Wednesday, February 1, 2023


- Advertisement --

Predicting the future in the computing industry is even harder and riskier due to dramatic changes in technology and limitless challenges to innovation. Only a small fraction of innovations truly disrupt the state of the art. Some are not practical or cost-effective, some are ahead of their time, and some simply do not have a market. In the modern era, everything around us is changing constantly. With ever-changing IT landscape, 2021 was no different. The year witnessed many disruptive technologies emerge and make headlines worldwide. Technologies like Artificial Intelligence, Machine Learning, Cloud, Blockchain and many more became the brainstorming topics of discussion for boardrooms, whereas 5G was the show stopper the whole year. These technologies will continue to impact businesses in 2022 and will certainly become integral parts of companies’ plans to lead in the future.

The long-term impact of the COVID-19 pandemic is being manifested in a number of ways. The pandemic has been a catalyst in low/no-touch technologies, many of which are now embedded permanently, as is the use of intelligent video to ensure that social distancing and public health guidelines are being adhered to.

In relation to the technology sector, the pandemic also resulted in supply chain issues that have caused many organizations to consider how they create and source key components in their products.

The ‘connected’ nature of everything has meant that the global shortage in semiconductors has been a significant issue in many sectors, from consumer technology to automotive manufacturing. This in turn has led to more organizations – Tesla, Apple and Volkswagen among them – publicly stating a desire to design their own semiconductors, or system-on-a-chip (SoC) (though it should be stressed that designing an SoC and manufacturing it are very separate activities).

While this might represent a trend in some sectors, it is of course something that Axis has been doing for years with ARTPEC, and certainly designing SoCs that are optimized for specific applications is something that we anticipate more organizations doing in the security sector and beyond.

Below are the technology trends that are likely to bring significant innovation and growth in 2022 and in the years to come.

Data Sharing Technology


Thanks to advances in data-sharing technologies, you can buy and sell potentially valuable information assets in highly efficient, cloud-based marketplaces. Combine this data with a new array of privacy-preserving technologies, such as fully homomorphic encryption (FHE) and differential privacy, and you can now share encrypted data and perform computations on it without having to decrypt it first. This provides the best of all potential worlds: sharing data while preserving security and privacy.

All of this has fuelled a promising new trend. Stores of sensitive data lying fallow in servers around the globe due to privacy or regulatory concerns are starting to generate value across enterprises in the form of new business models and opportunities. During the next 18 to 24 months, we expect to see more organizations explore opportunities to create seamless, secure data-sharing capabilities that can help them monetize their own information assets and accomplish business goals using other people’s data.

IT Automating at Scale


Over the last decade, cloud vendors have demonstrated how automating processes that remove repetitive work can help increase overall efficiency. Automated processes are consistent and auditable, which can help reduce errors and improve quality. It can also free skilled tech talent to focus on higher value-added tasks. IT leaders, for various reasons, have been slow to pursue these opportunities. This, however, is beginning to change. In what we recognize as an emerging trend, some CIOs are disrupting their organizations and the army of technologists that currently execute many manual tasks and handoffs across systems, architecture, development, and deployment.



Generative Ai learns about artefacts from data, and generates innovative new creations that are similar to the original but doesn’t repeat it.

Generative AI has the potential to create new forms of creative content, such as video, and accelerate R&D cycles in fields ranging from medicine to product creation. While advances in machine learning, computer vision, chatbots and edge artificial intelligence (AI) drive adoption, it’s these trends that dominate this year’s Hype Cycle. Through the use of natural language processing (NLP) and emerging technologies such as generative AI, knowledge graphs and composite AI, organizations are increasingly using Ai solutions to create new products, improve existing products and grow their customer base. However, the prime focus for organizations is to accelerate the speed at which the proofs of concept (POCs) move into production.

