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Gartner: 6 Barriers to Becoming a Digital Business

As organizations continue to embrace digital transformation, they are finding that digital business is not as simple as buying the latest technology — it requires significant changes to culture and systems. A recent Gartner, Inc. survey found that only a small number of organizations have been able to successfully scale their digital initiatives beyond the experimentation and piloting stages.

"The reality is that digital business demands different skills, working practices, organizational models and even cultures," said Marcus Blosch, Research VP at Gartner. "To change an organization designed for a structured, ordered, process-oriented world to one designed for ecosystems, adaptation, learning and experimentation is hard. Some organizations will navigate that change, and others that can't change will become outdated and be replaced."

Gartner has identified six barriers that CIOs must overcome to transform their organization into a digital business:

Barrier No. 1: A Change-Resisting Culture

Digital innovation can be successful only in a culture of collaboration. People have to be able to work across boundaries and explore new ideas. In reality, most organizations are stuck in a culture of change-resistant silos and hierarchies.

"Culture is organizational 'dark matter' — you can't see it, but its effects are obvious," said Blosch. "The challenge is that many organizations have developed a culture of hierarchy and clear boundaries between areas of responsibilities. Digital innovation requires the opposite: collaborative cross-functional and self-directed teams that are not afraid of uncertain outcomes."

CIOs aiming to establish a digital culture should start small: Define a digital mindset, assemble a digital innovation team, and shield it from the rest of the organization to let the new culture develop. Connections between the digital innovation and core teams can then be used to scale new ideas and spread the culture.

Barrier No. 2: Limited Sharing and Collaboration

The lack of willingness to share and collaborate is a challenge not only at the ecosystem level but also inside the organization. Issues of ownership and control of processes, information and systems make people reluctant to share their knowledge. Digital innovation with its collaborative cross-functional teams is often very different from what employees are used to with regards to functions and hierarchies — resistance is inevitable.

"It's not necessary to have everyone on board in the early stages. Try to find areas where interests overlap, and create a starting point. Build a first version, test the idea and use the success story to gain the momentum needed for the next step," said Blosch.

Barrier No. 3: The Business Isn't Ready

Many business leaders are caught up in the hype around digital business. But when the CIO or CDO wants to start the transformation process, it turns out that the business doesn't have the skills or resources needed.

"CIOs should address the digital readiness of the organization to get an understanding of both business and IT readiness," Blosch advised. "Then, focus on the early adopters with the willingness and openness to change and leverage digital. But keep in mind that digital may just not be relevant to certain parts of the organization."

Barrier No. 4: The Talent Gap

Most organizations follow a traditional pattern — organized into functions such as IT, sales and supply chain and largely focused on operations. Change can be slow in this kind of environment.

Digital innovation requires an organization to adopt a different approach. People, processes and technology blend to create new business models and services. Employees need new skills focused on innovation, change and creativity along with the new technologies themselves, such as artificial intelligence (AI) and the Internet of Things (IoT).

"There are two approaches to breach the talent gap — upskill and bimodal," said Blosch. "In smaller or more innovative organizations, it is possible to redefine individuals' roles to include more skills and competencies needed to support digital. In other organizations, using a bimodal approach makes sense by creating a separate group to handle innovation with the requisite skill set."

Barrier No. 5: The Current Practices Don't Support the Talent

Having the right talent is essential, and having the right practices lets the talent work effectively. Highly structured and slow traditional processes don't work for digital. There are no tried and tested models to implement, but every organization has to find the practices that suits it best.

"Some organizations may shift to a product management-based approach for digital innovations because it allows for multiple iterations. Operational innovations can follow the usual approaches until the digital team is skilled and experienced enough to extend its reach and share the learned practices with the organization," Blosch explained.

Barrier No. 6: Change Isn't Easy

It's often technically challenging and expensive to make digital work. Developing platforms, changing the organizational structure, creating an ecosystem of partners — all of this costs time, resources and money.

Over the long term, enterprises should build the organizational capabilities that make change simpler and faster. To do that, they should develop a platform-based strategy that supports continuous change and design principles and then innovate on top of that platform, allowing new services to draw from the platform and its core services.

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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 ... 

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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 ...

