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Gartner: Top 10 Strategic Technology Trends for 2018 - Part 2

Gartner highlighted the top strategic technology trends that will impact most organizations in 2018.

Gartner defines a strategic technology trend as one with substantial disruptive potential that is beginning to break out of an emerging state into broader impact and use, or which are rapidly growing trends with a high degree of volatility reaching tipping points over the next five years.

The first two trends listed in Part 2 focus on blending the digital and physical worlds to create an immersive, digitally enhanced environment.

The last three refer to exploiting connections between an expanding set of people and businesses, as well as devices, content and services to deliver digital business outcomes.

Start with Gartner: Top 10 Strategic Technology Trends for 2018 - Part 1

6. Conversational Platforms

Conversational platforms will drive the next big paradigm shift in how humans interact with the digital world. The burden of translating intent shifts from user to computer. The platform takes a question or command from the user and then responds by executing some function, presenting some content or asking for additional input. Over the next few years, conversational interfaces will become a primary design goal for user interaction and be delivered in dedicated hardware, core OS features, platforms and applications.

Conversational platforms will drive the next big paradigm shift in how humans interact with the digital world.

"Conversational platforms have reached a tipping point in terms of understanding language and basic user intent, but they still fall short," said Cearley. "The challenge that conversational platforms face is that users must communicate in a very structured way, and this is often a frustrating experience. A primary differentiator among conversational platforms will be the robustness of their conversational models and the application programming interface (API) and event models used to access, invoke and orchestrate third-party services to deliver complex outcomes." 

7. Immersive Experience

While conversational interfaces are changing how people control the digital world, virtual, augmented and mixed reality are changing the way that people perceive and interact with the digital world. The virtual reality (VR) and augmented reality (AR) market is currently adolescent and fragmented. Interest is high, resulting in many novelty VR applications that deliver little real business value outside of advanced entertainment, such as video games and 360-degree spherical videos. To drive real tangible business benefit, enterprises must examine specific real-life scenarios where VR and AR can be applied to make employees more productive and enhance the design, training and visualization processes.

Mixed reality, a type of immersion that merges and extends the technical functionality of both AR and VR, is emerging as the immersive experience of choice providing a compelling technology that optimizes its interface to better match how people view and interact with their world. Mixed reality exists along a spectrum and includes head-mounted displays (HMDs) for augmented or virtual reality as well as smartphone and tablet-based AR and use of environmental sensors. Mixed reality represents the span of how people perceive and interact with the digital world.

8. Blockchain

Blockchain is evolving from a digital currency infrastructure into a platform for digital transformation. Blockchain technologies offer a radical departure from the current centralized transaction and record-keeping mechanisms and can serve as a foundation of disruptive digital business for both established enterprises and startups. Although the hype surrounding blockchains originally focused on the financial services industry, blockchains have many potential applications, including government, healthcare, manufacturing, media distribution, identity verification, title registry and supply chain. Although it holds long-term promise and will undoubtedly create disruption, blockchain promise outstrips blockchain reality, and many of the associated technologies are immature for the next two to three years.

9. Event Driven

Central to digital business is the idea that the business is always sensing and ready to exploit new digital business moments. Business events could be anything that is noted digitally, reflecting the discovery of notable states or state changes, for example, completion of a purchase order, or an aircraft landing. With the use of event brokers, IoT, cloud computing, blockchain, in-memory data management and AI, business events can be detected faster and analyzed in greater detail. But technology alone without cultural and leadership change does not deliver the full value of the event-driven model. Digital business drives the need for IT leaders, planners and architects to embrace event thinking.

10. Continuous Adaptive Risk and Trust

To securely enable digital business initiatives in a world of advanced, targeted attacks, security and risk management leaders must adopt a continuous adaptive risk and trust assessment (CARTA) approach to allow real-time, risk and trust-based decision making with adaptive responses. Security infrastructure must be adaptive everywhere, to embrace the opportunity — and manage the risks — that comes delivering security that moves at the speed of digital business.

As part of a CARTA approach, organizations must overcome the barriers between security teams and application teams, much as DevOps tools and processes overcome the divide between development and operations. Information security architects must integrate security testing at multiple points into DevOps workflows in a collaborative way that is largely transparent to developers, and preserves the teamwork, agility and speed of DevOps and agile development environments, delivering "DevSecOps." CARTA can also be applied at runtime with approaches such as deception technologies. Advances in technologies such as virtualization and software-defined networking has made it easier to deploy, manage and monitor "adaptive honeypots" — the basic component of network-based deception.

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.

