In 2022, we will keep experiencing accelerated growth of digitization and virtualization of industries and society. Covid-19 will continue to drive change. However, the need for sustainability, ever-increasing data volume, speed of computing networks, etc. can be the leading drivers for a digital revolution.

We will definitely continue to leverage the new openness, agility, flexibility, and innovative thinking. The Gartner tech research and consulting company has released its list of the top emerging technologies to look out for in the upcoming year. They anticipate that by the end of 2025, generative AI will rationalize 10% of all produced data, an increase from less than 1% nowadays. 

Mr. David Groombridge, a Vice President Analyst in the Sourcing, Procurement and Vendor Management at Gartner Research, said that,

“It is an overarching drive for organizations to do more with and scale the digital environments they have been rapidly developing during the pandemic.”

So take a look at which technology will dominate in 2022.

Tech Trends in 2022 and Beyond

|1. Hyper-automation to automate enterprise processes 

Hyper-automation unites different elements of process automation and merging tools & modern technologies to scale automation in the enterprise. 

It has RPA (Robotic Process Automation) in the core and amplifies automation ability with AI, process mining, analytical data, and various latest tools. Along with saving costs, increasing productivity, and efficiency through automation, the hyper-automation also finances the data produced and gathered through digitized methods. Brands can leverage that information for improved and on-time business decisions. 

AI abilities like ML, NLP (Natural Language Processing), Smart OCR (Optical Character Recognition), and AI computer vision will help robots perceive and process more work. 

Automated process discovery tools help you to take a deep dive into the performance of your team. With these details, you can determine things you need to automate. You can engage RPA developers and testers, along with your specific subject experts, BA (Business Analysts), and users. 

Advanced analytics help you measure the ROI of automation and its impact according to business results. 
Gartner anticipates that the global hyper-automation market will reach USD 596.6B by the end of 2022. It also expects that hyper-automation will decrease technology operational expenses by 30% by the end of 2024, along with mitigating complications in technology environments.

|2. Generative AI to produce artificial content 

Generative AI refers to AI and ML algorithms that enable machines to utilize existing text, images, video, and audio files. Generative AI aids systems to determine the relevant pattern to the given input and provide equivalent content. 

Generative AI is availing the canvas and toolkit to intelligent specialists and marketers in a much more extensive and different way than ever before. 

The new deep learning programs become highly sophisticated and successful in generating human-like results in different key areas such as,

✅ Making Quasi-Life like images & Models  ✅ Translation of Languages  ✅ Accurately determining Images  ✅ Sequence prediction & advanced pattern recognition

Avatars of Generative AI offer security for individuals who do not want to reveal their identities while interviewing or working. Generative modeling aids reinforcement ML models to be least biased and understand more abstract theories in simulation and the actual world. 

Gartner anticipates that in the upcoming 3.5 years, generative AI will rationalize 10% of all data generated against less than 1% of nowadays. 

|3. Data Fabric to streamline data integration 

Data fabric is a unified architecture for data management that optimizes distributed data accessibility. It smartly curates and organizes data for self-service delivery. 

Data fabric helps organizations solve complicated data issues by managing their data irrespective of the apps, platforms, and places data get stored. It also maximizes the data value. The primary aim of Data Fabric is to,

✅ Link to any data source through pre-packaged connectors & components that eradicate the coding need.  ✅ Enable data integration and ingestion ability among data sources and apps. ✅ It helps batch, real-time, and big data use cases.  ✅ Manage various environments like on-premises cloud, multi-cloud, and hybrid, as a data source and data consumer.  ✅ Decrease dependency on traditional infrastructures and solutions.  ✅ Offer built-in data quality, data preparation, and management abilities, improved by ML augmented automation to enhance data health. ✅ Enable data sharing with internal/external participants through API.

Data fabric enables business users and data scientists to access reliable data quicker for their apps, AI and ML models, analytics, and business process automation. It enhances decision-making and helps to drive the digital revolution. 

Technical experts can leverage data fabric to streamline data management in difficult hybrid and multi-cloud landscapes with mitigating expenses and risks.

|4. AI engineering to generate more value 

AI engineering is an emerging discipline that uses algorithms, neural networks, computer programing, and various technologies in the development and implementation of AI in real-world environments. 

An AI engineer can obtain data effortlessly from different sources, design algorithms, develop and test ML models, then deliver those models to build AI-powered applications proficient enough to perform complicated tasks. 

The growth in accessibility of computing power and extensive datasets drive the creation of new Artificial Intelligence models and algorithms containing plenty of variables and the ability to make quick and effective decisions. 

AI engineering lets the brands generate hybrid operating environments combining data science, data engineering, and software development. Triumphant AI projects will provide value to the organization and tackle the related business pain points such as internal/external factors, customer affairs, or supply chains. 

Groombridge said that,

“By 2025, the 10 percent of enterprises that establish AI-engineering best practices will generate at least three times more value from their AI efforts than the 90 percent of enterprises that do not.”

|5. Autonomic computing for self-management of software

Autonomic computing is all about self-managing attributes of distributed computing resources. It recognizes and understands uncertain changes in the system, and takes proper actions automatically with zero human intervention.  

The key goal is to drastically reduce the system’s intrinsic complications to make computing highly intuitive and user-friendly for users and operators. Autonomic computing enables systems to self-configure, self-protect, self-heal, and self-optimize. 

Groombridge noted that, 

Autonomic systems with in-built self-learning can dynamically optimize performance, protect [companies] in hostile environments, and make sure that they’re constantly dealing with new challenges.”

