Skip to main content

Gartner: 3 Scale Accelerators to Drive Digital Transformation

CIOs trying to lead digital transformation at the speed needed to succeed need a mix of three scale accelerators, according to Gartner, Inc. The three scale accelerators include: digital dexterity, network effect technologies, and an industrialized digital platform.

During the opening keynote today at Gartner Symposium/ITxpo, Gartner analysts emphasized that scale is not just about size, it occurs up, across and out. Scaling Up allows for gaining efficiencies. Scaling Across quickly takes capabilities learned from one organization into another, while Scaling Out interconnects internal and external platforms and ecosystems.

First Scale Accelerator: Digital Dexterity

Digital dexterity is about a new organizational design and a new talent mix for a new working environment – a high-performing digital workplace. Organizations must change internally to change externally.

"To scale, we need people with digital dexterity. People who are collaborative, agile, analytical, innovative and creative," said Tina Nunno, VP and Gartner Fellow. "People who have both the ability and the desire to exploit existing and emerging technologies for better business outcomes."

A digitally dexterous culture requires three building blocks:

■ Technology

■ Engagement

■ Diversity

"It’s time to build your technology for user experience and double down on experiential skills, such as design thinking, guided navigation, and a/b testing. These all become your go-to tools," said Leigh McMullen, Managing VP at Gartner. "Invest in SaaS applications that make it easy for employees to do for themselves – things like data visualization and application integration. Exploit virtual personal assistants to free everyone from low-value tasks."

The second building block is engagement. "Make people and engagement the design center for your technology and your processes," Nunno said. "For that we can use the science of behavioral change. For example, by using peer advocates, trusted influencers, and social norming, we get closer to creating the right employee experience."

The third element to build a culture of digital dexterity is diversity. CIOs should look at exploiting diversity in all forms, such as diverse data, diverse talents, diverse suppliers, diverse backgrounds, and diverse cultures. "Diversity allows us to overcome all forms of bias to harness the power of the crowd and digital," McMullen said.

Second Scale Accelerator: Network Effect Technologies

Network effect technologies transform the CIO’s work from making tactical technology decisions into building strategic platforms. This unique set of technologies creates virtuous patterns of growth, where waves of disruption build upon each other, exponentially. Three network effect technologies to focus on for 2018 include: the Internet of Things (IoT), application programming interfaces (APIs), and artificial intelligence (AI).

"IoT scales the physical world. IoT allows us to sense, measure and mediate everything from oil pipelines to human veins. It allows us to make better decisions faster," Nunno said. "As the number of connected devices grows, you go from no information to abundant data. The network effect of IoT quickly converts individual objects into systems.

Nunno also explained the type of people to get the job done. She said, "Find people who are able and eager to embed all types of intelligence in IoT. Engage data management professionals to ensure you have diverse source data. Leverage the digital dexterity of citizen data scientists."

While IoT scales the physical world, APIs scale relationships into ecosystems. They enable CIOs to easily connect partners, employees, and even competitors into a vibrant, webscale network that unlocks value for everyone.

"Value emerges slowly, and then it accelerates quickly as more participants are added to the ecosystem and new APIs are discovered and used. That is the network effect," said McMullen.

With IoT scaling the physical world, and APIs scaling relationships, think of AI as scaling people. Gartner believes that AI will help people, not replace them. Certain jobs have been lost in every technology revolution, and new jobs have been created. AI is no different. Gartner analysts said the real potential with AI is the augmentation of people.

"AI’s best use today and well into the future will be to augment human capabilities," Nunno said. "A human machine is smarter than either by themselves. The machine scales the person. The person scales the machine."

Third Scale Accelerator: Industrialize the Digital Platform

The industrialized digital platform unleashes the digital dexterity of your workforce, and it unlocks the potential of network effect technology. To industrialize means using a digital platform to create new digital market places.

"The beauty of the industrialized digital platform is that it enables you to create value in all directions at scale: up, across and out," Nunno said. "Value creation used to be one directional: from the organization to customers. Now value creation can scale in all directions, from anyone, anywhere."

Organizations will need to set their digital ambition by determining what kind of organization they want to be. Without digital business ambition, organizations just have a collection of projects.

Next, CIOs should build on their legacy systems. They should combine their modernized legacy applications and their digital platform for massive integration complexity on a massive scale. They should integrate their platforms internally, as well as with external ecosystem partners. Integrating externally is key because top performing CIOs expect to double the number of important ecosystem partners they have during the next two years.