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 ...

Gartner: 6 Barriers to Becoming a Digital Business

As organizations continue to embrace digital transformation, they are finding that digital business is not as simple as buying the latest technology — it requires significant changes to culture and systems. A recent Gartner, Inc. survey found that only a small number of organizations have been able to successfully scale their digital initiatives beyond the experimentation and piloting stages.

"The reality is that digital business demands different skills, working practices, organizational models and even cultures," said Marcus Blosch, Research VP at Gartner. "To change an organization designed for a structured, ordered, process-oriented world to one designed for ecosystems, adaptation, learning and experimentation is hard. Some organizations will navigate that change, and others that can't change will become outdated and be replaced."

Gartner has identified six barriers that CIOs must overcome to transform their organization into a digital business:

Barrier No. 1: A Change-Resisting Culture

Digital innovation can be successful only in a culture of collaboration. People have to be able to work across boundaries and explore new ideas. In reality, most organizations are stuck in a culture of change-resistant silos and hierarchies.

"Culture is organizational 'dark matter' — you can't see it, but its effects are obvious," said Blosch. "The challenge is that many organizations have developed a culture of hierarchy and clear boundaries between areas of responsibilities. Digital innovation requires the opposite: collaborative cross-functional and self-directed teams that are not afraid of uncertain outcomes."

CIOs aiming to establish a digital culture should start small: Define a digital mindset, assemble a digital innovation team, and shield it from the rest of the organization to let the new culture develop. Connections between the digital innovation and core teams can then be used to scale new ideas and spread the culture.

Barrier No. 2: Limited Sharing and Collaboration

The lack of willingness to share and collaborate is a challenge not only at the ecosystem level but also inside the organization. Issues of ownership and control of processes, information and systems make people reluctant to share their knowledge. Digital innovation with its collaborative cross-functional teams is often very different from what employees are used to with regards to functions and hierarchies — resistance is inevitable.

"It's not necessary to have everyone on board in the early stages. Try to find areas where interests overlap, and create a starting point. Build a first version, test the idea and use the success story to gain the momentum needed for the next step," said Blosch.

Barrier No. 3: The Business Isn't Ready

Many business leaders are caught up in the hype around digital business. But when the CIO or CDO wants to start the transformation process, it turns out that the business doesn't have the skills or resources needed.

"CIOs should address the digital readiness of the organization to get an understanding of both business and IT readiness," Blosch advised. "Then, focus on the early adopters with the willingness and openness to change and leverage digital. But keep in mind that digital may just not be relevant to certain parts of the organization."

Barrier No. 4: The Talent Gap

Most organizations follow a traditional pattern — organized into functions such as IT, sales and supply chain and largely focused on operations. Change can be slow in this kind of environment.

Digital innovation requires an organization to adopt a different approach. People, processes and technology blend to create new business models and services. Employees need new skills focused on innovation, change and creativity along with the new technologies themselves, such as artificial intelligence (AI) and the Internet of Things (IoT).

"There are two approaches to breach the talent gap — upskill and bimodal," said Blosch. "In smaller or more innovative organizations, it is possible to redefine individuals' roles to include more skills and competencies needed to support digital. In other organizations, using a bimodal approach makes sense by creating a separate group to handle innovation with the requisite skill set."

Barrier No. 5: The Current Practices Don't Support the Talent

Having the right talent is essential, and having the right practices lets the talent work effectively. Highly structured and slow traditional processes don't work for digital. There are no tried and tested models to implement, but every organization has to find the practices that suits it best.

"Some organizations may shift to a product management-based approach for digital innovations because it allows for multiple iterations. Operational innovations can follow the usual approaches until the digital team is skilled and experienced enough to extend its reach and share the learned practices with the organization," Blosch explained.

Barrier No. 6: Change Isn't Easy

It's often technically challenging and expensive to make digital work. Developing platforms, changing the organizational structure, creating an ecosystem of partners — all of this costs time, resources and money.

Over the long term, enterprises should build the organizational capabilities that make change simpler and faster. To do that, they should develop a platform-based strategy that supports continuous change and design principles and then innovate on top of that platform, allowing new services to draw from the platform and its core services.

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 ...