Autonomic computing reduces overall maintenance costs as a small team is enough to operate the network. It also boosts the stability of IT systems with automation. Data consolidation is possible to optimize system potentials.  

Applications of autonomic computing comprise, 

✅ Balancing of server load ✅ Allocation of processes ✅ Memory error-correction  ✅ Power supply management  ✅ Software/drivers auto-update ✅ Pre-failure notifications  ✅ Device backup and recovery automation

|6. Decision Intelligence to turn data into improved actions

Decision Intelligence (DI) is an approach to augment data science with social and managerial science as well as decision theory. This mixture is highly effective when it comes to helping individuals use BI data to improve their lives, businesses, and the world around them. 

Gartner anticipates that, within the upcoming two years, one-third of the large-sized businesses will leverage Decision Intelligence for improved and highly structured decision-making.

DI exceeds the old-school BI dashboard and converts AI models into useful recommendations. DI solutions acknowledge and implement business frameworks, measure the effect of competing business purposes, model different scenarios, and suggest the best strategies to address business goals. 

System automation mitigates the dependency of human expertise or judgment by automatically finding connections and patterns in the data. A DI system suggests what actions a decision-maker should perform to address the scenarios discovered in the automated analysis. 

Humans and technology work side by side to decrease the time from data to decision and speed up the ability of decision making.

|7. Blockchain for enhanced security 

Blockchain technology has gained massive acceptance in the past couple of years. It will continue to transform many industries from gaming, governance, FinTech, and many more. 

As per the International Data Corporation, organizations will spend approx $6.6 Billion on blockchain solutions by the end of 2021. It shows 50% of growth from the last year. It is anticipated to rise beyond $15 Billion by the end of 2024.

One of the industries to endure accelerated blockchain adoption is FinTech. The reason is blockchain enables faster cross-border payments, enhanced security, and real-time processing.  

Blockchain stores encrypted data in a hash format and contain the blocks interconnected in the chain. So if anyone wants to access a single block, the entire network gets accessed.  

If anyone does not have the right keys, it is pretty complex or almost impossible to decrypt the data in the blockchain network. That is the reason blockchain technology is highly effective against cyberattacks, malfunctions, and data leakages. 

|8. Cloud-Native Platforms for greater agility, resilience, and portability

Cloud-native is a methodology to develop and run apps that leverage the benefits of the cloud computing delivery model. When companies develop and use apps with a cloud-native structure, it rapidly delivers new ideas to the market and responds quickly to customer needs.

To actually provide automation abilities anywhere, Gartner says organizations must avoid the familiar “lift and shift” migrations and leverage CNPs. CNPs utilize the core abilities of cloud computing to offer scalable and flexible IT abilities “as a service” to technology producers using internet technologies, delivering faster value time and mitigating costs.

That is why Gartner anticipates that CNPs will function as the foundation for 95 percent and more new digital initiatives by the end of 2025. It will increase from less than 40 percent in 2021.

CNPs provide on-demand access to computing power in addition to advanced data and app services for developers. Development of cloud-native integrates various concepts such as DevOps, consecutive delivery, microservices, and containers.

|9. Privacy-enhancing computation for confidential computing

It is a set of technologies that ensures better security and improves the privacy of your confidential data in today’s era. 

PEC enables parties to obtain value from the data and receives actionable outputs without sharing the data with those parties. It is an approach to interact without sharing personal or confidential details with anyone. 

There are various techniques for PEC such as,

✅ Zero-Knowledge Proofs ✅ Multi-Party Computation ✅ Homomorphic Encryption  ✅ Trusted Execution Environments (TEEs) ✅ Federated Analysis

They have differences that make them more/less suitable for various use cases, but all work to achieve the same objective. 

PEC provides a trusted environment where confidential data get analyzed in a decentralized manner. It encrypts the data and algorithms before performing the analysis. Businesses can research safely throughout the regions and with competitors without revealing their confidentiality.  

Gartner speculates that it is a path to manage customer loyalty by mitigating privacy concerns and cybersecurity events. It considers that approx 60 percent of large-sized organizations will use these practices by the end of 2025.

|10. Cybersecurity Mesh for better security infrastructure 

It is a distributed architectural method for robust, adaptable, and reliable cybersecurity monitoring. 

Cybersecurity Mash enables enterprises to create a security perimeter around every user. It lets the users access the assets from any device or place. 

A Cybersecurity Mesh comprises the design and implementation of an IT security structure. It does not emphasize developing a single perimeter surrounded to all devices/nodes of an IT network but rather creates smaller, specific perimeters surrounding every access point. 

Cybersecurity Mesh can create a more scalable, customizable, and flexible approach to network security. It ensures that every node contains its own perimeter. So IT network professionals better manage and monitor individual access levels to various parts of a given network. It also prevents hackers from operating a given node’s infirmity to operate an extensive network. 

According to Gartner, it is a path to mitigate the financial consequences of cyber affairs by 90 percent within just two years.

Summing Up

Anticipating the future of the computing industry is quite complex and speculative because of the vivid changes in technology and unlimited disputes over innovations. Even a tiny change can completely disrupt the industry processes. 

Some are not realistic or budget-friendly, some are before their time, and some don’t have a targeted audience. Various advanced technologies will never get accepted as others appear on time or better in the market. Therefore this post is only an attempt to acknowledge where technologies are going in 2022. 

Source: https://tokei123.org/technology-trends-to-digitize-2022/