The top strategic technology trends in 2022 are AI engineering, data sharing technologies, cybersecurity mesh, and the adoption of vertical cloud.
Strategic technology trends can be described as high adoption rates, rapid advances, and disruptive impact.
Paying attention to these trends can help reduce costs and automate manual tasks while increasing efficiency and customer satisfaction.
Let’s look at some of the top 2022 technological trends and how they are influencing the market.
What is a strategic technology trend?
A strategic technology trend is a technology adoption that can help a business respond to the changing market faster, optimize operations and make more informed decisions.
Let’s look at some of these trends.
1. No-code platforms
A no-code platform lets businesses build applications quickly using drag and drop features. They reduce a product’s time-to-market, while some have security features like audits and secure API endpoints. No-code platforms are very convenient for small businesses.
Examples of no-code platforms include:
- Appery.io for building mobile and web apps
- Salesforce Lightning – an app development framework
- Webflow – a SaaS web development platform
2. Data sharing made easy
Advancements in data sharing technologies combined with privacy-preserving technologies have made buying valuable information in cloud-based marketplaces safe and efficient.
For example, JoinData software makes data sharing more efficient in the agricultural sector. It allows farmers to choose who gets their data and for what purpose.
3. Cloud goes vertical
A vertical cloud is simply a cloud built according to an industry’s compliance regulations. They allow the business to automate industry-specific manual tasks.
Most specialized industries like healthcare and financial services have diverse compliance rules. So, they need an industry-specific cloud with built-in compliance checks rather than a public cloud.
4. Cyber AI
Companies are turning to cyber AI tools to anticipate and promptly detect cyber-attacks.
Examples of Cyber AI tools include:
- TAA tool (Symantec’s Targeted Attack analytics)
- IBM QRadar Advisor
They train their AI systems to detect bots, ransomware or malware attacks, security breaches, abnormal behaviors, and even predict risks.
5. Internet of behaviors
Internet of behaviors aims to establish when, how, and why human beings use technology to arrive at purchasing decisions. It combines the Internet of Things, behavioral analytics, and edge analytics to improve customer experience.
From a human psychology perspective, it focuses on better understanding and utilizing information to manufacture and promote products.
A great example of Internet behavior in use is the Facebook ads algorithm. Facebook uses the behavioral data of each user to display well-targeted and personalized ads.
6. Everything as a service
Everything as a Service (XaaS) is when a company grants customers the ability to utilize an asset while it still owns, monitors, and manages it.
XaaS includes Software as a Service (e.g., Netflix), Infrastructure as a Service (e.g., Amazon Web Services and Platform as a Service (e.g., Windows Azure).
The as-a-service model is more practical and fiscal. Some industries that have hugely embraced this trend are call centers, banking, and communications.
Gartner top strategic technology trends for 2022
This year’s Gartner technology trends fall under the themes: accelerated growth, engineering trust, and sculpting change.
Let’s check out the trends below.
1. Total Experience
Total Experience (TX) involves caring for the customers, pre-purchase users, and employees. It combines employee experience, user experience, multi-experience, and customer experience.
It encompasses all the elements that make up a customer’s journey with a brand and employee interaction, including the initial product research and discovery, product delivery, customer service and support, and beyond.
TX helps brands better understand what their customers want and empower the employees to give the best service.
You can learn more about the total experience in this post.
2. Data fabric
Data fabric is a data management solution that facilitates information access and sharing within an organization.
It helps different departments access and manage data regardless of where it’s stored. A Data fabric such as Snowflake makes every piece of information accessible when needed.
3. Cloud-native platforms
Cloud-native platforms are software systems designed to take advantage of the distributed, resilient, and agile cloud computing model. Thus, they can easily adapt to the changing digital landscape.
Cloud-native systems can be developed once, tested, deployed, and managed using the same set of tools without disrupting the user experience.
They use technologies such as Application Programming Interface (API), continuous integration (CI), and continuous delivery (CD) toolchains.
4. Cybersecurity mesh
Cybersecurity mesh is a network architecture made up of interconnected nodes that can communicate with each other directly or indirectly. It allows distributed security systems to work together.
Cybersecurity mesh delivers situational awareness based on data from various physical sources, including security cameras and environmental sensors.
It can also analyze network traffic, looking for anomalies or deviations from expected behavior.
To implement a cybersecurity mesh, organizations integrate different security tools such as Firewalls and Access Control to form an interconnected security mesh.
This mesh will use both intelligence and analytics control to reduce cybersecurity risks.
5. Privacy-enhancing computation
Privacy-enhancing computation is a mechanism for allowing multiple parties to compute over shared data without exposing that data to those involved.
A real-life case is when evaluating the creditworthiness of a buyer-seller pair without revealing their individual financial information to each other.
6. Decision intelligence
Decision intelligence refers to technologies and processes used to help organizations make better decisions. It relies on a combination of technology (AI, augmented analytics, and simulations) and human input.
The data-driven insights ensure that decisions are based on the best possible information and consistent, reliable processes.
Recommendation engines are a great example of decision intelligence in action. They analyze and predict products that consumers would most appropriate.
7. Composable applications
Composable applications are a new way of building software.
Rather than writing the entire application from scratch, developers build individual components connected to form an application using the composable approach.
Composable apps make it easier for developers to isolate problems and quickly identify where failures are occurring.
Hyperautomation applies advanced technologies like artificial intelligence (AI) and machine learning (ML) to quickly automate processes (business and IT).
Hyperautomation also refers to the sophistication of automation (i.e., discover, analyze, design, automate, measure, monitor, reassess.)
The idea behind hyperautomation is to use multiple tools, including robotic process automation (RPA) and intelligent business management software (iBPMS).
A great example of hyperautomation is the use of Optical Character Recognition to understand documents.
9. Generative AI
Generative AI is a class of machine learning algorithms that can create content in response to prompts.
A generative AI algorithm might take a word or phrase as input and generate text that responds to it in the simplest form.
More complex AI algorithms can generate images, videos, and audio in reaction to their inputs. Common examples of such algorithms are text-to-image conversions and Face Aging.
10. Distributed enterprises
According to Gartner, Distributed enterprises reflects a remote-first, digital-first model that supports hybrid workplaces and virtual services.
Traditionally, a distributed enterprise means that all parts of the enterprise are independent of each other. The branches of a distributed enterprise may have their accounting and inventory that they don’t share with other units of the same company.
11. Autonomic systems
Autonomic systems are computerized systems that learn from their environment to manage themselves without human intervention.
The goal of creating an autonomic system is to provide a system that can self-manage, self-heal, and even self-configure or self-optimize its performance.
Amelia AIOPS is an example of autonomic solutions that empower machines to self-regulate independently.
12. AI engineering
Artificial intelligence engineering is the application of engineering techniques to build production-ready systems that exhibit intelligent behavior. It combines Machine Learning, Artificial Intelligence, and Expert Systems.
An example of AI engineering is the robots that manufacture self-driving cars.