Analyzing Gartner’s technology trends for this 2025

For yet another year, the Gartner Group has published its list of the top ten technology trends for 2025, so let’s see which technologies they believe will define this year.

Analyzing Gartner’s technology trends for this 2024

As always, after registering you can download the official Gartner document on technology trends here.

That said, let’s look at what’s new.

Big Picture

The trends are organised into three main groups:

• Imperatives and risks of Artificial Intelligence.
• New frontiers of computing.
• Human-machine synergy.

Themes

We will now analyse each of these themes and their technological trends.

1.- Agentic Artificial Intelligence

With this peculiar name we find those assistants designed for decision making, with the capacity to act independently in order to obtain the objectives that have been specified.

These assistants, or programmes, make use of different Artificial Intelligence techniques, with the capacity to memorise, plan, detect the environment in which they act, use support tools and follow security guidelines in order to achieve their own objectives.

Intelligent agents in Artificial Intelligence are in their early stages, but their evolution is rapid. Understanding this technology and managing its risks is key to preparing the digital ecosystem.

Agentic Artificial Intelligence will boost productivity

Agentic Artificial Intelligence will enable more tasks and workflows to be automated. Software developers will be among the first to benefit, as AI-based coding assistants become more sophisticated.

In addition, intelligent agents will make it easier to manage complex technical projects using natural language. They will also transform decision-making in organisations by analysing data and predicting actions autonomously, even while you sleep.

From assistance to full autonomy

There are two extremes of agentic Artificial Intelligence:

• At one are traditional systems with defined tasks.
• At the other, fully autonomous agents capable of learning, deciding and acting without human intervention.

While there is still a gap between today’s assistants and truly autonomous AI, it will close as better tools are developed to build, govern and trust these solutions.

2.- Artificial Intelligence Governance Platforms

This type of platform will facilitate the management and control of Artificial Intelligence systems, ensuring that their use is ethical and responsible.

They will allow managers and decision-makers to verify that Artificial Intelligence is reliable, acts transparently, is fair and responsible, and that it complies with the ethical and security standards in place. All this will enable Artificial Intelligence to be in line with the organisation’s values and to meet society’s expectations of it.

As Artificial Intelligence becomes more common, ethical risks increase due to lack of alignment with human values, biases, lack of transparency and privacy concerns. To address these challenges, governance platforms have become a key solution.

Why Artificial Intelligence Governance is crucial

Organisations face increasing pressure to regulate and oversee AI due to:

• Stricter global regulations on Artificial Intelligence and data privacy.
• Increased public concern about the risks of Artificial Intelligence.
• The harmful potential of advanced systems capable of generating misleading content.

Large technology companies are already implementing responsible AI principles, backed by multi-billion dollar investments to drive the ethical development of Artificial Intelligence. In addition, more and more organisations are appointing dedicated executives to oversee these systems.

How governance platforms strengthen ethics

These platforms help to automate ethics compliance in Artificial Intelligence through:

• Integrated methods for responsible Artificial Intelligence: With increased transparency to foster trust and informed decision-making.
• Risk Assessments: Identifying biases, privacy violations and negative impacts.
• Model Lifecycle Management: Involving large-scale monitoring and maintenance of ethical models.
• Ongoing Auditing and Monitoring: Ensuring that systems remain aligned with ethical standards.
• Regulatory Compliance: With monitoring of regulations such as GDPR and CCPA to ensure compliance.
• Accountability and Oversight: Enabling various stakeholders to design and test Artificial Intelligence in an ethical manner.

3.- Security against Disinformation

If controlling information is vital, security against disinformation is designed to help identify what is reliable from what is not.

The aim is to create systems that can guarantee that the information consumed is correct, verifying its authenticity and preventing the impersonation that leads to disinformation. It also seeks to monitor the dissemination of content that could be harmful.

Organisations are already suffering the effects of disinformation campaigns and adopting technologies to combat them. Demand is so high that by 2028, it is estimated that 50% of businesses will implement specific disinformation security solutions, compared to less than 5% in 2024.

How and why disinformation happens

Malicious actors have different objectives, among them:

• Polarising Audiences: with manipulated information.
• Stealing Customer Data: through phishing techniques.
• Disrupt Business Operations: with coordinated attacks.

Common methods of disinformation

But how do they try to achieve this? The most common methods are:

• Use of Deepfakes to impersonate identities.
• Spreading Fake News on social networks and fake impersonation sites.
• Massive generation of Disinformation with Generative Artificial Intelligence.
• Highly convincing Phishing to deceive employees and customers.
• Exploitation of Vulnerabilities in collaborative tools and call centres.
• Malware and Credential theft to take control of accounts.

