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The Case for Radically New IT Training

Terry Critchley

This blog presents the case for a radical new approach to basic information technology (IT) education. This conclusion is based on a study of courses and other forms of IT education which purport to cover IT "fundamentals." It is based on my own decades of IT experience and dogma-free research into current IT literature and media.

Information technology training occurs in numerous forms from computer science (CS) courses, as taught in schools and universities, to other eclectic ones and PC-oriented versions of the computing world. I maintain that these courses, especially CS ones, do not stack up to the needs of the fluid, modern IT as practiced in the workplace, especially the enterprise. My reasoning is as follows:

1. There is, and has been for over two decades, an IT skills shortage which is at its peak today (any day).

2. Practically the only source of CS skills are the schools and universities and, of the CS graduates, over half do not stay in the IT job they were hired for. CS is grossly understaffed with females and a survey I created for the CAS (computing at school) group unearthed major reasons for this female reluctance as: too geekish and theoretical, boring and needs great maths skills. Neither is true of the IT world I inhabited and which I observe and write about today.

3. CS and other curricula, many of which I have studied, do not match even the keywords which typify modern IT as practiced in the workplace. This can be shown by comparing any existing curriculum with the attached keyword list which typifies modern IT. This list has been verified as representative of modern IT by four of my peers in IT.

4. Fully 70% of IT projects fail in degrees from not quite what I wanted to total disaster. This failure rate applies to the more specific area of digital transformation and legacy modernization. In short, nearly every IT activity.

As a result, most businesses are reliant on computers (digital) in a range of ways from their being necessary for us to function to mission critical. This reliance is badly hampered by the drawbacks in skills available, as discussed above.

A Solution to This Dilemma

There are a few possible solutions:

1. Do nothing and carry on as usual — the it'll be alright on the night solution

2. Soldier on as usual but get more and more people to study CS and undertake other versions of IT training — the bang your head against the wall solution

3. Devise new IT training, along with an IT apprenticeship, which is apposite the current IT demanded in the workplace; make it accessible by means other than expensive 3- or 4-year university courses; make it easily updated as technology changes; and to widen the demography, age-independent, of new IT training entrants. These needs mandate an online course(s).

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In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

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 ...

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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 ...

The Case for Radically New IT Training

Terry Critchley

This blog presents the case for a radical new approach to basic information technology (IT) education. This conclusion is based on a study of courses and other forms of IT education which purport to cover IT "fundamentals." It is based on my own decades of IT experience and dogma-free research into current IT literature and media.

Information technology training occurs in numerous forms from computer science (CS) courses, as taught in schools and universities, to other eclectic ones and PC-oriented versions of the computing world. I maintain that these courses, especially CS ones, do not stack up to the needs of the fluid, modern IT as practiced in the workplace, especially the enterprise. My reasoning is as follows:

1. There is, and has been for over two decades, an IT skills shortage which is at its peak today (any day).

2. Practically the only source of CS skills are the schools and universities and, of the CS graduates, over half do not stay in the IT job they were hired for. CS is grossly understaffed with females and a survey I created for the CAS (computing at school) group unearthed major reasons for this female reluctance as: too geekish and theoretical, boring and needs great maths skills. Neither is true of the IT world I inhabited and which I observe and write about today.

3. CS and other curricula, many of which I have studied, do not match even the keywords which typify modern IT as practiced in the workplace. This can be shown by comparing any existing curriculum with the attached keyword list which typifies modern IT. This list has been verified as representative of modern IT by four of my peers in IT.

4. Fully 70% of IT projects fail in degrees from not quite what I wanted to total disaster. This failure rate applies to the more specific area of digital transformation and legacy modernization. In short, nearly every IT activity.

As a result, most businesses are reliant on computers (digital) in a range of ways from their being necessary for us to function to mission critical. This reliance is badly hampered by the drawbacks in skills available, as discussed above.

A Solution to This Dilemma

There are a few possible solutions:

1. Do nothing and carry on as usual — the it'll be alright on the night solution

2. Soldier on as usual but get more and more people to study CS and undertake other versions of IT training — the bang your head against the wall solution

3. Devise new IT training, along with an IT apprenticeship, which is apposite the current IT demanded in the workplace; make it accessible by means other than expensive 3- or 4-year university courses; make it easily updated as technology changes; and to widen the demography, age-independent, of new IT training entrants. These needs mandate an online course(s).

Hot Topics

The Latest

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

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 ...