AI trends to watch for 2025

Attracted by the substantial cost savings for businesses and its capability to streamline many daily tasks for individuals, AI technology is expected to take the world by storm in 2025. Let’s take a look at some of the leading AI trends we expect to see this year.

Generative AI

Since its public release in late 2022, ChatGPT has pushed generative AI into widespread adoption among individuals and businesses. According to a McKinsey report published shortly after ChatGPT's launch, generative AI across its various use cases could contribute an estimated $4.4 trillion annually to global business processes. This significant value is expected to grow further in the coming years.

On the other hand, projections by Statista indicate that the market size for generative AI will reach $62.72 billion by 2025, with market volume anticipated to expand to $356.10 billion by 2030. These figures clearly show the transformative potential of generative AI in reshaping industries and driving economic growth on a global scale.

In the future, we expect gen AI will be able to generate content on the fly during user conversations. It will also create visuals and images during live conversations to enrich users' text/voice conversations with machines. This real-time interaction with people is the primary marker we expect to see in the future.

Personalization of products and services

AI technology will become able to personalize products and services based on user interests or buying habits. For instance, online retailers can show personalized products based on customer search, browsing history, preferences, and needs. Here are the main areas where AI will help personalize products and services:

• Personalize product recommendation: AI technology will analyze previous user purchases, web browsing history, social media interactions, and even chatbot conversations to recommend products and services based on their needs and preferences. Giant tech providers like Amazon and Netflix already use such techniques to increase sales and improve user experience.

• Virtual assistance: Chatbots are increasingly used in customer support to address user inquiries and offer immediate help with common issues (By 2029, the global chatbot market will grow to $46.64 billion). Tailoring responses to individual customer needs can significantly enhance engagement and positively influence purchasing decisions.

• Delivering intelligent content: AI technologies will be used to provide personalized, relevant content based on customer browsing activity and purchase history. For example, a search for computer security books in an online store might trigger the delivery of related emails, newsletters, articles, and blog posts containing links to encourage the purchase of relevant materials matching the customer's interests and needs.

• Target advertisements: AI enhances the precision of targeted advertising by tailoring ad delivery to individual interests and browsing habits. This enables businesses to focus resources on reaching customers with relevant offers, avoiding the waste associated with irrelevant ad displays.

Increased usage of AI in healthcare

In the coming year, we predict a great increase in the deployment of AI in the healthcare sector. Recent research projects that the global market value of AI in healthcare will rise from nearly $27 billion in 2024 to over $613 billion by 2034. Healthcare providers will gain considerable advantages from integrating AI technology into their operations. Key areas of application include:

• Virtual nursing assistance for patients

• Assistance in diagnosing patient health

• Facilitating administrative workflows, such as registering new patients and managing payroll

• Remote patient monitoring, including vital signs such as heart rate, diabetes management, and blood pressure, enabling immediate intervention in emergencies

• AI-driven analysis of medical images to suggest appropriate diagnoses

• AI assistance in surgical operations

• AI-driven predictive analytics to identify high-risk patients

Advancing conversational AI

The future of conversational AI will bring transformative advancements in how users interact with technology. Conversational AI will seamlessly integrate voice and video commands beyond text-based interfaces to enable more natural interactions similar to humans.

For instance, users will be able to engage with virtual assistants by typing queries and speaking instructions or demonstrating tasks via video. This multimodal interaction will cater to diverse user preferences and improve accessibility for individuals with varying needs, such as those with visual or motor impairments.

Increase in incorporating AI into cybersecurity products

AI is already a key component in many cybersecurity solutions, but its future will bring even more advanced and targeted applications. Key areas of development include:

• Enhanced predictive threat intelligence: Future AI-driven security tools will actively identify and prevent attacks before they occur by analyzing real-time patterns and trends to detect emerging threats.

• Automated vulnerability management: AI will conduct continuous scans of IT environments to detect and address vulnerabilities, applying patches and updates proactively to prevent threat actors from exploiting open vulnerabilities.