To combat these threats, organisations must adopt holistic strategies that reduce risk and increase transparency.

The future of security against Disinformation

Threats are evolving rapidly, and businesses need to anticipate them with proactive strategies. Adopting anti-disinformation security solutions not only protects the organisation, but also strengthens public trust and ensures secure operations in an increasingly complex digital environment.

4.- Post-quantum Cryptography

Although it sounds a bit futuristic, Post-quantum Cryptography (PQC) refers to cryptographic methods that have been designed to protect against the potential threats posed by quantum computers, which already exist.

Governments are already establishing legal frameworks for organisations to adopt post-quantum cryptography strategies, so it is recommended not to wait for it to become mandatory but to start developing your own now.

Challenges in adopting Post-quantum Cryptography

The transition to quantum-resistant cryptography presents several obstacles:

• Lack of Direct Replacements: As there are no immediate alternatives to current algorithms, which implies a process of discovery, categorisation and re-implementation.
• Performance Differences: As new algorithms require larger keys and ciphers, which can slow down performance and require applications to be rewritten.
• Organisational Ignorance: Many companies do not have visibility into their cryptographic algorithms, key management and secrets.

Unprepared vendors: most have not yet developed upgrade plans.

How to ease the transition

To overcome these challenges, a structured approach based on policies and best practices is needed, such as:

• Establish Clear Policies: Defining rules for algorithm replacement, data retention and cryptographic system updates. This will avoid arbitrary decisions and facilitate management.
• Create a Database of Cryptographic Metadata: Document the algorithms in use to assess the impact of migration, mitigate risks and update incident response plans.
• Consult with Vendors: Ask your technology partners about their plans for adopting post-quantum cryptography and how upgrades will affect systems.
• Adopt an Agile Development approach to Cryptography: By testing and validating new post-quantum algorithms, understanding their performance and security implications. If necessary, upgrade or replace hardware to support these changes.

5.- Invisible Ambient Intelligence

This is the widespread use of small, inexpensive tags and sensors to track the location and state of different elements and environments.

All this information is sent to cloud platforms (such as Onesait Platform) for storage, analysis and subsequent consumption. This type of technology can be integrated into everyday objects in a way that is transparent to the user.

Companies therefore face significant risks if they do not know where their products are or how they are stored. Invisible technology is changing this by providing information about hidden parts of the value chain.

Invisible Technology drives Ambient Intelligence

Advances in networking and electronics have made it possible to monitor previously inaccessible environments. Three key technologies are driving this transformation:

• Very low-power wireless networks: With Bluetooth as the dominant standard, but also Wi-Fi, 5G and emerging technologies such as wireless backscatter could also play a role in the future.
• Energy harvesting: Devices that harness ambient energy sources to enable battery-free tags with a virtually infinite lifetime.
• Low-cost, low-power electronics: Ultra-lightweight chips that can operate on harvested energy, run sensors and send basic messages.

While these technologies can currently transmit only minimal information, in the future more sophisticated devices are expected to be able to run simple algorithms and communicate with each other.

The future of Ambient Intelligence

As these technologies evolve, they will move beyond just tracking products to automated decision-making within supply chains. With the right infrastructure in place, ambient intelligence will become a key pillar for operational efficiency and logistics optimisation.

6.- Energy-efficient computing

Speaking of energy consumption, energy-efficient computing refers to the design and operation of computers, data centres (DPCs) and other digital systems in order to minimise energy consumption and, therefore, the production of their carbon footprint.

IT leaders can try to reduce the carbon footprint of their operations by adopting greener energy sources, modern hardware and better programming practices. However, these efforts alone are not enough to make a significant impact.

To maximise energy efficiency, four progressive strategies are recommended:

1.- Optimising the use of hardware and software.

• Adjust the use of existing hardware to improve its efficiency.
• Optimise algorithms and data structures to reduce energy consumption.
• Use more sustainable energy sources in data centres and servers.

2.- Replacement of inefficient hardware

Replace older equipment with more efficient hardware only when the carbon savings generated offset the environmental cost of manufacturing it.

3.- Re-architecting Applications

Modify code and software platforms to improve efficiency. The use of GPUs or FPGAs instead of general purpose processors for computationally intensive tasks is proposed.

4.- Revolution in IT Infrastructure

It is proposed to adopt new emerging Computational Architectures, such as:

• Neuromorphic Systems, inspired by the human brain.
• Optical Computing, when mature enough for implementation.

The future of sustainability in IT

Adopting these strategies will enable organisations to reduce costs, improve efficiency and move towards a more sustainable infrastructure. Green computing is not only a competitive advantage, but an essential step in mitigating the environmental impact of the technology sector.

