Gartner: 10 tech trends you need to know for 2023



According to the Top 10 Strategic Technology Trends published for 2023, CIOs are looking beyond cost savings to new forms of operational excellence, optimizing resiliency, and scaling industry solutions and product delivery. pioneer of new forms of interaction. At the Gartner/Xpo 2022 IT Symposium.

According to Gartner Vice President and Distinguished Analyst Francis Karamuzis, they include multiple forms of wireless, artificial intelligence and resiliency, and external events can affect decisions about them, further complicating matters.

“Depending on which part of the world you are in, there are a lot of issues looming, like a potential economic downturn, supply chain issues, the war in Ukraine and its aftermath, energy-related issues,” Karamoges said in the rice. countryside.

As they focus on further accelerating digital transformation, CIOs need to consider the availability of soon-to-be-implemented and out-of-the-box technologies. Against this background, Gartner’s top 10 strategic technology trends for 2023 look like this:

5G and Wi-Fi 6 and 7
Although no one wireless technology is dominant, enterprises use a variety of wireless solutions to support a variety of environments, including office Wi-Fi, mobile device services, low-power protocols and even wireless, Gartner said. Gartner predicts that by 2025, 60% of businesses will use five or more wireless technologies simultaneously.

“Businesses will see many solutions, including 4G, 5G, LTE, and WIFI 5, 6, 7. All of these will create new data that businesses can use for analytics, and low-power systems will collect it directly from the network, which it means that the network becomes a direct source of business value,” said Karamouzis.

As wireless networks move beyond just connecting, he said, they will also be informed by built-in analytics.

The rise of super apps
According to Gartner, organizations will increasingly adopt new platforms that combine the functionality of multiple applications and services into a single ecosystem (super app).

Super app technology is in the news right now because Elon Musk says he wants to make Twitter the first truly successful super app in North America. Super apps are gaining popularity in Asia thanks to platforms like WeChat, AliPay and Gojek.

“Most examples of super apps are mobile apps, but the concept can also be applied to desktop client apps like Microsoft Teams and Slack. It’s important to note that super apps bring together multiple apps that are used by customers and users. This means we can replace Gartner predicting that by 2027 more than 50% of the world’s population will use multiple super apps every day.

Industry cloud platform
Like super apps, the industry’s leading cloud platforms offer a mix of software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS) with dedicated support. Provides auxiliary functions. industry use cases. Companies can use these bundled capabilities as building blocks to differentiate their digital business initiatives, achieve agility, innovation, and reduce time to market. Gartner predicts that by 2027, more than 50% of companies will use industry-specific cloud platforms to accelerate their business initiatives.

adaptive AI
Gartner previously called AI a strategic technology, but this year we’re seeing a new trend. It is an adaptive artificial intelligence system that constantly renews its intelligence model. These modules can learn new data at runtime and in the application development environment, allowing them to quickly adapt to unforeseen real-world situations.

“Today, if a company has a SaaS application or a new version, they just need to update the software and usually everything goes well, but AI can’t do this, because AI literally learns what to do on the fly,” Karamuzis. said. AI applications use real-time feedback to dynamically change learning and adjust goals.

AI trust, risk and security management
Gartner says that also with regards to AI, many organizations are not prepared to manage the risks of AI. Organizations must implement new capabilities to ensure the reliability, security, and data protection of AI models by implementing trust, risk, and security management.

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