From ‘practical blockchain’ to ‘shadow AI’: A look at the top strategic ICT trends for 2020

Prepare for the impact of these trends — they will transform industries and your business, reports Gartner

The model will shift from one of technology-literate people to one of people-literate technology

Brian Burke, Gartner

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 is rapidly growing with a high degree of volatility reaching tipping points over the next five years.

These strategic technology trends and their combinations have the potential both to create opportunity and to drive significant disruption, it states.

The analyst firm lists the top 10 technology trends that enterprise architecture and technology innovation leaders must evaluate over the next year.

Through 2025, technologies related to these trends will experience significant changes, cross critical tipping points and reach new levels of maturity that expand and enable repeatable use cases and reduce risk, according to a new report by Gartner analysts David Cearley, Nick Jones, David Smith, Brian Burke, Arun Chandrasekaran and CK Lu.

“Technology innovation leaders must examine the business impact of our top 10 strategic technology trends and seize the opportunities to enhance existing, or create new, processes, products and business models,” they advise. 

Credit: Gartner

“Prepare for the impact of these trends — they will transform industries and your business.”

The top strategic technology trends are:


Hyperautomation is the combination of multiple machine learning (ML), packaged software and automation tools to deliver work. 

Gartner says the term refers not only to the breadth of the pallet of tools, but also to all the steps of 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 for hyperautomation, it states. 

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By 2022, 30 per cent of organisations using AI for decision-making will contend with ‘shadow AI’ as the biggest risk to effective and ethical decisions

Gartner says this trend was kicked off with robotic process automation (RPA). However, it states, RPA alone is not hyperautomation.  

Hyperautomation requires a combination of tools to help support replicating pieces of where the human is involved in a task. 

Gartner says to derive the full benefits of hyperautomation, organisations need an overarching view across their functional and process silos.  

“Indeed, developing more and more sophisticated models is akin to developing a digital twin of an organisation,” it states. 


Through 2028, the user experience will undergo a significant shift in how users perceive the digital world and how they interact with it, reports Gartner.

Conversational platforms are changing the way in which people interact with the digital world. Virtual reality (VR), augmented reality (AR) and mixed reality (MR) are changing the way in which people perceive the digital world. This combined shift in both perception and interaction models leads to the future multisensory and multimodal 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 at Gartner. “This ability to communicate with users across many human senses will provide a richer environment for delivering nuanced information.” 

Gartner says multiexperience design requires the back end to be flexible enough to support the different capabilities and workflows of every app. “Because users don’t always use the same devices, and often switch from one device to another during their working day, the back end must offer a continuous experience.” 

Democratisation of expertise 

Democratisation is focused on providing people with access to technical expertise (such as ML and application development) or business domain expertise (such a sales process, economic analysis) via a radically simplified experience and without requiring extensive and costly training. “Citizen access” (such as citizen data scientists and citizen integrators), as well as the evolution of citizen development and no-code models, are examples of this.  

Through 2023, Gartner expects four key areas of the democratisation trend to accelerate, including democratisation of data and analytics (tools targeting data scientists expanding to target the professional developer community), democratisation of development (AI tools to leverage in custom-developed applications), democratisation of design (expanding on the low-code, no-code phenomena with automation of additional application development functions to empower the citizen-developer) and democratisation of knowledge. The latter involves non-IT professionals gaining access to tools and expert systems that empower them to exploit and apply specialised skills beyond their own expertise and training. 

Organisations may have to deal with ‘shadow AI’, a natural consequence of democratisation where individuals without formal training exploit easy-to-use tools to develop their own AI-powered solutions and provide peer-to-peer support to others in similar efforts.  

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As autonomous things proliferate, we expect a shift from standalone intelligent things to a swarm of collaborative intelligent things where multiple devices will work together, either independently of people or with human input

Brian Burke, Gartner

Like ‘shadow IT’, ‘shadow AI’ has both a positive and a negative side, says Gartner. 

Shadow AI takes “bring your own” to a more granular level by allowing “bring your own data” and “bring your own algorithm” outside the ownership, control or stewardship of IT, explains Gartner.  

Gartner says ‘shadow IT’ is not inherently bad as long as practices and training are in place. 

“By 2022, 30 per cent of organisations using AI for decision making will contend with ‘shadow AI’ as the biggest risk to effective and ethical decisions.” 

Human augmentation

Human augmentation 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 multiexperience interface in smart spaces. 

Over the next 10 years, Gartner says 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 the workplace. 

Implementing human augmentation technologies and processes poses serious ethical issues, notes Gartner. 

These include ethical considerations and assessments for determining specific vulnerabilities, risks and moral issues.  

For example, does the digital divide widen as affluent individuals can augment themselves and their children while less affluent people cannot? Answers to these societal issues will become increasingly important, says Gartner. 

It notes how companies that misuse personal data will lose the trust of their customers.  

“Trustworthiness is a key factor in driving revenue and profitability. Building customer trust in an organisation is difficult, but losing it is easy,” says Gartner. 

