Generative AI, Distributed Enterprise, Cybersecurity Mesh to drive tech innovation in 2022: Gartner

Generative artificial intelligence, Data fabric and distributed enterprise – are among the top technology trends that will drive research and innovation in 2022 and a few years after that, according to research firm Gartner Inc.

Cloud Native Platforms, Autonomous Systems, Decision Intelligence, Hyper automation, Privacy-enhancing computation, Cyber security Mesh and AI Engineering are the other top strategic technology trends that will mark innovation in 2022.

“CIOs must find the IT force multipliers to enable growth and innovation, and create scalable, resilient technical foundations whose scalability will free cash for digital investments. These imperatives form the three themes of this year’s trends: engineering trust, sculpting change and accelerating growth,” David Groombridge, research vice president at Gartner, said.

The top strategic technology trends for 2022 are:

Generative Artificial Intelligence

One of the most visible and powerful AI techniques coming to market is generative AI – machine learning methods that learn about content or objects from their data, and use it to generate brand-new, completely original, realistic artefacts.

Generative AI can be used for various activities such as creating software code, facilitating drug development and targeted marketing, but also misused for scams, fraud, political disinformation, forged identities and more.

By 2025, Gartner expects generative AI to account for 10 per cent of all data produced, up from less than 1 per cent today.

Data Fabric

The number of data and application silos has surged in the last decade, while the number of skilled personnel in data and analytics (D&A) teams has either stayed constant or even dropped.

Data fabrics – flexible, resilient integration of data across platforms and business users – have emerged to simplify an organisation’s data integration infrastructure.

A data fabric’s real value is its ability to dynamically improve data usage with its inbuilt analytics, cutting data management efforts by up to 70 per cent and accelerating time to value.

Distributed Enterprise

With the rise in remote and hybrid working patterns, traditional office-centric organisations are evolving into distributed enterprises comprised of geographically dispersed workers.

“This requires CIOs to make major technical and service changes to deliver friction-less work experiences, but there is another side to this coin: the impact on business models,” Groombridge said.

“For every organisation, from retail to education, their delivery model has to be reconfigured to embrace distributed services. The world didn’t think they’d be trying on clothes in a digital dressing room two years ago,” he said.

Gartner expects that by 2023, 75 per cent of organisations that exploit distributed enterprise benefits will realise revenue growth 25 per cent faster than competitors.

Cloud-Native Platforms

To truly deliver digital capabilities anywhere and everywhere, enterprises must turn away from the familiar “lift and shift” migrations and toward CNPs.

CNPs use the core capabilities of cloud computing to provide scalable and elastic IT-related capabilities “as a service” to technology creators using internet technologies, delivering faster time to value and reduced costs.

For this reason, Gartner predicts that cloud-native platforms will serve as the foundation for more than 95 per cent of new digital initiatives by 2025 — up from less than 40 per cent in 2021.

Autonomic Systems

As enterprises grow, traditional programming or simple automation will not scale. Autonomic systems are self-managing physical or software systems that learn from their environments. Unlike automated or even autonomous systems, autonomic systems can dynamically modify their algorithms without an external software update, enabling them to rapidly adapt to new conditions in the field, much like humans can.

Decision Intelligence

An organisation’s decision-making competency can be a significant source of competitive advantage, but it’s becoming more demanding.

Decision intelligence is a practical discipline used to improve decision making by explicitly understanding and engineering how decisions are made, and outcomes evaluated, managed and improved by feedback.

Gartner predicts that in the next two years, a third of large organisations will be using decision intelligence for structured decision-making to improve competitive advantage.

Hyper-automation

Hyper-automation enables accelerated growth and business resilience by rapidly identifying, vetting and automating as many processes as possible.

“Gartner research shows that the top-performing hyper-automation teams focus on three key priorities: improving the quality of work, speeding up business processes, and enhancing the agility of decision-making,” Groombridge said.

Privacy-Enhancing Computation

As well as dealing with maturing international privacy and data protection legislation, CIOs must avoid any loss of customer trust resulting from privacy incidents. Therefore, Gartner expects 60 per cent of large organisations to use one or more privacy-enhancing computation techniques by 2025.

PEC techniques, which protect personal and sensitive information at a data, software or hardware level, securely share, pool and analyse data without compromising confidentiality or privacy.

Cybersecurity Mesh

“Data is strung throughout many of this year’s trends, but it is only useful if enterprises can trust it,” Groombridge said. “Today, assets and users can be anywhere, meaning the traditional security perimeter is gone. This requires a cybersecurity mesh architecture (CSMA),” he observed.

CSMA helps provide an integrated security structure and posture to secure all assets, regardless of location. By 2024, organisations adopting a CSMA to integrate security tools to work as a cooperative ecosystem will reduce the financial impact of individual security incidents by an average of 90 per cent.

AI Engineering

IT leaders struggle to integrate AI within applications, wasting time and money on AI projects that are never put in production, or struggling to retain value from AI solutions once released. AI engineering is an integrated approach for operationalising AI models.

“For fusion teams working on AI, the real differentiator for their organisations will lie in their ability to continually enhance value through rapid AI change,” Groombridge said.

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

COMMENT NOW