Gartner's 2024 IT Trends Forecast

AITRiSM (Trust, Risk and Security Management) is a strategic approach that combines practices and technologies from AI-augmented testing, chaos engineering, and observability to increase the resilience of products, services, and systems.

AITRiSM has the potential to offer organizations a robust strategy to mitigate potential risks and enhance user experiences. It enables businesses to create resilient systems capable of handling unpredictable scenarios, helping to promote business continuity and stability.

Continuous Threat Exposure Management is a dynamic, real-time approach to identifying and managing potential threats in the digital environment. It focuses on continuous monitoring and rapid response to reduce the risk of exposure.

With the rise in cyber threats, CTEM provides an invaluable tool for organizations to stay ahead of potential vulnerabilities. Its proactive approach allows businesses to respond swiftly to threats, minimizing both damage and downtime.

By integrating CTEM into security strategies, organizations can further safeguard their digital assets, ensuring their operations remain uninterrupted and secure.

Sustainable technology aims to balance economic and social development while reducing environmental and ecological risks. It helps organizations increase the energy efficiency of IT services while enabling enterprise sustainability with analytics, traceability, AI innovations, and emissions management software.

Sustainable technology is now a necessity -- not just an option. By adopting sustainable technology, businesses not only contribute to environmental preservation but also improve efficiency and reduce costs, gaining competitive advantages in the process.

Hardware design and code design can also play a pivotal role in sustainable technology, as efficiently designed hardware components and optimized code can significantly reduce energy consumption, contributing to a sustainable technology ecosystem and further promoting environmental conservation.

Platform engineering focuses on building operating platforms that sit between users and the backing services developers use to accelerate digital delivery and reduce friction.

Platform engineering boosts productivity by providing developers with readily available capabilities. It increases responsiveness and scalability, enables lower operating costs, and empowers developers to deliver more value quickly and efficiently.

The increasing demand for intuitive and problem-solving software signifies the value users place on easy-to-navigate interfaces and solutions that directly address their specific needs and challenges.

AI-augmented development integrates AI tools within a developer's environment to produce application code, translate legacy code to modern languages, enable design-to-code transformation, and enhance application testing capabilities.

Leveraging AI in the development process frees developers to focus on higher-level activities. This trend not only improves developer productivity but also reduces time-to-market, making businesses more competitive and agile.

Industry cloud platforms are solution sets of cloud services tailored to specific industries, offering pre-configured or faster time-to-market and IT agility. These platforms are designed to meet the unique data, process, and security requirements of individual industries while leveraging cloud capabilities.

Industry cloud platforms increase agility, speed-to-innovate, and quick time-to-value for organizations. These platforms enable businesses to tailor their services to industry-specific needs, creating more targeted and effective solutions.

These platforms foster collaboration and streamline workflows, accelerating digital transformation and making organizations more competitive in their respective markets.

Intelligent applications push insights within apps that businesses already use, enhancing data-driven decision-making and improving customer and user experiences. Intelligent applications are designed to improve business processes by automating routine tasks, providing analytics-driven insights, and facilitating more informed, data-driven decision-making within an organization.

By adding predictions or recommendations, intelligent applications tailor the user experience and advance data-driven decision-making. They empower businesses to make smarter decisions and deliver better services. Data becomes exponentially more useful when it's not siloed, and these apps promise to effectively parse more information than current solutions.

AI can enhance applications by incorporating predictive and suggestive features rather than just operational ones, enabling customization based on user preferences. This not only elevates the user experience but also fosters improved results and promotes data-oriented decision-making processes.

Democratized generative AI enables organizations to create new content using AI technologies, making information and skills more accessible and democratized.

It allows AI systems to generate ideas, content, and even solutions, thereby transforming creativity and innovation processes across industries.

Democratized generative AI automates a broad range of tasks, boosts productivity, reduces costs, and creates new growth opportunities. It empowers businesses to leverage AI technologies in creative and innovative ways and allows individuals to reduce their knowledge gaps.

One thing's for certain... AI will continue to make its way through every industry.

Augmented connected workforce strategies enhance the value provided by human staff by establishing a framework that maximizes the utilization of AI, workforce analytics, and skill augmentation. This approach optimizes the efficiency and effectiveness of operations while ensuring seamless collaboration between humans and technology.

This trend accelerates the development of new digital skills required for work and provides digital tools to reduce time to competency for new hires. It enables businesses to create a more connected and efficient workforce and allows individuals to augment existing skillsets.

Machine customers refer to automated systems, powered by AI and machine learning, that make purchasing decisions on behalf of human users. These "buyers" analyze user behaviors, preferences, and contexts to make data-driven buying decisions.

The concept of machine customers opens up new possibilities for companies to engage and serve their customers through connected products.

As these machine customers grow in sophistication and autonomy, the scope of what they can order will expand, potentially encompassing a wide array of goods and services -- from ordering necessary components for a manufacturing process to performing routine purchases for office supplies, scheduling maintenance or repair tasks, or even ordering groceries.