native digital generations prefer to work on digital channels versus in-person channels. This ongoing trend has given rise to improvements in customer service, where interactions are delivered across multiple digital channels, ranging from social channels like Twitter and Facebook to text and voice communications. However, there is still more work to be done to unify these platforms more seamlessly. Technologies such as social, chat, and more recently, bots create the personal touch in a more scalable manner, reducing costs and increasing customer satisfaction. These trends will continually take hold, personalization and fast touch points are valued by today’s users who seemingly have less time than ever before.

The level of patience and complexity involved in making these channels seamless is an increasing challenge with today’s IT complexity. Your customers will not tolerate failure, and expect technology to just work. IBM’s survey data confirms this trend.


This accelerating trend is what will differentiate those businesses who learn to engage in new and differentiated ways across multiple channels. Companies which lead, versus those that follow have very different perspectives on what will likely transpire during a time of disruption.

In Figure 8 below, those indicated as torchbearers see companies who look for another market to expand into being able to enter these markets quickly as innovators in another segment or market. Similarly, these first-mover companies see the need to enter into new or adjacent markets. For this reason you see most companies creating labs or innovation centers. We increasingly see next generation visibility being required for these new and highly agile software systems. This demand is critical for survival, innovation, and growth.

These experiments can only be done promptly when the organization adopts smaller agile teams across the business and technology groups. Breaking up large, and likely slow moving monolithic organizations, software, and systems into smaller units which operate independently. The ability to experimentation and make decisions on their own.

The companies who lead tend to do this far more frequently than those who follow or are laggards. We see this regularly in our customer base, where a large degree of diversity exists in the autonomy within each team. The question remains as to how this will play out with economic changes or political change.


In summary, while this data and analysis confirm many trends, it shows clearly different and increasingly changed thinking as digital becomes the preferred channel for many businesses. The IBM data also informs that decentralized decision making and experimentation are clearly taking hold, but those who lead are in a different place than those who follow. It will be interesting to see how this progresses with IBMs new survey data.

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 ...