7.- Hybrid computing

Hybrid computing is the order of the day, combining different technologies and hardware, such as CPUs, GPUs, edge devices, ASICs, neuromorphic, quantum, photonic systems, etc. All with the aim of solving complex computational problems.

It proposes the use of an orchestration layer that splits workflows across different computing technologies and unifies data into a universal architecture that enables extreme levels of efficiency. This enables applications in scientific simulations, data analysis, machine learning and Artificial Intelligence.

Optimising the computing environment

Orchestration of multiple computing technologies solves problems that are difficult to handle today, such as:

• High Complexity: New models will be able to solve multidimensional optimisation problems with greater scalability, reducing memory and energy usage.
• Interoperability: The future of computing requires multiple interconnected mechanisms. An orchestration layer will facilitate integration between humans and Artificial Intelligence agents, as well as communication between different autonomous systems.
• New Use Cases: The convergence of classical, quantum, neuromorphic and photonic computing will revolutionise sectors such as manufacturing, logistics, financial services, life sciences, materials discovery and drug development.

Embracing the cultural enablers of modern computing

Technological evolution is not about replacing classical computing, but about creating an ecosystem where traditional models coexist with new architectures.

Organisations adopting this vision will be able to optimise their resources, increase efficiency and develop innovative capabilities to address complex problems in an increasingly interconnected world.

8.- Spatial computing

With this type of computing, the physical world is enriched, anchoring the digital content of the physical world and allowing users to interact in an immersive way, enjoying a realistic and intuitive experience.

Spatial computing integrates multiple technologies to create immersive digital experiences in the real world, unlocking both commercial and consumer use cases.

How it works

The technology maps physical spaces (indoors and outdoors), along with the objects and people within them. It then anchors digital content in the real world, enabling seamless and realistic interactions.

Key Technologies that make it possible

• Augmented Reality: Which overlays virtual elements on top of the real world, aligning digital objects with physical ones for interactive experiences.
• Mixed Reality: Merging the real and the virtual, allowing graphical and physical objects to interact in a natural way.
• Metaverse: Connecting digital spaces where users can socialise and create, synchronising their movements and actions in virtual environments.

In addition, spatial computing is supported by embedded technologies such as:

• Eye Tracking: To monitor the user’s gaze.
• Voice Recognition: For spoken commands.
• Motion Sensors and Haptic Controllers: To manipulate virtual objects.
• 5G and 6G Connections: To ensure Real-Time interactions with adequate speed and bandwidth.

The future of spatial computing

As these technologies evolve, spatial computing will revolutionise sectors such as entertainment, education, commerce and industry, enabling unprecedented digital experiences in the physical world.

9.- Multifunctional Robots

The use of Multifunctional Robots to perform multiple tasks, following instructions or examples provided by humans. Such robots will be flexible in both design and operation.

Today, industrial companies rely on nearly four million single-function robots to address labour shortages, reduce costs and increase efficiency. However, the evolution towards multi-functional robots promises a higher return on investment (ROI) and more efficient use of these machines.

Benefits of Multifunctional Robots

• More flexible Workforce: These robots can perform a variety of tasks, allowing companies to better adapt to their operational needs.
• Increased Robot Utility: By performing multiple functions, multifunctional robots are used more frequently and for tasks of higher strategic value.
• Better Human-Machine Collaboration: Unlike single-function robots, which typically operate in confined spaces, multifunctional robots are designed to work in human environments, interacting with tools and spaces designed for people.

The future of Industrial Robotics

As these robots are integrated into factories and work environments, their ability to navigate human spaces and perform more complex tasks will drive innovation in design, shapes and sizes, transforming manufacturing and industrial automation.

10.- Neurological Enhancement

Although it sounds like sci-fi, this refers to augmenting people’s cognitive abilities through technologies that read and decode brain activity and, optionally, write to the brain.

Neurotechnologies are evolving rapidly, enabling organisations to monitor and improve employee performance and customer interaction.

Brain-Machine Interfaces

Brain-Based Machine Interfaces (BBMIs) are at the heart of this transformation. These neural interfaces enable two-way communication between the human brain and a machine through electrostimulation, making it possible:

• Improve Cognitive Skills: such as information processing, memory and learning.
• Extract Brain Data: about thoughts and emotions to personalise experiences.

From wearable devices to brain implants

The applications of this technology vary depending on their level of invasiveness:

• Wearable Devices (headbands, bracelets): low invasiveness, lower functionality, but great potential for mass adoption.
• Neural Implants: high invasiveness, but with greater capacity for cognitive enhancement.

The future of neuroenhancement

As this technology advances, its adoption will bring ethical and regulatory challenges, but also new opportunities to enhance human performance, revolutionising productivity and interaction with technology.

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More information: Gartner Press Release
Header Image: Gartner

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