Gartner: 12 strategic technology trends for executive leaders in 2022

With the past two years seeing rapid technological change, Arun Chandrasekaran, research vice president analyst at Gartner, outlines the 12 technology trends executive leaders should consider in 2022 to ensure growth, greater digitalisation and operational efficiency across their organisation.


The last two years triggered faster technology change than ever before, and this pace is only set to increase. In this world where change is the norm, executive leaders’ priorities have become clear – they want to achieve growth, greater digitalisation and operational efficiency. To achieve their goals, they can consider these 12 strategic technology trends.

  1. Generative AI

Generative AI enables businesses to reshape research & development (R&D) across customer experience, new product development, software engineering and data science. Executive leaders should consider piloting generative AI use cases to accelerate R&D content personalisation and evaluate its use to create synthetic data when handling large, sensitive data volumes in the cloud or with partners.

  1. Autonomic systems

Autonomic systems facilitate adaptability to market changes, excelling where conventional automation might be inadequate. In business use, autonomic technologies can be implemented to deliver agility and performance benefits in software or physical systems.

  1. Total experience

Total experience allows organisations to achieve resilient business outcomes and simultaneously increase revenue from customers and reduce internal costs. To enhance experiences for customers and employees, invest in multi-experience technologies to improve experiences across channels, devices, touchpoints, and interaction modalities.

  1. Distributed enterprise

There is an ongoing shift in business models, with the move to the hybrid working requiring businesses to expand digital service models to combine virtual and physical offerings, increase employee flexibility and achieve cost savings. To address this, create fusion teams of IT and business technologists to accelerate hybrid business models. These can reduce employee fatigue and burnout by deploying new collaboration tools and redesigning workspaces.

  1. AI engineering

AI engineering can scale a few AI models in production to hundreds, realising significant value with each model deployed. Including AI engineering practices as a core part of AI strategy is key to reducing failures and industrialising AI initiatives, and can be accelerated by partnering with high-performance teams from the start and by filling critical roles for AI.

  1. Hyper-automation

Hyper-automation enables organisations to design and implement business model change for a variety of processes and business functions, driving competitive advantage, reducing technical debt, and enhancing business agility. To maximise success, plan and create multiple initiatives, using fusion teams throughout the iterative process of designing, building, scaling, and governing the hyper-automation roadmap.

  1. Decision intelligence

Decision intelligence allows organisations to become disruption-ready and resilient by identifying, prioritising, modelling and (re)engineering decisions for improvement – enabling them to augment and automate a combination of human workers and techniques. Using decision intelligence to model decisions – and developing practices that incorporate both human and AI decision-making capabilities – will improve business-critical decision making with more data-driven support or AI-powered augmentation.

  1. Composable applications

Composable applications create agility and enable safer, faster, and more efficient change as well as helping re-engineer how organisations make the most complex and pressing business decisions. Think about setting up business-IT fusion teams, equipping them with dedicated design tools, and championing composable architectural principles in all new technology initiatives to develop a roadmap for building them.

  1. Cloud-Native platforms

Cloud-native platforms help traditional organisations that lack digital talent and expertise, build agile applications and succeed more quickly with their digital initiatives. As a result, it increases productivity and improves efficiency. First, choose cloud-native platforms for new initiatives, and then expand the scope of digital workloads that can benefit from cloud-native technologies.

  1. Privacy-enhancing computation (PEC)

By using various privacy-enhancing computation (PEC) techniques, organisations can ensure confidentiality, allowing them to gain and use the information without exposing identifiable data. To implement this, identify where your organisation wants to use personal data in untrusted environments for analytics and business intelligence purposes and apply PEC techniques.

  1. Cybersecurity mesh architecture (CSMA)

Cybersecurity mesh architecture (CSMA) helps provide a common, integrated security structure to secure all assets, whether on-premises, in data centres or on the cloud and fosters a more-consistent security posture. Introducing a robust defensive posture by eliminating silos and inefficiencies will help to combat the increase in security complexity by better integrating the security infrastructure.

  1. Data fabric

Data fabric ensures lessons that are learned from interactions with data can be reused for other operational systems and back-office and analytics solutions - quadrupling the output from human-driven efforts. Proactively and continually instil enterprise-wide data-sharing capabilities throughout your organisation – including people, processes, and technologies. To achieve this, conduct a pilot focusing on one data product or domain-oriented prototype which integrates data from a minimum of three sources and vet its fit within an organisation.

Arun Chandrasekaran is a research vice president analyst at Gartner. His research focuses on providing strategic advice to CTOs and CIOs on how to spur technology innovation within enterprise IT.