TECHNOLOGY TRENDS Today’s trend is tomorrow’s mainstream adoption, and as entrepreneur you need to consider how this is affecting your businesses or where opportunities are for your business and what you should prioritise.
This branch of robotics is about automating business processes as far as possible to make them more precise, more efficient and up to 10 times faster by combining technologies like RPA (Robotic Process Automation), Artificial Intelligence (AI) and machine learning, amongst others.
In simply terms hyper automation is the continuous integration of automation into business operations. It equates to more machines doing more things. Automation uses technology to automate tasks that once required humans.
This concept involving the use of an ecosystem of advanced automation technologies to augment enterprises' use of human intelligence. The aim is to create increasingly automated business processes so that better-informed and more agile organizations can capitalize on data and insights for more efficient decision-making.
Multi-experience, a concept introduced by Gartner in 2019, is about adapting to the growing market of devices and offering a consistent user experience across multiple touchpoints like smartphones, wearables, desktops, and other modalities (voice, touch, gesture, etc).
Simple example - a multi-experience development platform (MXDP) will be a development platform that is used for developing not just mobile and web applications, but also chat, voice, augmented reality and wearable experiences. MXDP platforms help businesses quickly scale their app development projects across a range of devices, platforms, and form factors. Basically, MXDPs are the evolution of omnichannel MADPs.
Multi-experience replaces technology-literate people with people-literate technology. In this trend, the traditional idea of a computer evolves from a single point of interaction to include multisensory and multi-touch-point interfaces like wearables and advanced computer sensors.
The future is multi-experience digital platforms and as technology advances, this will continue to become more and more apparent.
Access by people to technical and business experience will take place without expensive requirements and will revolve around four big areas: data and analysis, development, design and know-how.
Democratisation of technology means providing people with easy access to technical or business expertise without extensive (and costly) training.
It focuses on four key areas — application development, data and analytics, design and knowledge — and is often referred to as “citizen access,” which has led to the rise of citizen data scientists, citizen programmers and more.
For example, democratization would enable developers to generate data models without having the skills of a data scientist. They would instead rely on AI-driven development to generate code and automate testing.
Everywhere you look, democratization is happening in business:
Uber gave everyone the opportunity to be a driver and a passenger — without owning a single taxi.
AirBnB gave everyone the opportunity to own rental property and be a renter — without owning the real estate.
Amazon gave everyone the opportunity to be a retailer and a consumer — without owning the products (in most cases, anyway).
Technological trends also involve the use of innovation to improve our physical and cognitive abilities, from subcutaneous implants to greater access to information.
The first level of human augmentation is replication. This refers to any augmentation that replicates something a typical person can already do.
Human augmentation is the use of technology to enhance a person’s cognitive and physical experiences.
Human augmentation carries a range of cultural and ethical implications. For example, using CRISPR technologies to augment genes has significant ethical implications.
Physical augmentation falls into four main categories: Sensory augmentation (hearing, vision, perception), appendage and biological function augmentation (exoskeletons, prosthetics), brain augmentation (implants to treat seizures) and genetic augmentation (somatic gene and cell therapy).
AI and ML are increasingly used to make decisions in place of humans
Cognitive augmentation enhances a human’s ability to think and make better decisions, for example, exploiting information and applications to enhance learning or new experiences.
Cognitive augmentation also includes some technology in the brain augmentation category as they are physical implants that deal with cognitive reasoning.
Consumers are demanding greater control over their personal data. Transparency and traceability are fundamental in this sense to meet regulatory requirements, maintain ethics in the use of technology and halt the increase in mistrust of companies.
The evolution of technology is creating a trust crisis. As consumers become more aware of how their data is being collected and used, organisations are also recognising the increasing liability of storing and gathering the data.
Additionally, AI and ML are increasingly used to make decisions in place of humans, evolving the trust crisis and driving the need for ideas like explainable AI and AI governance.
This trend requires a focus on six key elements of trust: Ethics, integrity, openness, accountability, competence and consistency.
Legislation, like the European Union’s General Data Protection Regulation (GDPR), is being enacted around the world, driving evolution and laying the ground rules for organisations.