Cloud Goes Vertical


As the global economy moves from a pandemic footing to a more future-focused endemic one, many organizations are looking for opportunities to become more nimble and efficient by offloading business processes to the cloud. In response, cloud giants, software vendors, and system integrators are developing an array of cloud-based solutions, accelerators, and APIs that are preconfigured to support common use cases within industry verticals.These solutions are designed specifically for easy adoption, and can be built upon to create digital differentiation. Clearly, the Cloud goes vertical trend is gaining momentum, so the time to begin exploring its possibilities for your organization is now. You can start by performing an assessment of your business process ecosystem to determine which processes you would consider cloud-sourcing from external vendors, and the pros and cons of doing so. The “big three” cloud services providers—Amazon Web Services (AWS), Google Cloud Platform, and Microsoft Azure—offer cloud-based industry enclaves that automate business processes that are unique to sectors like health care, manufacturing, automotive, retail, and media, among others.



Trendy cryptocurrencies and nonfungible tokens (NFTs) capture media headlines and the public imagination, but these and other blockchain and distributed ledger technologies (DLTs) are also making waves in the enterprise. Much like the TCP/IP protocols that provide underlying support to enterprise network communications, shared ledgers could eventually become an integral, if invisible, foundation of business operations, allowing established industry leaders to expand their portfolios and create new value streams and enabling startups to dream up exciting new business models. Blockchain and DLT platforms have crossed the disillusionment trough of the hype cycle and are well on their way to driving real productivity. They are fundamentally changing the nature of doing business across organizational boundaries and helping companies reimagine how they make and manage identity, data, brand, provenance, professional certifications, copyrights, and other tangible and digital assets. In fact, while companies cancelled purely speculative blockchain projects during the pandemic, they doubled down on those delivering proven benefits.

Drones and Unmanned Aerial Vehicles(UAV)


Aerial drones or Unmanned Aerial Vehicles (UAVs) are an emerging technology with significant market potential. UAVs are used for observation and tactical planning. UAVs may lead to substantial cost savings in monitoring of difficult-to-access infrastructure, spraying fields and performing surveillance in precision agriculture, as well as in deliveries of packages. In some applications, like disaster management, transport of medical supplies, or environmental monitoring, aerial drones may even help save lives. UAVs are classified based on the altitude range, endurance and weight, and support a wide range of applications including military and commercial applications.

Cyber Security Mesh


When COVID-19 accelerated digital business, it also accelerated the trend wherein many digital assets — and individuals — are increasingly located outside of the traditional enterprise infrastructure. In addition, cybersecurity teams are being asked to secure countless forms of digital transformation and other new technologies. This requires security options that are flexible, agile, scalable and composable — those that will enable the organization to move into the future, but in a secure manner. Cyber Security Mesh is a flexible, composable architecture that integrates widely distributed and disparate security services. Cybersecurity mesh enablbest-of-breed, stand-alone security solutions to work together to improve overall security while moving control points closer to the assets they’re designed to protect. It can quickly and reliably verify identity, context and policy adherence across cloud and non-cloud environments. The proliferation cyber-physical system of  — which includes systems that combine the cyber and physical worlds for technologies like autonomous cars or digital twins — represents yet another security risk for organizations, and how threat actors will target these systems is one of our top predictions for the coming years. Security and risk management has become a board-level issue for organizations. The number and sophistication of security breaches is rising, spurring increased legislation to protect consumers and putting security at the forefront of business decisions.

Privacy Enhancing


Privacy enhencing computation secures the processing of personal data in untrusted environments — which is increasingly critical due to evolving privacy and data protection laws as well as growing consumer concerns.Privacy-enhancing computation utilizes a variety of privacy-protection techniques to allow value to be extracted from data while still meeting compliance requirements. As cybersecurity and regulatory compliance become the bigges concerns of corporate boards,  top two  some are adding cybersecurity experts specifically to scrutinize security and risk issues. This is just one of our top 8 security and risk trends, many of which are driven by the recent events such as security breaches and the ongoing COVID-19 pandemic.

Cyber AI


Despite making significant investments in security technologies, organizations continue to struggle with security breaches: Their adversaries are quick to evolve tactics and stay ahead of the technology curve. Humans may soon be overwhelmed by the sheer volume, sophistication, and difficulty of detecting cyberattacks. People are already challenged to efficiently analyze the data flowing into the security operations center (SOC) from across the security tech stack. This doesn’t include the information feeds from network devices, application data, and other inputs across the broader technology stack that are often targets of advanced attackers looking for new vectors or using new malware. And as the enterprise increasingly expands beyond its firewalls, security analysts are charged with protecting a constantly growing attack surface. It’s time to call for AI backup. Cyber AI can be a force multiplier that enables organizations not only to respond faster than attackers can move, but also to anticipate these moves and react to them in advance. Cyber AI technology and tools are in the early stages of adoption; the global market is expected to grow by US$19 billion between 2021 and 2025.