• Active threat hunting: Future AI solutions will autonomously monitor interactions across the IT infrastructure to identify and halt potential threats immediately. This will effectively reduce the reliance on manual intervention.

• Countering Advanced Persistent Threats (APTs): AI will improve the detection of APTs by analyzing abnormal activities over extended periods and identifying subtle and evasive tactics employed by attackers to surpass traditional security tools.

• Fully automated incident response: Incident response processes will become entirely automated, allowing security teams to prioritize strategic tasks. This will significantly improve response times and minimize the impact of incidents.

• Strengthened authentication methods: AI will enhance biometric authentication, making it more resistant to spoofing attacks. Behavioral-based authentication systems will also improve, leading to recognizing users by their patterns of interaction within IT environments.

Key trends driving the democratization of AI

The democratization of AI tools refers to making AI technologies accessible and usable to a broader audience, regardless of technical expertise or available resources. This trend is gaining momentum and is poised to transform various fields.

AI-as-a-Service (AIaaS)

Leading technology providers such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure offer AI-as-a-Service solutions that provide access to robust AI infrastructure and pre-trained models. These platforms feature intuitive interfaces, which enable users with minimal technical expertise to deploy and benefit from AI technologies. By eliminating complex barriers, AIaaS allows even non-technical individuals or small businesses to leverage the power of AI in domains like customer support, fraud detection, and predictive analytics.

Pre-trained AI Models

More organizations are making pre-trained AI models (e.g., OpenML) available for integration into custom applications. These models enable developers and businesses to create AI-powered solutions without requiring extensive training data or advanced ML expertise. For example, pre-trained models for natural language processing or image recognition can be seamlessly incorporated into software, making advanced AI capabilities widely accessible.

AI learning materials

The availability of comprehensive AI learning materials – including video tutorials, online courses, technical manuals, and articles (such as the one you are reading now) – continues to expand. These resources enable individuals from diverse backgrounds to acquire AI-related skills, from basic usage to advanced implementation. This increases the global AI talent pool, which is capable of driving innovation in AI across multiple industries.

Now that we have shed light on the most prominent AI technologies that we expect to evolve in the coming year, let us discuss the ethical considerations of deploying AI technologies across different industries.

Ethical consideration of using AI technology

Despite AI technology's numerous benefits, it will still encounter various risks and disadvantages. Here are the most prominent ones:

AI bias

Bias can originate from both the AI systems and the datasets on which they are trained. Since AI solutions rely on ML models, the quality of the training data plays a crucial role. If the datasets include biased information, this bias is reflected in the AI system's outcomes. Similarly, the algorithms driving these models may introduce inherent biases during their design or implementation, which lead to inaccurate classifications or wrong decisions.

Privacy issues

ML models are trained using data collected from various sources, such as customer interactions, social media platforms, and datasets acquired from brokers. These sources often contain sensitive information, including personally identifiable information (PII) or protected health information (PHI). Any breach—whether due to cyberattacks or unintentional errors—can expose this sensitive data, leading to severe privacy violations and reputational damage for the organizations involved.

The explainability problem

Most AI solutions are delivered as "black box" systems, meaning users have limited or no insight into how the model arrives at its decisions. This lack of transparency poses significant risks, as it becomes challenging to determine whether an outcome is accurate or influenced by bias.

Replacing human workers

Adopting AI solutions in various industries raises concerns about the potential replacement of human workers. Tasks once performed by people – such as customer service, data analysis, or logistics planning – are increasingly being automated. While this can lead to efficiency gains, it also risks displacing jobs, particularly in roles requiring repetitive workflows.

Decreasing human skills

Over-reliance on AI technology to perform various tasks can lead to a gradual decline in human problem-solving and creative thinking abilities. When workers consistently go to AI for decision-making, troubleshooting, or to innovate new ideas, they may become less willing to engage in critical thinking or develop unique solutions to workplace challenges.