AI in 2025: Key Trends and Predictions | SCRUMLAUNCH

Just some 2 years ago, ChatGPT ignited an AI revolution that has been growing at an incredible rate ever since and has no sign of stopping. This technology is evolving in terms of its possible applications, affected industries (like education, healthcare, and home use), and the complexity of tasks it can handle, becoming an integral part of everyday life and various professional fields. According to Statista, the AI market size is expected to show an annual growth rate (CAGR 2024-2030) of 28.46%, resulting in a market volume of US$826.70bn by 2030.

With many businesses trying to integrate AI into their operations to remain competitive, along with different professions struggling to learn how to use AI tools to optimize their work, we are all curious to know where AI will be heading in 2025. Will it evolve to the extent of making what seemed like science fiction yesterday become everyday life tomorrow? Will it displace our jobs? Will it replace human creativity? And how do we deal with its ethical, social, and legal impacts?  

Predicting the future, as everyone knows, is complicated, but here are some key trends that can help us answer some of these questions and shed light on what to expect from AI in 2025. 

1. Further AI Evolution

Generative AI models like ChatGPT will continue to evolve and find more applications. Unlike traditional AI, which focuses on data analysis, generative models rely on large databases to recognize patterns and create original content from scratch, whether text, images, code, or audio. And while GPT-4 was a game-changer, the next upgrade is underway. ChatGPT-5 is expected to launch in late 2024 or early 2025, though OpenAI has not yet confirmed an official release date. The next version is expected to offer a significant leap in AI capabilities, including improved multimodal functionality. It will be able to process not just text, but also images, audio, and even video—far surpassing what GPT-4 can do.

Besides, ChatGPT competitors are also keeping pace. Google's Gemini, for instance, has quickly gained traction after its release. You can check Open AI's ChatGPT VS Google's Gemin comparison in our blog. So, investment and research in generative AI continues to surge. While even GPT-5 is not expected to be regarded as Artificial General Intelligence (AGI), the ultimate goal of AI evolution that has human-like reasoning and critical thinking skills, we are getting one step closer to it.

According to Statista, the Generative AI market worldwide is projected to grow by 46.47% (2024-2030), resulting in a market volume of US$356.10bn in 2030. In 2025, it will be able to create even more complex text, images, and videos, significantly changing marketing, design, and content creation approaches. As it evolves, generative AI will become an indispensable assistant in creative processes. We will see AI not only creating content across all media but also automating routine tasks, allowing people to focus on more strategic and creative aspects of their work. For example, AI will be able to help process complex topics in journalism, while, say, in education, it will develop individual curricula.

2. Multimodal AI 

Unlike traditional AI that focuses on just one type of data, multimodal AI can simultaneously handle and analyze different kinds of data like text, images, audio, and video. Multimodal AI is a big step forward in how machines understand and process information. It will be a game changer for tasks like emotional recognition, visual question answering, text-to-image and text-to-audio generation, robotics navigation, video summarizing, and more, providing a more holistic understanding of complex situations. 

The impact of multimodal AI will be especially significant in fields such as healthcare, autonomous driving, and customer service. For example, a multimodal AI system in healthcare can analyze a patient's medical history, MRI images, and speech during consultations to make a more accurate diagnosis and develop a treatment plan. Self-driving cars will rely on multimodal AI to process visual data from cameras and radars alongside data from sensors. This fusion helps the vehicle understand its environment and make safer driving decisions. In customer service, AI will combine text and voice data to better understand customer emotions and provide personalized responses, increasing user satisfaction. 

According to GradViewResearch, the global multimodal AI market size was estimated at USD 1.34 billion in 2023 and is projected to grow at a CAGR of 35.8% from 2024 to 2030. Still, it is worth pointing out that creating effective multimodal AI models is not going to be achieved easily, as it involves processing complex relationships across data types, synchronizing different data formats, and managing vast amounts of data, while the amount of data is not always sufficient for it. 

3. Autonomous AI Agents

Agent-based AI is a new frontier where AI systems are designed to make autonomous decisions based on given goals without constant human intervention. Unlike traditional AI, which follows specific instructions or prompts, AI Agent understands its environment and adapts based on the outcomes it achieves. 

Autonomous AI Agents are changing the game across many industries by taking on tasks that used to need constant human input. For example, in finance, AI Agents can manage investment portfolios, make trades, and adjust strategies based on market trends. In manufacturing, they can handle maintenance, predict breakdowns, and optimize operations to avoid downtime. In healthcare, AI Agents can monitor patients, detect any unusual changes, and alert healthcare workers when needed. These examples show how autonomous AI makes work more efficient across different fields.

In 2025, AI agents are expected to power more complex real-world systems, changing how we approach automation, safety, and efficiency. At first, AI Agents will perform routine tasks. Then, they will be able to monitor various processes and make decisions. Gartner predicts that by 2028, AI agents will perform at least 15 percent of everyday work decisions. It could be seen as an important step towards achieving AGI. However, it also raises complicated questions about the need for AI oversight and accountability. 

