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

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.

"Gartner's top 10 strategic technology trends for 2018 tie into the Intelligent Digital Mesh. The intelligent digital mesh is a foundation for future digital business and ecosystems," said David Cearley, VP and Gartner Fellow. "IT leaders must factor these technology trends into their innovation strategies or risk losing ground to those that do."

The first three strategic technology trends explore how artificial intelligence (AI) and machine learning are seeping into virtually everything and represent a major battleground for technology providers over the next five years.

The next four trends 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.

The top 10 strategic technology trends for 2018 are:

1. AI Foundation

Creating systems that learn, adapt and potentially act autonomously will be a major battleground for technology vendors through at least 2020. The ability to use AI to enhance decision making, reinvent business models and ecosystems, and remake the customer experience will drive the payoff for digital initiatives through 2025.

"AI techniques are evolving rapidly and organizations will need to invest significantly in skills, processes and tools to successfully exploit these techniques and build AI-enhanced systems," said Cearley. "Investment areas can include data preparation, integration, algorithm and training methodology selection, and model creation. Multiple constituencies including data scientists, developers and business process owners will need to work together."

2. Intelligent Apps and Analytics

Over the next few years, virtually every app, application and service will incorporate some level of AI. Some of these apps will be obvious intelligent apps that could not exist without AI and machine learning. Others will be unobtrusive users of AI that provide intelligence behind the scenes. Intelligent apps create a new intelligent intermediary layer between people and systems and have the potential to transform the nature of work and the structure of the workplace.

Over the next few years, virtually every app, application and service will incorporate some level of AI.

"Explore intelligent apps as a way of augmenting human activity and not simply as a way of replacing people," said Cearley. "Augmented analytics is a particularly strategic growing area which uses machine learning to automate data preparation, insight discovery and insight sharing for a broad range of business users, operational workers and citizen data scientists."

AI has become the next major battleground in a wide range of software and service markets, including aspects of enterprise resource planning (ERP). Packaged software and service providers should outline how they'll be using AI to add business value in new versions in the form of advanced analytics, intelligent processes and advanced user experiences.

3. Intelligent Things

Intelligent things are physical things that go beyond the execution of rigid programming models to exploit AI to deliver advanced behaviors and interact more naturally with their surroundings and with people. AI is driving advances for new intelligent things (such as autonomous vehicles, robots and drones) and delivering enhanced capability to many existing things (such as Internet of Things [IoT] connected consumer and industrial systems).

"Currently, the use of autonomous vehicles in controlled settings (for example, in farming and mining) is a rapidly growing area of intelligent things. We are likely to see examples of autonomous vehicles on limited, well-defined and controlled roadways by 2022, but general use of autonomous cars will likely require a person in the driver's seat in case the technology should unexpectedly fail," said Cearley. "For at least the next five years, we expect that semiautonomous scenarios requiring a driver will dominate. During this time, manufacturers will test the technology more rigorously, and the nontechnology issues such as regulations, legal issues and cultural acceptance will be addressed."

4. Digital Twin

A digital twin refers to the digital representation of a real-world entity or system. Digital twins in the context of IoT projects is particularly promising over the next three to five years and is leading the interest in digital twins today.

Well-designed digital twins of assets have the potential to significantly improve enterprise decision making. These digital twins are linked to their real-world counterparts and are used to understand the state of the thing or system, respond to changes, improve operations and add value. Organizations will implement digital twins simply at first, then evolve them over time, improving their ability to collect and visualize the right data, apply the right analytics and rules, and respond effectively to business objectives.

"Over time, digital representations of virtually every aspect of our world will be connected dynamically with their real-world counterpart and with one another and infused with AI-based capabilities to enable advanced simulation, operation and analysis," said Cearley. "City planners, digital marketers, healthcare professionals and industrial planners will all benefit from this long-term shift to the integrated digital twin world."

5. Cloud to the Edge

Edge computing describes a computing topology in which information processing, and content collection and delivery, are placed closer to the sources of this information. Connectivity and latency challenges, bandwidth constraints and greater functionality embedded at the edge favors distributed models. Enterprises should begin using edge design patterns in their infrastructure architectures — particularly for those with significant IoT elements.

While many view cloud and edge as competing approaches, cloud is a style of computing where elastically scalable technology capabilities are delivered as a service and does not inherently mandate a centralized model.

"When used as complementary concepts, cloud can be the style of computing used to create a service-oriented model and a centralized control and coordination structure with edge being used as a delivery style allowing for disconnected or distributed process execution of aspects of the cloud service," said Cearley.

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

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.

