Top tech trends for 2020
From hyper-automation to practical applications for blockchain, advances open new opportunities, according to Gartner
A strategic technology trend, according to the research and advisory group Gartner, is one with substantial disruptive potential that is beginning to break out of an emerging state into broader impact and use. It can also include technology that is growing rapidly with a high degree of volatility and poised to reach a tipping point over the next five years.
With those definitions in mind, Gartner has identified 10 strategic technology trends for 2020:
Hyper-automation: Hyper-automation involves using a combination of multiple machine learning (ML), packaged software and automation tools to deliver work. It refers not only to the variety of the tools themselves, but also to all the steps involved in automation itself (discover, analyse, design, automate, measure, monitor and reassess). Understanding the range of automation mechanisms, how they relate to one another and how they can be combined and coordinated is a major focus.
This trend began with robotic process automation (RPA). However, RPA alone is not hyper-automation. Hyper-automation requires a combination of tools to help support replicating pieces of where the human is involved in a task.
Multi-experience: Through 2028, the user experience will undergo a significant shift in how users perceive the digital world and how they interact with it. Conversational platforms are changing the way in which people interact with the digital world.
Virtual, augmented and mixed reality are all changing the way in which people perceive the digital world. This combined shift in both perception and interaction models is leading to the future multi-sensory and multi-modal experience.
“The model will shift from one of technology-literate people to one of people-literate technology. The burden of translating intent will move from the user to the computer,” says Brian Burke, research vice-president of Gartner. “This ability to communicate with users across many human senses will provide a richer environment for delivering nuanced information.”
Democratisation of expertise: This involves providing people access to technical expertise (for example, ML, application development) or business domain expertise (for example, sales or economic analysis) via a radically simplified experience and without requiring extensive and costly training.
“Citizen access” (for example, citizen data scientists, citizen integrators), as well as the evolution of citizen development and no-code models, are examples of democratisation.
Human augmentation: This explores how technology can be used to deliver cognitive and physical improvements as an integral part of the human experience. Physical augmentation enhances humans by changing their inherent physical capabilities by implanting or hosting a technology element on their bodies, such as a wearable device.
Cognitive augmentation can occur through accessing information and exploiting applications on traditional computer systems and the emerging multi-experience interface in “smart spaces”.
Over the next 10 years, increasing levels of physical and cognitive human augmentation will become prevalent as individuals seek personal enhancements. This will create a new “consumerisation” effect where employees seek to exploit their personal enhancements — and even extend them — to improve their office environment.
Transparency and traceability: Consumers are increasingly aware that their personal information is valuable and are demanding control. Organisations recognise the increasing risk of securing and managing personal data, and governments are implementing strict legislation to ensure they do. Transparency and traceability are critical elements to support these digital ethics and privacy needs.
Empowered edge: Edge computing means information processing and content collection and delivery are placed closer to the sources, repositories and consumers of this information. It tries to keep the traffic and processing local to reduce latency, exploit the capabilities of the edge and enable greater autonomy.
“Edge computing will become a dominant factor across virtually all industries and use cases as the edge is empowered with increasingly sophisticated and specialised computing resources and more data storage,” said Mr Burke. “Complex edge devices, including robots, drones, autonomous vehicles and operational systems will accelerate this shift.”
Distributed cloud: This involves the distribution of public cloud services to different locations while the originating public cloud provider assumes responsibility for the operation, governance, updates and evolution of the services. This represents a significant shift from the centralised model of most public cloud services.
Autonomous things: Autonomous things are physical devices that use artificial intelligence (AI) to automate functions previously performed by humans. The most recognisable forms are robots, drones, autonomous vehicles/ships and appliances. Their automation goes beyond that provided by rigid programming models, and they exploit AI to deliver advanced behaviours that interact more naturally with their surroundings and with people.
As the technology capability improves, regulation permits and social acceptance grows, autonomous things will increasingly be deployed in uncontrolled public spaces.
Practical blockchain: Blockchain has the potential to reshape industries by enabling trust, providing transparency and enabling value exchange, potentially lowering costs, reducing transaction times and improving cash flow. Assets can be traced to their origin, significantly reducing the opportunities for substitutions with counterfeit goods.
“Blockchain remains immature for enterprise deployments because of a range of technical issues including poor scalability and interoperability,” said Mr Burke. “Despite these challenges, the significant potential for disruption and revenue generation means organisations should begin evaluating blockchain, even if they don’t anticipate aggressive adoption in the near term.”
AI security: AI and ML will continue to be applied to augment human decision-making. While this creates great opportunities to enable hyper-automation and use autonomous things, it creates significant new challenges for the security team with a massive increase in potential points of attack. The focus should be on three key areas — protecting AI-powered systems, using AI to enhance security defence, and anticipating nefarious use of AI by attackers.