This branch of IT will have a big impact on the Internet of Things (IoT) by making it possible for data generated by devices to be processed locally, without it needing to be uploaded to the cloud or sent to an external data centre.
Edge computing is a topology where information processing and content collection and delivery are placed closer to the sources of the information, with the idea that keeping traffic local and distributed will reduce latency.
This includes all the technology on the Internet of Things (IoT). Empowered edge looks at how these devices are increasing and forming the foundations for smart spaces, and moves key applications and services closer to the people and devices that use them.
Faster networking technologies, such as 5G wireless, are allowing for edge computing systems to accelerate the creation or support of real-time applications, such as video processing and analytics, self-driving cars, artificial intelligence and robotics, virtual and augmented reality, smart cities.
Hybrid cloud refers to a mixed computing, storage, and services environment made up of on-premises infrastructure, private cloud services, and a public cloud—such as Amazon Web Services (AWS) or Microsoft Azure
We will be seeing the decentralisation of most cloud services. However, the provider of the source public cloud will retain responsibility for the operation, control, updating and evolution of the services.
Distributed cloud allows data centers to be located anywhere. This solves both technical issues like latency and also regulatory challenges like data sovereignty. It also offers the benefits of a public cloud service alongside the benefits of a private, local cloud.
Autonomous Things (AuT), also known as the Internet of Autonomous Things (IoAT), are devices that use machine learning and artificial intelligence (AI) algorithms to complete specific tasks.
As social acceptance grows, and so far as regulations and technological progress allow, we will be seeing more autonomous vehicles, drones, robots and the like on the streets.
Autonomous things, which include drones, robots, ships and appliances, exploit AI to perform tasks usually done by humans.
This technology operates on a spectrum of intelligence ranging from semiautonomous to fully autonomous and across a variety of environments including air, sea and land.
While currently autonomous things mainly exist in controlled environments, like in a mine or warehouse, they will eventually evolve to include open public spaces.
Autonomous things will also move from stand-alone to collaborative swarms.
However, autonomous things cannot replace the human brain and operate most effectively with a narrowly defined, well-scoped purpose.
As would be the case of any emerging technology, autonomous technology is also plagued with challenges.
Autonomous Things have a high potential to reduce accidents, improve emergency response times, and offer accurate targeting of enemies in war zones.
However, these technologies are still new and need to be improved before they become practical to be used in our everyday lives.
Shared & Distributed ledger / Immutable & Traceable Ledger / Encryption / Tokenization / Distributed Public
Blockchain technology is a type of distributed ledger, an expanding chronologically ordered list of cryptographically signed, irrevocable transactional records shared by all participants in a network.
A simple analogy for understanding blockchain technology is a Google Doc. When we create a document and share it with a group of people, the document is distributed instead of copied or transferred.
Blockchain also allows parties to trace assets back to their origin, which is beneficial for traditional assets, but also paves the way for other uses such as tracing food-borne illnesses back to the original supplier.
It also allows two or more parties who don’t know each other to safely interact in a digital environment and exchange value without the need for a centralized authority.
The complete blockchain model includes five elements: A shared and distributed ledger, immutable and traceable ledger, encryption, tokenization and a distributed public consensus mechanism.
However, blockchain remains immature for enterprise deployments due to a range of technical issues including poor scalability and interoperability.
Everyone with permissioned access sees the same information, and integration is simplified by having a single shared blockchain. Consensus is handled through more traditional private models.
Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems.
Specific applications of AI include expert systems, natural language processing (NLP), speech recognition and machine vision.
The popularisation of AI and machine learning will bring new challenges to information security since they will considerably increase system vulnerability.
This makes it essential to develop new technologies and profiles to strengthen cybersecurity.
Evolving technologies such as hyper-automation and autonomous things offer transformational opportunities in the business world. However, they also create security vulnerabilities in new potential points of attack. Security teams must address these challenges and be aware of how AI will impact the security space.
The need for security assurance is growing rapidly as business and society increasingly rely on universal connectivity and compute.
As we explore the potential of AI and machine learning to protect systems and networks, security assurance procedures play the important role of verifying security properties of the network platform.
The driving forces in this area include mission-critical use cases and regulatory demands, as well as cloud and edge computing.