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10 Strategic Technology Trends for Enterprises to Explore in 2024

Strategic technology trends

While generative AI and other types of AI have brought new opportunities to enterprises and propelled the development of several trends, adopting a rigorous approach and paying attention to risks are necessary to derive long-term business value from AI. Below are the ten strategic technology trends that enterprises need to explore in 2024.

Important strategic LATEST trends for 2024 include:

Democratized Generative AI

Gen AI

The fusion of large-scale pre-trained models, cloud computing, and open source is driving the democratization of generative artificial intelligence (generative AI), enabling these models to be used by global workers. By 2026, Latest predicts that over 80% of enterprises will use generative AI APIs or models or deploy applications that support generative AI in production environments, compared to less than 5% in early 2023.

Generative AI applications allow enterprise users to access and utilize a wide range of internal and external sources of information, meaning the rapid adoption of generative AI will significantly promote the democratization of enterprise knowledge and skills. Large language models enable enterprises to connect employees with knowledge through rich semantic understanding in a conversational manner.

AI Trust, Risk, and Security Management (AI TRiSM)


The democratization of AI has made the need for AI trust, risk, and security management (TRiSM) more urgent and explicit. Without safeguards, AI models may quickly generate multiple adverse effects beyond control, offsetting all the positive performance and social benefits AI brings. AI TRiSM provides tools for model operations (ModelOps), proactive data protection, AI-specific security, model monitoring (including monitoring of data drift, model drift, and/or unexpected results), and risk control for third-party model and application inputs and outputs.

Latest predicts that by 2026, enterprises implementing AI TRiSM control measures will improve decision accuracy by eliminating up to 80% of errors and illegal information.

AI-Augmented Development

AI-augmented development

AI-augmented development refers to using AI technologies such as generative AI and machine learning to assist software engineers in application design, coding, and testing. AI-assisted software engineering enhances developers’ productivity, enabling development teams to meet the increasing demands for software from business operations. These AI-incorporated development tools reduce the time software engineers spend writing code, allowing them more time for strategic activities such as designing and assembling attractive business applications.

Intelligent Applications

Intelligent applications

In intelligent applications, “intelligence,” as defined by Latest, refers to the adaptive learning ability to autonomously respond appropriately. In many use cases, this intelligence is used to better enhance or automate work. As a foundational capability, intelligence in applications encompasses various AI-based services such as machine learning, vector storage, and connected data. Therefore, intelligent applications can provide continuously adaptive user experiences.

There is currently a clear demand for intelligent applications. In a survey, 26% of CEOs identified talent shortages as the greatest risk to organizational disruption. Attracting and retaining talent is CEOs’ top priority in human resources, and AI is considered the technology with the greatest impact on their industry in the next three years.

Augmented-Connected Workforce

Augmented-connected workforce

The Augmented-Connected Workforce (ACWF) is a strategic approach to optimizing employee value. The demand to accelerate and scale talent is driving the trend of ACWF. ACWF utilizes intelligent applications and workforce analytics to provide daily environments and guidance that empower workforce experience, well-being, and self-skill development. Additionally, ACWF can deliver business outcomes and positive impacts to key stakeholders.

By the end of 2027, 25% of Chief Information Officers (CIOs) will use ACWF programs to reduce the time to competency for critical positions by 50%.

Continuous Threat Exposure Management (CTEM)


Continuous Threat Exposure Management (CTEM) is a pragmatic, systematic approach that enables enterprises to continuously and uniformly assess the accessibility, exposure, and exploitability of digital and physical assets. Adjusting CTEM assessments and remediations based on threat carriers or business initiatives (rather than infrastructure components) not only identifies vulnerabilities but also identifies threats that cannot be patched.

Latest predicts that by 2026, enterprises determining security investment priorities based on CTEM programs will reduce two-thirds of vulnerabilities.

Machine Customers

Machine customers

Machine customers (also known as “customer robots”) are non-human economic entities capable of autonomously negotiating and purchasing goods and services in exchange for compensation. By 2028, there will be 15 billion connected products with the potential to become customers, and this number will increase by several billion in subsequent years. By 2030, this growth trend will generate trillions of dollars in revenue, eventually surpassing the emergence of digital commerce. Strategic considerations include providing opportunities for these algorithms and devices to facilitate and even create new types of customer robots.

Sustainable Technology

Sustainable technology

Sustainable technology is a digital solution framework aimed at achieving environmental, social, and governance (ESG) outcomes that support long-term ecological balance and human rights. The use of technologies such as AI, cryptocurrencies, the Internet of Things, and cloud computing has raised concerns about related energy consumption and environmental impacts. Therefore, improving efficiency, circularity, and sustainability when using IT becomes increasingly important. In fact, by 2027, 25% of CIOs’ personal compensation will be tied to their impact on sustainable technology.

Platform Engineering

Platform engineering

Platform engineering is a discipline that involves building and operating self-service internal development platforms. Each platform is a layer created and maintained by specialized product teams and supports user needs through integration with tools and processes. The goal of platform engineering is to optimize productivity and user experience and accelerate the realization of business value.

Industry Cloud Platforms

Industry cloud platforms

Latest predicts that by 2027, over 70% of enterprises will use Industry Cloud Platforms (ICPs) to accelerate their business plans, compared to less than 15% in 2023. ICPs integrate underlying SaaS, PaaS, and IaaS services into a comprehensive suite of products through composable functionalities, driving industry-specific business outcomes. These functionalities typically include industry data weaving, bundled business function libraries, composite tools, and other platform innovation features. ICPs are tailor-made cloud solutions for specific industries to further meet the needs of enterprise organizations.

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