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4 Emerging Technologies for CIOs to Watch in 2023

These four technologies are expected to have significant impact on enterprises in the coming years.

4 Min Read
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CIOs have a responsibility to pay attention to emerging technologies and trends to find opportunities to improve productivity and enhance operational efficiency. Investing in emerging technologies can allow CIOs to create a competitive advantage. Yet while many emerging technologies offer potentially transformational benefits, CIOs must also be aware of the challenges they may pose, such as security and privacy concerns or operational complexity.   

Gartner's latest Hype Cycle for Emerging Technologies has identified a number of disruptive technologies that will affect business and society for the next two through 10 years. CIOs must follow these emerging technologies to determine their impact on organizations, as well as the innovation opportunities they will offer. In partnership with C-suite peers, strategize how to exploit them in line with the organization's ability to handle unproven technologies.

Gartner's 2023 emerging technologies fall into four key themes: human-centric security and privacy, emergent AI, pervasive cloud, and developer experience. Here are four key technologies, encompassing each of these themes, that are expected to have significant impact on enterprises within the next decade:

Generative AI

Generative AI technologies can generate new derived versions of content, strategies, designs, and methods by learning from large repositories or original source content. Generative AI is accelerating due to the popularity of LLMs. Within the next 12 months, most technology products and services will incorporate some form of generative AI capabilities.

New foundation models with varying sizes and capabilities are rapidly coming to market. Machine learning and natural language processing platforms are adding generative AI capabilities for reusability of generative models, making them accessible to AI teams.

Organizations should quantify the advantages and limitations of generative AI and provide generative AI guidelines, as it requires skills, funds, and caution to execute the strategy well. Organizations should also weigh technical capabilities with ethical factors and optimize the cost and efficiency of AI solutions by employing composite AI approaches to combine generative AI with other AI techniques.


AI trust, risk, and security management (AI TRiSM) is a framework that comprises a set of risk and security controls and trust enablers that help enterprises govern and manage AI models and applications' life cycle, as well as accomplish business goals.

Regulations for AI risk management — such as the EU AI Act and other regulatory frameworks in North America, China, and India — are driving businesses to institute measures for managing AI model application risk. Such regulations define new compliance requirements organizations will have to meet on top of existing ones, like those pertaining to privacy protection.

Organizations that do not consistently manage AI risks are exponentially inclined to experience adverse outcomes, such as project failures and breaches. Enterprises interfacing with hosted, large language models (LLMs) are missing native capabilities to automatically filter inputs and outputs — for example, confidential data policy violations or inaccurate information used for decision making. AI models and applications must be constantly monitored to ensure that implementations are compliant, fair, and ethical.

Once models and applications are in production, AI TRiSM becomes more challenging to retrofit to the AI workflow, thus creating inefficiencies and opening the process to potential risks. Setting up an organizational task force or dedicated unit to manage AI TRiSM efforts is an important first step. 

Cloud Sustainability

Increasing attention and focus on environmental and social issues is motivating organizations to improve their sustainability posture. Cloud computing has great potential to improve sustainability outcomes through efficiency of operations and the delivery of cloud-based technology innovations.

Cloud sustainability is the use of cloud services to achieve sustainability benefits within economic, environmental, and social systems. As such, cloud sustainability refers to both the sustainable operation and delivery of cloud services by a cloud provider, as well as the consumption and use of cloud services by organizations and individuals to achieve sustainability outcomes.

Achieving cloud sustainability outcomes is a shared responsibility between the cloud provider and the customer. Cloud providers must demonstrate sustainable cloud operations, and cloud consumers must employ sustainability practices in their use of cloud services. CIOs should engage with relevant executives and other internal stakeholders proactively to create and achieve sustainability goals. They should also establish credible metrics for measuring and reporting cloud sustainability outcomes.

AI-Augmented Software Engineering

AI-augmented software engineering (AIASE) is the use of AI technologies, such as machine learning (ML) and natural language processing (NLP), to help software engineers create, deliver, and maintain applications. This is integrated within engineers' existing tools to provide real-time, intelligent feedback and suggestions.

AI-powered automation enables software engineers to focus their time, energy, and creativity on high-value activities such as feature development. This accelerates application delivery and allocates software engineering capacity to business initiatives with high priority, as well as automatically generates test scenarios that were previously created manually.

Software engineers who fear job obsolescence have shown resistance to AIASE. Because of this, it is important to reassure software engineers that AIASE is an augmentation toolset for human engineers, rather than a replacement.

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