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5 Problems With the IT Industrial Revolution

Matthew Selheimer

Over the last several years there has been lots of talk about the need for an "Industrial Revolution" in IT. We're actually pretty big fans of the metaphor here at ITinvolve.

I think it's well accepted that IT needs to improve both its speed of service delivery and quality. These are classic benefits from any industrialization effort, and they both create ripple-effect benefits in other areas too (e.g. ability to improve customer service, increased competitiveness).

But despite all the talk and recommendations (e.g. adoption automation tools, get on board with DevOps), there are five common problems that stand in the way of the IT industrialization movement. A recent Forrester Consulting study commissioned by Chef gives us some very useful, empirical data to call these problems out for action.

1. First Time Change Success Rates aren't where they need to be

40% of Fortune 1000 IT leaders say they have first time change success rates below 80% or simply don't know, and another 37% say their success rates are somewhere between 80% and 95%. You can't move fast if you aren't able to get it right the first time, because it not only slows you down to troubleshoot and redo, but it hurts your other goal of improving quality.

2. Infrastructure Change Frequency is still far too slow

69% of Fortune 1000 IT leaders say it takes them more than a week to make infrastructure changes. With all the talk and adoption of cloud infrastructure-as-a-service, these numbers are just staggering. Whether you are making infrastructure changes to improve performance, reliability, security, or to support new service deliveries, we have to get these times down to daily or (even better) as needed. There are a lot of improvements to be made here.

3. Application Change Frequency is just as bad

69% of Fortune 1000 IT leaders say it takes them more than a week to release application code into production. Notice that it doesn't say "to develop, test, and release code into production". We're talking about just releasing code that has already been written and tested. 41% say it still takes them more than a month to release code into production. Hard to believe, but the data is clear.

4. IT break things far too often when making changes

46% of Fortunate 1000 leaders reported that more than 10% of their incidents were the results of changes that IT made. Talk about hurting end user satisfaction and their perception of IT quality. What's worse, though, is that 31% said they didn't even know what percentage of their incidents are caused by changes made by IT!

5. The megatrends (virtualization, agile development, cloud, mobile) are intensifying the situation

As the report highlights, these trends "cause complexity to explode in a nonlinear fashion."

So what can you do about this if you believe that "industrialization" and, therefore, automation is the answer (or at least a big part of the answer). Well, first, you have to make sure your automation is intelligent – i.e. informed and accurate. Because we all know that doing the wrong things faster will make things worse faster.

Good automation must be driven by a model that fully comprehends the current state of configuration, the desired state, and the necessary changes and risks to get there. It's only when armed with this information, can automation engineers effectively build out the scripts, run books, etc. to deliver agility with stability and quality.

Matthew Selheimer is VP of Marketing at ITinvolve.

Related Links:

www.itinvolve.com

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5 Problems With the IT Industrial Revolution

Matthew Selheimer

Over the last several years there has been lots of talk about the need for an "Industrial Revolution" in IT. We're actually pretty big fans of the metaphor here at ITinvolve.

I think it's well accepted that IT needs to improve both its speed of service delivery and quality. These are classic benefits from any industrialization effort, and they both create ripple-effect benefits in other areas too (e.g. ability to improve customer service, increased competitiveness).

But despite all the talk and recommendations (e.g. adoption automation tools, get on board with DevOps), there are five common problems that stand in the way of the IT industrialization movement. A recent Forrester Consulting study commissioned by Chef gives us some very useful, empirical data to call these problems out for action.

1. First Time Change Success Rates aren't where they need to be

40% of Fortune 1000 IT leaders say they have first time change success rates below 80% or simply don't know, and another 37% say their success rates are somewhere between 80% and 95%. You can't move fast if you aren't able to get it right the first time, because it not only slows you down to troubleshoot and redo, but it hurts your other goal of improving quality.

2. Infrastructure Change Frequency is still far too slow

69% of Fortune 1000 IT leaders say it takes them more than a week to make infrastructure changes. With all the talk and adoption of cloud infrastructure-as-a-service, these numbers are just staggering. Whether you are making infrastructure changes to improve performance, reliability, security, or to support new service deliveries, we have to get these times down to daily or (even better) as needed. There are a lot of improvements to be made here.

3. Application Change Frequency is just as bad

69% of Fortune 1000 IT leaders say it takes them more than a week to release application code into production. Notice that it doesn't say "to develop, test, and release code into production". We're talking about just releasing code that has already been written and tested. 41% say it still takes them more than a month to release code into production. Hard to believe, but the data is clear.

4. IT break things far too often when making changes

46% of Fortunate 1000 leaders reported that more than 10% of their incidents were the results of changes that IT made. Talk about hurting end user satisfaction and their perception of IT quality. What's worse, though, is that 31% said they didn't even know what percentage of their incidents are caused by changes made by IT!

5. The megatrends (virtualization, agile development, cloud, mobile) are intensifying the situation

As the report highlights, these trends "cause complexity to explode in a nonlinear fashion."

So what can you do about this if you believe that "industrialization" and, therefore, automation is the answer (or at least a big part of the answer). Well, first, you have to make sure your automation is intelligent – i.e. informed and accurate. Because we all know that doing the wrong things faster will make things worse faster.

Good automation must be driven by a model that fully comprehends the current state of configuration, the desired state, and the necessary changes and risks to get there. It's only when armed with this information, can automation engineers effectively build out the scripts, run books, etc. to deliver agility with stability and quality.

Matthew Selheimer is VP of Marketing at ITinvolve.

Related Links:

www.itinvolve.com

Forrester Consulting Study: IT Speed: The Crisis and the Savior of the Enterprise

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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.