Gartner: Top 10 Strategic Technology Trends for 2018 - Part 2

Gartner highlighted the top strategic technology trends that will impact most organizations in 2018.

Gartner defines a strategic technology trend as one with substantial disruptive potential that is beginning to break out of an emerging state into broader impact and use, or which are rapidly growing trends with a high degree of volatility reaching tipping points over the next five years.

The first two trends listed in Part 2 focus on blending the digital and physical worlds to create an immersive, digitally enhanced environment.

The last three refer to exploiting connections between an expanding set of people and businesses, as well as devices, content and services to deliver digital business outcomes.

Start with Gartner: Top 10 Strategic Technology Trends for 2018 - Part 1

6. Conversational Platforms

Conversational platforms will drive the next big paradigm shift in how humans interact with the digital world. The burden of translating intent shifts from user to computer. The platform takes a question or command from the user and then responds by executing some function, presenting some content or asking for additional input. Over the next few years, conversational interfaces will become a primary design goal for user interaction and be delivered in dedicated hardware, core OS features, platforms and applications.

Conversational platforms will drive the next big paradigm shift in how humans interact with the digital world.

"Conversational platforms have reached a tipping point in terms of understanding language and basic user intent, but they still fall short," said Cearley. "The challenge that conversational platforms face is that users must communicate in a very structured way, and this is often a frustrating experience. A primary differentiator among conversational platforms will be the robustness of their conversational models and the application programming interface (API) and event models used to access, invoke and orchestrate third-party services to deliver complex outcomes." 

7. Immersive Experience

While conversational interfaces are changing how people control the digital world, virtual, augmented and mixed reality are changing the way that people perceive and interact with the digital world. The virtual reality (VR) and augmented reality (AR) market is currently adolescent and fragmented. Interest is high, resulting in many novelty VR applications that deliver little real business value outside of advanced entertainment, such as video games and 360-degree spherical videos. To drive real tangible business benefit, enterprises must examine specific real-life scenarios where VR and AR can be applied to make employees more productive and enhance the design, training and visualization processes.

Mixed reality, a type of immersion that merges and extends the technical functionality of both AR and VR, is emerging as the immersive experience of choice providing a compelling technology that optimizes its interface to better match how people view and interact with their world. Mixed reality exists along a spectrum and includes head-mounted displays (HMDs) for augmented or virtual reality as well as smartphone and tablet-based AR and use of environmental sensors. Mixed reality represents the span of how people perceive and interact with the digital world.

8. Blockchain

Blockchain is evolving from a digital currency infrastructure into a platform for digital transformation. Blockchain technologies offer a radical departure from the current centralized transaction and record-keeping mechanisms and can serve as a foundation of disruptive digital business for both established enterprises and startups. Although the hype surrounding blockchains originally focused on the financial services industry, blockchains have many potential applications, including government, healthcare, manufacturing, media distribution, identity verification, title registry and supply chain. Although it holds long-term promise and will undoubtedly create disruption, blockchain promise outstrips blockchain reality, and many of the associated technologies are immature for the next two to three years.

9. Event Driven

Central to digital business is the idea that the business is always sensing and ready to exploit new digital business moments. Business events could be anything that is noted digitally, reflecting the discovery of notable states or state changes, for example, completion of a purchase order, or an aircraft landing. With the use of event brokers, IoT, cloud computing, blockchain, in-memory data management and AI, business events can be detected faster and analyzed in greater detail. But technology alone without cultural and leadership change does not deliver the full value of the event-driven model. Digital business drives the need for IT leaders, planners and architects to embrace event thinking.

10. Continuous Adaptive Risk and Trust

To securely enable digital business initiatives in a world of advanced, targeted attacks, security and risk management leaders must adopt a continuous adaptive risk and trust assessment (CARTA) approach to allow real-time, risk and trust-based decision making with adaptive responses. Security infrastructure must be adaptive everywhere, to embrace the opportunity — and manage the risks — that comes delivering security that moves at the speed of digital business.

As part of a CARTA approach, organizations must overcome the barriers between security teams and application teams, much as DevOps tools and processes overcome the divide between development and operations. Information security architects must integrate security testing at multiple points into DevOps workflows in a collaborative way that is largely transparent to developers, and preserves the teamwork, agility and speed of DevOps and agile development environments, delivering "DevSecOps." CARTA can also be applied at runtime with approaches such as deception technologies. Advances in technologies such as virtualization and software-defined networking has made it easier to deploy, manage and monitor "adaptive honeypots" — the basic component of network-based deception.

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.