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Businesses Face Digital Ceiling in Transformation Progress

Businesses globally face a "digital ceiling" when it comes to digital transformation, according to new research from Infosys Knowledge Institute (IKI), the thought leadership and research arm of Infosys. The study reveals that businesses must change their mindsets to achieve sophisticated levels of digital maturity.

Infosys Digital Radar 2020 assessed the digital transformation efforts of companies on a Digital Maturity Index and found year-over-year progress in basic areas, such as digital initiatives to improve a company's efficiency. However, most companies come up against a "digital ceiling" when trying to achieve the most advanced levels of maturity.

The report, which surveyed over 1,000 executives globally, ranked the most digitally advanced companies as "Visionaries", followed by "Explorers" and then "Watchers."

Companies know how to achieve moderate transformation success, with an 18% increase in companies progressing this year from the lowest tier of Watchers to the middle Explorer tier. However, Explorers struggled to move into the top Visionary cluster, with the top tier remaining the same, indicating a "digital ceiling" to transformation efforts.

The Visionary cluster remains unchanged despite companies reporting fewer barriers to digital transformation than last year. Human, rather than technological, barriers are now the most persistent, with the two of the top hurdles being the lack of talent or skills (34%) and a risk-averse corporate culture (35%).

How to Break Through the Digital Ceiling

The research demonstrates that top performers break through the digital ceiling because they think differently.

Firstly, successful companies focus strongly on people, using digital transformation to make improvements centred on customers and employees.

Most companies (68%) across the spectrum stated operational efficiency and increased productivity as a main transformation objective. But successful companies in the Visionary cluster are particularly motivated to make improvements for their employees. Nearly half of Visionaries describe "empowering employees" as a major business objective for transformation, compared with less than one third of Explorers and less than one fifth of Watchers.

Likewise, Visionaries have an increased focus on customer centred initiatives, being significantly more likely than other clusters to undertake transformation to improve customer experiences and engagement and in order to respond more quickly to customer needs.
Secondly, successful companies have a different mindset when it comes to transformation processes.

Traditional linear transformations result in long transformation timelines, meaning a company's improvements are out of date by the time the process is complete. Instead, top performers demonstrate a cyclical mindset, implementing recurring rapid feedback loops to accelerate transformation and keep updates relevant.

The Visionary cluster is far ahead of others in digital initiatives tied to quick cycles: 75% operate at scale in Agile and DevOps, compared with an overall average of 34% for the entire survey group.

Businesses Overestimate Tech Barriers and Underestimate Importance of Company Mndset

The importance of culture and a cyclical transformation mindset to breaking through the digital ceiling were underestimated by businesses last year.

In the 2019 Digital Radar report, companies were asked to predict the biggest barriers to their transformation progress for the following year. This year's Infosys Digital Radar 2020 compares these predictions to the actual challenges businesses faced in 2019.

Businesses reported dramatic declines in the impact that technological barriers have on their transformation progress, including:

■ Inability to experiment quickly (down 49%)

■ Insufficient budget (down 40%)

■ Cybersecurity challenges (down 40%)

However, businesses made much less progress against cultural barriers, including lack of change management capabilities (down 7%) and lack of talent (down 6%).

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 ...

Businesses Face Digital Ceiling in Transformation Progress

Businesses globally face a "digital ceiling" when it comes to digital transformation, according to new research from Infosys Knowledge Institute (IKI), the thought leadership and research arm of Infosys. The study reveals that businesses must change their mindsets to achieve sophisticated levels of digital maturity.

Infosys Digital Radar 2020 assessed the digital transformation efforts of companies on a Digital Maturity Index and found year-over-year progress in basic areas, such as digital initiatives to improve a company's efficiency. However, most companies come up against a "digital ceiling" when trying to achieve the most advanced levels of maturity.

The report, which surveyed over 1,000 executives globally, ranked the most digitally advanced companies as "Visionaries", followed by "Explorers" and then "Watchers."

Companies know how to achieve moderate transformation success, with an 18% increase in companies progressing this year from the lowest tier of Watchers to the middle Explorer tier. However, Explorers struggled to move into the top Visionary cluster, with the top tier remaining the same, indicating a "digital ceiling" to transformation efforts.

The Visionary cluster remains unchanged despite companies reporting fewer barriers to digital transformation than last year. Human, rather than technological, barriers are now the most persistent, with the two of the top hurdles being the lack of talent or skills (34%) and a risk-averse corporate culture (35%).

How to Break Through the Digital Ceiling

The research demonstrates that top performers break through the digital ceiling because they think differently.

Firstly, successful companies focus strongly on people, using digital transformation to make improvements centred on customers and employees.

Most companies (68%) across the spectrum stated operational efficiency and increased productivity as a main transformation objective. But successful companies in the Visionary cluster are particularly motivated to make improvements for their employees. Nearly half of Visionaries describe "empowering employees" as a major business objective for transformation, compared with less than one third of Explorers and less than one fifth of Watchers.

Likewise, Visionaries have an increased focus on customer centred initiatives, being significantly more likely than other clusters to undertake transformation to improve customer experiences and engagement and in order to respond more quickly to customer needs.
Secondly, successful companies have a different mindset when it comes to transformation processes.

Traditional linear transformations result in long transformation timelines, meaning a company's improvements are out of date by the time the process is complete. Instead, top performers demonstrate a cyclical mindset, implementing recurring rapid feedback loops to accelerate transformation and keep updates relevant.

The Visionary cluster is far ahead of others in digital initiatives tied to quick cycles: 75% operate at scale in Agile and DevOps, compared with an overall average of 34% for the entire survey group.

Businesses Overestimate Tech Barriers and Underestimate Importance of Company Mndset

The importance of culture and a cyclical transformation mindset to breaking through the digital ceiling were underestimated by businesses last year.

In the 2019 Digital Radar report, companies were asked to predict the biggest barriers to their transformation progress for the following year. This year's Infosys Digital Radar 2020 compares these predictions to the actual challenges businesses faced in 2019.

Businesses reported dramatic declines in the impact that technological barriers have on their transformation progress, including:

■ Inability to experiment quickly (down 49%)

■ Insufficient budget (down 40%)

■ Cybersecurity challenges (down 40%)

However, businesses made much less progress against cultural barriers, including lack of change management capabilities (down 7%) and lack of talent (down 6%).

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 ...