Manage Physical Technology Stack


The explosion of smart devices and increased automation of physical tasks are extending IT’s remit to include device and data management, edge computing, governance, and more. For decades, IT organizations have focused on managing technologies, tools, applications, frameworks, data ecosystems, and other elements of a primarily digital tech stack. Historically, the physical tech stack has been far less dynamic, consisting primarily of employee access points and data center infrastructure. As it moves onto the shop floor and into operations, technology is evolving from business enabler to value driver, becoming the linchpin of the enterprise. Today, the digital capabilities of security, automation, data-driven analytics and decision-making, and artificial intelligence (AI) and machine learning are needed to manage smart devices across the enterprise. Consider, for example, that by 2025, 30% of new industrial control systems will include analytics and AI-edge inference capabilities, up from less than 5% in 2021 or that connected passenger vehicles are expected to generate 10 exabytes of data per month by 2025. From milling machines in manufacturing plants, connected heart monitors in hospitals, and inspection drones for infrastructure, to robot cookers in restaurants, smart sensors in office buildings, and new ‘phygital’ consumer products, a new generation of physical assets is being embedded with advanced digital technologies to enable business-critical functions. IT organizations are increasingly on the hook to manage, monitor, measure, and secure these assets. CIOs must wisely choose technologies based on application, device, and security requirements and consider how they will onboard, manage, and maintain devices and networking technologies that now require the highest levels of uptime and redundancy. They must also rethink device governance and oversight, and reconsider how the technology workforce is organized, defined, managed, and trained.

The Emerging Technology


In the global arena of enterprise technology, optimism rules the roost. We are so enthralled by rapid-fire innovation and the opportunity-laden disruption that follows that we have—with considerable justification—developed an abiding faith in technological progress. Today’s acorns will become tomorrow’s towering oaks, or so the preferred narrative goes. Quantum technologies are poised to transform computing, sensing, and communications within the next decade. Exponential intelligence, the next generation of AI technologies that understand human emotion and intent. Ambient computing, which will make technology ubiquitous in our work and home environments. Quantum computing, while maturing rapidly, remains the focus of several esoteric debates. One is whether Majorana fermions exist. Admittedly, most people don’t have a dog in this fight, but those who do seem ready to rumble. One side believes that Majorana fermion particles—which theoretically contain their own antiparticles—could make remarkably stable quantum qubits. Doubters counter that nobody has been able to find evidence that these particles even exist and, until they do, Majorana’s quantum possibilities remain just that: possibilities.

5G Focused


Some might think we’re behind the curve in highlighting 5G as a ‘trend’ in the surveillance sector given that it’s been high on the agenda for a few years. But we see a fundamental difference between a ‘hype’ and a ‘trend’. For us, a new technology only becomes a trend when we start to see valuable use cases appear in the security and surveillance sector. Though we still think it’s early days, this is starting to happen with 5G.While much of the hype around 5G has been focused on improvements in network performance for consumer applications, one of the more interesting areas is how private 5G networks are emerging as a more compelling use case for the technology.We do feel that private 5G networks show some genuine potential for video surveillance solutions across large or multiple customer sites and could bring particular benefits from a cybersecurity perspective. Certainly, if customers are creating private 5G networks then video surveillance will need to integrate seamlessly. Watch this space.

