All forecasts suggest that next year, technology will continue to transform not only the business landscape but also the way we interact with the world.
Are businesses prepared to face the challenges and seize the opportunities of this evolving landscape? Let’s explore the 10 technology trends set to define 2025.
The year 2025 promises to be a turning point for technology and business, marked by emerging trends that will redefine how companies operate.
In its latest report, Gartner has identified 10 technological trends that will steer organizations' strategies in 2025.
According to Gartner, these trends offer a roadmap designed to help IT leaders and CIOs make better-informed decisions.
These trends are grouped into three key areas: the challenges and opportunities of artificial intelligence, the new frontiers of computing, and the growing integration between humans and machines. Together, they outline a future where disruptive advances are intertwined with increasingly urgent ethical and social responsibilities.
For instance, artificial intelligence not only aims to optimize processes but also brings critical governance and security challenges, particularly in addressing the rising tide of misinformation.
For businesses, the challenge isn’t just adopting these technologies but understanding how to use them strategically, ethically, and sustainably.
Agentic AI refers to artificial intelligence systems designed to act autonomously, making decisions and executing tasks without direct human intervention.
These systems integrate multiple AI techniques and capabilities such as memory, planning, environmental perception, tool usage, and adherence to safety protocols to operate independently. They can perceive their surroundings, reason, plan actions, and learn from experiences to adapt to new situations.
The ability of Agentic AI to function autonomously or semi-autonomously has the potential to help CIOs realize their vision of generative AI by enhancing productivity across the organization.
AI governance platforms are technological solutions designed to oversee, manage, and ensure the ethical and responsible use of AI systems within organizations.
These platforms provide capabilities for business leaders and users to evaluate, implement, and monitor governance policies, ensuring that AI systems operate transparently, fairly, and in compliance with current regulations.
Artificial intelligence is expanding across an increasing number of sectors, particularly in industries with stricter regulations. However, this growth comes with significant challenges, such as algorithmic bias, privacy concerns, and the rising demand for alignment with ethical principles and human values.
As these technologies become more deeply embedded in society, preventing negative impacts becomes essential. Responsibility in their development and application thus becomes a critical pillar.
Specialized hardware are devices or components engineered to perform specific tasks with greater efficiency than general-purpose hardware. Unlike standard processors designed for a broad range of functions, specialized hardware is fine-tuned for targeted applications, delivering enhanced performance, lower energy consumption, and faster speeds.
For years, cloud computing has been viewed as an almost limitless resource. However, the industry is shifting towards a new era of resource optimization. In this context, hardware—long considered a diminishing strategic priority—is making a powerful comeback.
Specialized hardware is emerging as a critical enabler of growth in three pivotal areas of artificial intelligence: embedded AI and the Internet of Things (IoT), data centers, and advanced robotics.
Small Language Models (SLM) are natural language models specifically designed to operate with limited computational resources. Unlike their larger counterparts, Large Language Models (LLM), SLMs prioritize efficiency in size, speed, and energy consumption without significantly compromising their ability to handle specific natural language processing (NLP) tasks.
Small Language Models are gaining traction in the artificial intelligence landscape for their ability to deliver targeted, cost-effective solutions. Unlike Large Language Models LLMs, which require extensive resources to function, SLMs are purpose-built to tackle specific problems while using fewer data and computational power.
This shift reflects a practical need to optimize existing tasks. For instance, instead of deploying a massive model to summarize reports or manage inventory queries, companies can train an SLM on curated datasets to provide precise, rapid responses, saving both time and resources.
Another key advantage is their ability to run locally, enhancing privacy and reducing reliance on cloud infrastructure. Leading players like Microsoft, Meta, and Mistral are at the forefront of developing smaller, more accessible models, highlighting that efficiency and customization are driving the next phase of AI evolution.
Multimodal models are AI systems capable of processing and generating information across multiple mediums, such as text, images, video, and sound. Unlike traditional models that focus on a single data type, multimodal models integrate various forms of communication to emulate human-like interactions.
The rise of multimodal models is driven by the increasing need for businesses to handle and produce information across multiple formats. Their ability to deliver versatile solutions tailored to diverse media makes them an efficient tool for addressing complex commercial challenges.
