Top 10 Strategic Technology Trends for 2019

Gartner highlighted the top strategic technology trends that organizations need to explore in 2019. They define 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 Intelligent Digital Mesh has been a consistent theme for the past two years and continues as a major driver through 2019. Trends under each of these three themes are a key ingredient in driving a continuous innovation process as part of a ContinuousNEXT strategy,“ said David Cearley, vice president and Gartner Fellow. The top 10 strategic technology trends for 2019 are:

1. Autonomous Things

Autonomous things, such as robots, drones and autonomous vehicles, use AI to automate functions previously performed by humans. Their automation goes beyond the automation provided by rigid programing models and they exploit AI to deliver advanced behaviors that interact more naturally with their surroundings and with people.

2. Augmented Analytics

Augmented analytics focuses on a specific area of augmented intelligence, using machine learning to transform how analytics content is developed, consumed and shared. Augmented analytics capabilities will advance rapidly to mainstream adoption, as a key feature of data preparation, data management, modern analytics, business process management, process mining and data science platforms.

Automated insights from augmented analytics will also be embedded in enterprise applications to optimize the decisions and actions of all employees within their context, not just those of analysts and data scientists. Augmented analytics automates the process of data preparation, insight generation and insight visualization, eliminating the need for professional data scientists in many situations.

3. AI-Driven Development

The market is rapidly shifting from an approach in which professional data scientists must partner with application developers to create most AI-enhanced solutions to a model in which the professional developer can operate alone using predefined models delivered as a service. This provides the developer with an ecosystem of AI algorithms and models, as well as development tools tailored to integrating AI capabilities and models into a solution.

Another level of opportunity for professional application development arises as AI is applied to the development process itself to automate various data science, application development and testing functions. By 2022, at least 40 percent of new application development projects will have AI co-developers on their team.

4. Digital Twins

A digital twin refers to the digital representation of a real-world entity or system. By 2020, Gartner estimates there will be more than 20 billion connected sensors and endpoints and digital twins will exist for potentially billions of things. Organizations will implement digital twins simply at first. They will 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.

5. Empowered Edge

The edge refers to endpoint devices used by people or embedded in the world around us. Edge computing describes a computing topology in which information processing, and content collection and delivery, are placed closer to these endpoints. It tries to keep the traffic and processing local, with the goal being to reduce traffic and latency.

In the near term, edge is being driven by IoT and the need keep the processing close to the end rather than on a centralized cloud server. Over the next five years, specialized AI chips, along with greater processing power, storage and other advanced capabilities, will be added to a wider array of edge devices. The extreme heterogeneity of this embedded IoT world and the long life cycles of assets such as industrial systems will create significant management challenges.

6. Immersive Experience

Conversational platforms are changing the way in which people interact with the digital world. Virtual reality, augmented reality and mixed reality are changing the way in which people perceive the digital world. This combined shift in perception and interaction models leads to the future immersive user experience.

7. Blockchain

Blockchain, a type of distributed ledger, promises to reshape industries by enabling trust, providing transparency and reducing friction across business ecosystems potentially lowering costs, reducing transaction settlement times and improving cash flow.

Today, trust is placed in banks, clearinghouses, governments and many other institutions as central authorities with the “single version of the truth“ maintained securely in their databases. The centralized trust model adds delays and friction costs to transactions. Blockchain provides an alternative trust mode and removes the need for central authorities in arbitrating transactions.

8. Smart Spaces

A smart space is a physical or digital environment in which humans and technology-enabled systems interact in increasingly open, connected, coordinated and intelligent ecosystems. Multiple elements, including people, processes, services and things, come together in a smart space to create a more immersive, interactive and automated experience for a target set of people and industry scenarios.

9. Digital Ethics and Privacy

Digital ethics and privacy is a growing concern for individuals, organizations and governments. People are increasingly concerned about how their personal information is being used by organizations in both the public and private sector, and the backlash will only increase for organizations that are not proactively addressing these concerns.

10. Quantum Computing

Quantum computing is a type of nonclassical computing that operates on the quantum state of subatomic particles that represent information as elements denoted as quantum bits. The parallel execution and exponential scalability of quantum computers means they excel with problems too complex for a traditional approach or where a traditional algorithms would take too long to find a solution.

Industries such as automotive, financial, insurance, pharmaceuticals, military and research organizations have the most to gain from the advancements in QC. In the pharmaceutical industry, for example, QC could be used to model molecular interactions at atomic levels to accelerate time to market for new cancer-treating drugs or QC could accelerate and more accurately predict the interaction of proteins leading to new pharmaceutical methodologies.