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 ...

Gartner: 3 Scale Accelerators to Drive Digital Transformation

CIOs trying to lead digital transformation at the speed needed to succeed need a mix of three scale accelerators, according to Gartner, Inc. The three scale accelerators include: digital dexterity, network effect technologies, and an industrialized digital platform.

During the opening keynote today at Gartner Symposium/ITxpo, Gartner analysts emphasized that scale is not just about size, it occurs up, across and out. Scaling Up allows for gaining efficiencies. Scaling Across quickly takes capabilities learned from one organization into another, while Scaling Out interconnects internal and external platforms and ecosystems.

First Scale Accelerator: Digital Dexterity

Digital dexterity is about a new organizational design and a new talent mix for a new working environment – a high-performing digital workplace. Organizations must change internally to change externally.

"To scale, we need people with digital dexterity. People who are collaborative, agile, analytical, innovative and creative," said Tina Nunno, VP and Gartner Fellow. "People who have both the ability and the desire to exploit existing and emerging technologies for better business outcomes."

A digitally dexterous culture requires three building blocks:

■ Technology

■ Engagement

■ Diversity

"It’s time to build your technology for user experience and double down on experiential skills, such as design thinking, guided navigation, and a/b testing. These all become your go-to tools," said Leigh McMullen, Managing VP at Gartner. "Invest in SaaS applications that make it easy for employees to do for themselves – things like data visualization and application integration. Exploit virtual personal assistants to free everyone from low-value tasks."

The second building block is engagement. "Make people and engagement the design center for your technology and your processes," Nunno said. "For that we can use the science of behavioral change. For example, by using peer advocates, trusted influencers, and social norming, we get closer to creating the right employee experience."

The third element to build a culture of digital dexterity is diversity. CIOs should look at exploiting diversity in all forms, such as diverse data, diverse talents, diverse suppliers, diverse backgrounds, and diverse cultures. "Diversity allows us to overcome all forms of bias to harness the power of the crowd and digital," McMullen said.

Second Scale Accelerator: Network Effect Technologies

Network effect technologies transform the CIO’s work from making tactical technology decisions into building strategic platforms. This unique set of technologies creates virtuous patterns of growth, where waves of disruption build upon each other, exponentially. Three network effect technologies to focus on for 2018 include: the Internet of Things (IoT), application programming interfaces (APIs), and artificial intelligence (AI).

"IoT scales the physical world. IoT allows us to sense, measure and mediate everything from oil pipelines to human veins. It allows us to make better decisions faster," Nunno said. "As the number of connected devices grows, you go from no information to abundant data. The network effect of IoT quickly converts individual objects into systems.

Nunno also explained the type of people to get the job done. She said, "Find people who are able and eager to embed all types of intelligence in IoT. Engage data management professionals to ensure you have diverse source data. Leverage the digital dexterity of citizen data scientists."

While IoT scales the physical world, APIs scale relationships into ecosystems. They enable CIOs to easily connect partners, employees, and even competitors into a vibrant, webscale network that unlocks value for everyone.

"Value emerges slowly, and then it accelerates quickly as more participants are added to the ecosystem and new APIs are discovered and used. That is the network effect," said McMullen.

With IoT scaling the physical world, and APIs scaling relationships, think of AI as scaling people. Gartner believes that AI will help people, not replace them. Certain jobs have been lost in every technology revolution, and new jobs have been created. AI is no different. Gartner analysts said the real potential with AI is the augmentation of people.

"AI’s best use today and well into the future will be to augment human capabilities," Nunno said. "A human machine is smarter than either by themselves. The machine scales the person. The person scales the machine."

Third Scale Accelerator: Industrialize the Digital Platform

The industrialized digital platform unleashes the digital dexterity of your workforce, and it unlocks the potential of network effect technology. To industrialize means using a digital platform to create new digital market places.

"The beauty of the industrialized digital platform is that it enables you to create value in all directions at scale: up, across and out," Nunno said. "Value creation used to be one directional: from the organization to customers. Now value creation can scale in all directions, from anyone, anywhere."

Organizations will need to set their digital ambition by determining what kind of organization they want to be. Without digital business ambition, organizations just have a collection of projects.

Next, CIOs should build on their legacy systems. They should combine their modernized legacy applications and their digital platform for massive integration complexity on a massive scale. They should integrate their platforms internally, as well as with external ecosystem partners. Integrating externally is key because top performing CIOs expect to double the number of important ecosystem partners they have during the next two years.

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 ...