The 2024 release of tools like OpenAI’s GPT-4 Omni, Google’s Project Astra, and AWS’s Titan has marked a significant milestone in generative multimodal AI, capturing market attention. These advancements promise AI systems that can be trained once and operate seamlessly across formats, streamlining processes like rapid translations, content creation, and combined data analysis from sensors and images—key for industries like supply chain management.
Edge computing is a computing model that processes data closer to where it is generated or consumed, rather than relying solely on centralized data centers or the cloud.
This approach analyzes and processes data on local devices or nearby nodes, reducing latency, improving efficiency, and optimizing bandwidth usage.
Edge computing is gaining momentum by moving data processing closer to the devices that generate it, decreasing reliance on cloud infrastructure.
The integration of AI into PCs and smartphones—spearheaded by companies like Apple, Microsoft, and HP—is accelerating this shift. Meanwhile, businesses are adopting hybrid strategies to balance costs, performance, and privacy.
Far from being just an emerging trend, edge computing addresses pressing market demands with practical, scalable solutions.
Spatial computing transforms the physical world by integrating digital content directly into real environments, enabling users to interact with it in an immersive, realistic, and natural way.
Spatial computing is gaining momentum thanks to advancements in technologies such as augmented reality (AR), mixed reality (MR), and artificial intelligence. These innovations enable immersive digital experiences in industries like gaming, healthcare, and e-commerce.
The adoption of 5G, along with groundbreaking devices like Apple Vision Pro and Meta Quest 3, is driving consumer interest and opening up opportunities for new business models.
Additionally, with major players like Nvidia and Qualcomm spearheading the development of technological ecosystems, the market is projected to grow from $110 billion in 2023 to $1.7 trillion by 2033.
Hybrid computing integrates multiple technologies—such as CPUs, GPUs, edge devices, ASICs, and neuromorphic, quantum, and photonic systems—to address complex computational challenges.
This approach combines the unique capabilities of each technology within a unified environment, maximizing their strengths and optimizing overall performance.
Hybrid computing enables companies to adopt emerging technologies, such as photonic, bioinformatic, neuromorphic, and quantum systems, to create transformative impacts on their operations.
A prominent example is generative artificial intelligence, which requires advanced computational, networking, and large-scale storage capabilities to solve complex problems efficiently and accurately.
Energy-efficient computing, also known as green computing, focuses on designing, developing, and using computing systems that minimize energy consumption and reduce environmental impact.
This approach aims to optimize the use of technological resources to lower the carbon footprint and promote sustainability in the information technology sector.
Sustainability has become a top priority for corporate boards.
The IT sector has a significant environmental footprint, particularly in industries like financial services and technology, due to the high energy consumption of technologies such as artificial intelligence.
As improvements in traditional processing systems approach their limits, emerging technologies like graphics processing units (GPUs), neuromorphic computing, and quantum computing are expected to deliver substantial advances in energy efficiency over the next five to ten years.
10. Disinformation Security
Disinformation security focuses on ensuring the reliability and authenticity of information circulating in digital environments.
Its primary aim is to develop systems capable of identifying trustworthy content, verifying its accuracy, and preventing attempts at identity fraud or the creation of manipulated information.
This also involves actively monitoring the spread of harmful content to mitigate its impact on society, organizations, and critical processes.
Disinformation has become a digital arms race. Malicious actors are exploiting tactics such as phishing, hacktivism, fake news dissemination, and social engineering to create chaos, instill fear, and perpetrate fraud.
With advancements and the increasing accessibility of technologies like artificial intelligence and deep learning, targeted disinformation attacks against businesses are on the rise. These represent persistent, high-impact threats that require effective management.
The year 2025 is shaping up to be a period of unprecedented technological transformation, where businesses must adapt quickly to an ever-evolving landscape.
The trends identified by Gartner not only provide a glimpse into the future but also emphasize the importance of a strategic and ethical adoption of these innovations.
From agentic artificial intelligence to neurological enhancement, each trend presents both opportunities and challenges that organizations must address with responsibility and foresight.
The key to success will lie in a company’s ability to integrate these technologies in ways that drive efficiency, foster sustainability, and build trust in an increasingly digital world.
By preparing for this future, organizations will not only secure their relevance but also contribute to a more equitable and secure technological ecosystem.