Hyper Automation


Hyperautomation is the concept of automating everything in an organization that can be automated. Organizations that adopt hyperautomation aim to streamline processes across their business using artificial intelligence (AI), robotic process automation (RPA), and other technologies to run without human intervention. Hyperautomation is an emerging approach to automation, but Gartner has already identified it as one of the top 10 strategic technology trends. They conducted a recent survey which showed that 85% of participants will ‘either increase or sustain their organization’s hyperautomation investments over the next 12 months, and over 56% already have four or more concurrent hyperautomation initiatives.’ According to Gartner, “Hyperautomation is rapidly shifting from an option to a condition of survival, ranking outdated work processes as the No. 1 workforce issue.” It is also important to note that the role that the pandemic has played in the adoption and acceleration of hyperautomation within the market, fuelling the prioritization of digital transformation and automation initiatives over the last year. With the business ecosystem operating in a distributed manner, hyperautomation eases the burden that repetitive processes and legacy infrastructure incur on an organization and its resources. The transformation that hyperautomation affords an organization enables it to operate in a more streamlined manner, often resulting in reduced costs and a stronger competitive position.

AI Engineering


AI engineering automates updates to data, models and applications to streamline AI delivery. Combined with strong AI governance, AI engineering will operationalize the delivery of AI to ensure its ongoing business value. The convergence of several technology trends has enabled AI researchers to achieve breakthroughs and become commercially available. These trends include the increase in computing processing power, the rise of big data and the adoption of cloud-based computing. As artificial intelligence (AI) approaches the tipping point of mainstream adoption by enterprises, AI-related job demand is reaching critical mass. AI is providing us with the next generation of innovation. With AI now being considered the new IT, it is no surprise to see companies making significant investments in bridging the gap between human and artificial intelligence to create new products and services. Artificial intelligence systems and robots were once futuristic and distant realities, but they’re now becoming a part of our everyday lives. A research firm MarketsandMarkets predicts that by 2025, the AI market is projected to become USD 190 billion in industry. Gartner, another research firm, said that AI is bound to create 2.4 million jobs in the near future.

Autonomics System


Autonomic systems are self-managed physical or software systems that learn from their environments and dynamically modify their own algorithms in real-time to optimize their behaviour in complex ecosystems. Autonomic systems create an agile set of technology capabilities that are able to support new requirements and situations, optimize performance and defend against attacks without human intervention. The increasing heterogeneity, dynamism and inter-connectivity in software applications, services and networks led to complex, unmanageable and insecure systems. Coping with such a complexity necessitates to investigate a new paradigm namely Autonomic Computing. Although academic and industry efforts are beginning to proliferate in this research area, there are still lots of open issues that remain to be solved. This paper proposes a categorization of complexity in IT systems and presents an overview of autonomic computing research area. The paper also discusses a summary of the major autonomic computing systems that have been already developed both in academia and industry, and finally outlines the underlying research issues and challenges from a practical as well as a theoretical point of view.

Low-Code and No-Code Tecnology


The scarcity of skilled AI developers or engineers stands as a major barrier to adopting AI technology in many companies. No-code and low-code technologies come to the rescue. These solutions aim to offer simple interfaces, in theory, to develop highly complex AI systems. Today, web design and no-code user interface (UI) tools let users create web pages simply by dragging and dropping graphical elements together. Similarly, no-code AI technology allows developers to create intelligent AI systems by simply merging different ready-made modules and feeding them industrial domain-specific data. Furthermore, NLP, low-code, and no-code technologies will soon enable us to instruct complex machines with our voice or written instructions. These advancements will result in the ‘democratization’ of AI, ML, and data technologies. With the rise and exponential growth of digital technology, most of the daunting tasks reserved for highly skilled professionals have become easily accessible to commoners. The advent of low-code has proven that software development, once a monopoly of skilled software developers, is now in the hands of amateurs to experiment and utilize it for their business growth. The COVID-19 pandemic has expedited the digital transformation of enterprises all over the world. Today, digital transformation is the need of every business, and those who want to succeed in the market competition are getting along with it. Low-code enables many businesses, leaders, and citizen developers to adapt to digital transformation despite limited technical skills and fewer financial resources.

To Sum Up

Technology today is evolving at a rapid pace, enabling faster change and progress, causing an acceleration of the rate of change. However, it is not only technology trends and emerging technologies that are evolving, but a lot more has changed this year due to the outbreak of COVID-19 making IT professionals realize that their role will not stay the same in the contactless world tomorrow. And an IT professional in 2022 will constantly be learning, unlearning, and relearning (out of necessity if not out of desire).

If you have an interesting Article / Report/case study to share, please get in touch with us at , 9811346846/9625243429.

- Advertisement -