"Gartner's top 10 strategic technology trends for 2018 tie into the Intelligent Digital Mesh. The intelligent digital mesh is a foundation for future digital business and ecosystems," said David Cearley, VP and Gartner Fellow. "IT leaders must factor these technology trends into their innovation strategies or risk losing ground to those that do."

The first three strategic technology trends explore how artificial intelligence (AI) and machine learning are seeping into virtually everything and represent a major battleground for technology providers over the next five years.

The next four trends 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.

The top 10 strategic technology trends for 2018 are:

1. AI Foundation

Creating systems that learn, adapt and potentially act autonomously will be a major battleground for technology vendors through at least 2020. The ability to use AI to enhance decision making, reinvent business models and ecosystems, and remake the customer experience will drive the payoff for digital initiatives through 2025.

"AI techniques are evolving rapidly and organizations will need to invest significantly in skills, processes and tools to successfully exploit these techniques and build AI-enhanced systems," said Cearley. "Investment areas can include data preparation, integration, algorithm and training methodology selection, and model creation. Multiple constituencies including data scientists, developers and business process owners will need to work together."

2. Intelligent Apps and Analytics

Over the next few years, virtually every app, application and service will incorporate some level of AI. Some of these apps will be obvious intelligent apps that could not exist without AI and machine learning. Others will be unobtrusive users of AI that provide intelligence behind the scenes. Intelligent apps create a new intelligent intermediary layer between people and systems and have the potential to transform the nature of work and the structure of the workplace.

Over the next few years, virtually every app, application and service will incorporate some level of AI.

"Explore intelligent apps as a way of augmenting human activity and not simply as a way of replacing people," said Cearley. "Augmented analytics is a particularly strategic growing area which uses machine learning to automate data preparation, insight discovery and insight sharing for a broad range of business users, operational workers and citizen data scientists."

AI has become the next major battleground in a wide range of software and service markets, including aspects of enterprise resource planning (ERP). Packaged software and service providers should outline how they'll be using AI to add business value in new versions in the form of advanced analytics, intelligent processes and advanced user experiences.

3. Intelligent Things

Intelligent things are physical things that go beyond the execution of rigid programming models to exploit AI to deliver advanced behaviors and interact more naturally with their surroundings and with people. AI is driving advances for new intelligent things (such as autonomous vehicles, robots and drones) and delivering enhanced capability to many existing things (such as Internet of Things [IoT] connected consumer and industrial systems).

"Currently, the use of autonomous vehicles in controlled settings (for example, in farming and mining) is a rapidly growing area of intelligent things. We are likely to see examples of autonomous vehicles on limited, well-defined and controlled roadways by 2022, but general use of autonomous cars will likely require a person in the driver's seat in case the technology should unexpectedly fail," said Cearley. "For at least the next five years, we expect that semiautonomous scenarios requiring a driver will dominate. During this time, manufacturers will test the technology more rigorously, and the nontechnology issues such as regulations, legal issues and cultural acceptance will be addressed."

4. Digital Twin

A digital twin refers to the digital representation of a real-world entity or system. Digital twins in the context of IoT projects is particularly promising over the next three to five years and is leading the interest in digital twins today.

Well-designed digital twins of assets have the potential to significantly improve enterprise decision making. These digital twins are linked to their real-world counterparts and are used to understand the state of the thing or system, respond to changes, improve operations and add value. Organizations will implement digital twins simply at first, then evolve them over time, improving their ability to collect and visualize the right data, apply the right analytics and rules, and respond effectively to business objectives.

"Over time, digital representations of virtually every aspect of our world will be connected dynamically with their real-world counterpart and with one another and infused with AI-based capabilities to enable advanced simulation, operation and analysis," said Cearley. "City planners, digital marketers, healthcare professionals and industrial planners will all benefit from this long-term shift to the integrated digital twin world."

5. Cloud to the Edge

Edge computing describes a computing topology in which information processing, and content collection and delivery, are placed closer to the sources of this information. Connectivity and latency challenges, bandwidth constraints and greater functionality embedded at the edge favors distributed models. Enterprises should begin using edge design patterns in their infrastructure architectures — particularly for those with significant IoT elements.

While many view cloud and edge as competing approaches, cloud is a style of computing where elastically scalable technology capabilities are delivered as a service and does not inherently mandate a centralized model.

"When used as complementary concepts, cloud can be the style of computing used to create a service-oriented model and a centralized control and coordination structure with edge being used as a delivery style allowing for disconnected or distributed process execution of aspects of the cloud service," said Cearley.

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

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As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Beyond Traditional Autoscaling: The Future of Kubernetes in AI Infrastructure

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

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Will AI Solve the Growing Data Divide?

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