“However, organisations that gain and maintain the trust of their customers will thrive. We expect that companies that are digitally trustworthy will generate more online profit than those that aren’t.” 

Transparency and traceability 

Consumers are increasingly aware that their personal information is valuable and are demanding control, say Gartner. 

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. 

Transparency and traceability refer to a range of attitudes, actions and supporting technologies and practices designed to address regulatory requirements, preserve an ethical approach to use of artificial intelligence (AI) and other advanced technologies, and repair the growing lack of trust in companies.  

Gartner says as organisations build out transparency and trust practices, they must focus on three areas: AI and ML; personal data privacy, ownership and control; and ethically aligned design.

The empowered edge

Edge computing is a computing topology in which 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 at the edge. 

“Much of the current focus on edge computing comes from the need for IoT systems to deliver disconnected or distributed capabilities into the embedded IoT world for specific industries such as manufacturing or retail,” says Burke.  

“However, edge computing will become a dominant factor across virtually all industries and use cases as the edge is empowered with increasingly more sophisticated and specialised compute resources and more data storage. Complex edge devices, including robots, drones, autonomous vehicles and operational systems will accelerate this shift.”  

Edge computing and other distributed environments will challenge data management technology provider capabilities, resulting in data and analytics leaders more closely scrutinising how they navigate related technology markets, says Gartner.  

It advises organisations to assess incumbent and prospective vendors based on ability to process and govern distributed data. 

Distributed cloud 

A distributed cloud is the distribution of public cloud services to different locations while the originating public cloud provider assumes responsibility for the operation, governance, updates to and evolution of the services. This represents a significant shift from the centralised model of most public cloud services and will lead to a new era in cloud computing, reports Gartner. 

Gartner notes that the distributed cloud is in the early stages of development. Many providers aim to offer most of their public services in a distributed manner in the long term. But for now, they provide only a subset - and often a small subset - of their services in a distributed way.  

Some of the providers’ approaches do not support the full delivery, operation and update elements of a full distributed cloud. Providers are extending services to third-party data centres and out to the edge with offerings such as Microsoft Azure Stack, Oracle Cloud at Customer, Google’s Anthos, IBM Red Hat and AWS Outposts. 

Autonomous things 

Autonomous things are physical devices that use AI to automate functions previously performed by humans. The most recognisable forms of autonomous things are robots, drones, autonomous vehicles/ships and appliances.  

Their automation goes beyond the automation 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. 

“As autonomous things proliferate, we expect a shift from standalone intelligent things to a swarm of collaborative intelligent things where multiple devices will work together, either independently of people or with human input,” says Burke.  

“For example, heterogeneous robots can operate in a coordinated assembly process. In the delivery market, the most effective solution may be to use an autonomous vehicle to move packages to the target area. Robots and drones aboard the vehicle could then affect final delivery of the package.” 

When exploring particular use cases for autonomous things, start with an understanding of the space or spaces in which the thing will operate and the people, obstacles, terrain and other autonomous objects it will need to interact with, advises Gartner. 

“Next, consider the outcomes you are trying to achieve with the autonomous thing. Finally, consider which technical capabilities will be needed to address this defined scenario.”  

Practical blockchain

Blockchain has the potential to reshape industries by enabling trust, providing transparency and enabling value exchange across business ecosystems, potentially lowering costs, reducing transaction settlement times and improving cash flow, says Gartner.  

Assets can be traced to their origin, significantly reducing the opportunities for substitutions with counterfeit goods. Asset tracking also has value in other areas, such as tracing food across a supply chain to more easily identify the origin of contamination or track individual parts to assist in product recalls.  

Another area in which blockchain has potential is identity management. Smart contracts can be programmed into the blockchain where events can trigger actions; for example, payment is released when goods are received. 

“Blockchain remains immature for enterprise deployments due to a range of technical issues including poor scalability and interoperability. 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 of the technologies in the near term,” says Burke. 

Gartner further advises: “Identify how the term ‘blockchain’ is being used, both internally and by providers. Develop clear definitions for internal discussions. Use caution when interacting with vendors that have ill-defined/nonexistent blockchain offerings or thinly veiled repackaging of legacy offerings as blockchain solutions.” 

AI security 

The next frontier of AI-related security concerns is emerging as attackers begin to use ML and other AI techniques to power their attacks, reports Gartner.

Attackers have just started to leverage ML. They explore ML in

many security areas and are helped by the commoditization of ML tools and the availability of training data. 

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Use caution when interacting with vendors that have ill-defined/nonexistent blockchain offerings or thinly veiled repackaging of legacy offerings as blockchain solutions

AI and ML will continue to be applied to augment human decision making across a broad set of use cases, says Gartner.  

“While this creates great opportunities to enable hyperautomation and leverage autonomous things to deliver business transformation, it creates significant new challenges for the security team and risk leaders with a massive increase in potential points of attack with IoT, cloud computing, microservices and highly connected systems in smart spaces.” 

Thus, security and risk leaders should focus on three key areas — protecting AI-powered systems, leveraging AI to enhance security defense, and anticipating nefarious use of AI by attackers. 

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