4. Responsible AI

Continuing with the topic raised, we're beginning to see AI that can take actions with direct effects in the real world. As it advances quickly, we need to think about its ethical, social, and legal effects to make sure it benefits everyone. In 2025, issues like data privacy, bias, transparency, and accountability will be key for governments and tech companies to tackle.

Besides, with the rise of AI-created fake content and false news, we're entering a "post-truth" era where it's hard to trust what we see. 

This year, laws in the EU and China have been passed in an attempt to limit AI's potential for causing harm. Measures include criminalizing uses such as making deep fakes and regulating their use in sectors like finance and law enforcement. AI legislation also presupposes creating AI systems that humans can monitor or modify if needed. In situations where AI decisions have significant consequences, like legal judgments, medical diagnoses, or financial approvals, it's critical that human operators can intervene and make the final call when necessary. The EU's Artificial Intelligence Act and China's Generative AI Measures focus on "high-risk" AI systems, signaling that responsible AI oversight is already underway.  In the U.S., while federal AI legislation is pending, a recent executive order requires that "the most powerful AI systems" share safety test results with the government. 

Anyway,  in 2025, we’ll probably see a more developed set of rules for AI, ensuring it’s used responsibly and ethically. Organizations may also implement stricter AI ethics guidelines, leading to more transparent AI models and a stronger focus on building fair, less biased algorithms. It will be especially important in areas like loan approvals and criminal justice, where AI decisions need to be clear and unbiased.

5. Computer Vision AI 

Computer vision is a field of AI that enables machines to interpret and understand visual data from the world around them, similar to how humans do. With advanced algorithms, AI can process images and videos to identify patterns, objects, and scenes. With AI technology progressing, computer vision is becoming more widely adopted, driven by demand for greater efficiency and automation.

This technology has many applications across industries as it transforms how machines interact with and respond to visual information. For instance, one of technology's main applications is security and surveillance. Security systems with computer vision can identify unusual activities, recognize faces, and enhance monitoring in sensitive areas. In manufacturing, computer vision is used for quality control, inspecting products for defects, and ensuring they meet specific standards. In healthcare, computer vision can assist in diagnosing diseases by analyzing X-rays, MRIs, and other scans, identifying patterns that may indicate conditions like tumors or fractures. It can also used in environmental monitoring, where AI helps track wildlife or weather conditions.

As computer vision matures, it will significantly increase the level of automation and accuracy across industries. By allowing machines to "see" and understand the world, computer vision creates smarter, more responsive systems that will help businesses and researchers make better decisions using visual data. According to Statista, the market size in computer vision is expected to show an annual growth rate (CAGR 2024-2030) of 10.50%, resulting in a market volume of US$46.96bn by 2030. However, privacy concerns and accuracy issues in facial recognition may impact this growth rate.

6. Human - AI Collaboration

The rise of AI will have social and economic implications, including some jobs displacement. However, while AI may eliminate certain jobs, it can also create new opportunities.  Many roles will emerge for those who can design, implement, and maintain AI systems, along with positions focused on data analysis. But besides these roles, we expect the emergence of roles at the intersection between humans and AI to achieve outcomes neither could accomplish alone. Instead of AI simply taking over human roles, the goal is to create a powerful synergy where AI enhances and complements human abilities. —handling complex calculations, processing large data sets, or generating ideas—while humans provide critical thinking, creativity, ethical judgment, emotional intelligence, and decision-making.

In 2025, human-AI collaboration will likely be the norm in many industries, with AI augmenting technical human abilities while freeing up our time to apply our creative and interpersonal skills to jobs that machines still can't manage. Far from replacing human creativity, AI will act as a collaborator, assisting artists, writers, and musicians in developing new ideas and expanding their creative potential. This dynamic can lead to a more productive and fulfilling work environment.

Obviously, workers will need to acquire new skills and undergo retraining to adapt to changing job roles and requirements. Governments, educational institutions, and employers will have to invest in reskilling and upskilling programs to ensure a smooth transition for workers affected by AI-driven changes in the labor market. 

7. AI models trained specifically for industries

The rapid AI integration across major software tools shows how AI is becoming a core part of business technology, not just an add-on, blurring the line between "AI software" and "non-AI software." As AI matures, we'll likely see progression from general AI integration in software to the development of targeted, high-performance AI solutions for specific industries like healthcare, finance, law, and manufacturing. Unlike general-purpose AI, these specialized models are trained on industry-specific data, making them more accurate, relevant, and efficient. They solve unique problems and help optimize operations specific to certain industry sectors.

For example, in finance, AI models can focus on predicting market trends, assessing risk, detecting fraud, and streamlining operations while enhancing security. In manufacturing, specialized AI will support quality control by inspecting products on assembly lines, identifying defects, and predicting equipment failures before they disrupt production. Transportation and logistics may see increased automation of driving tasks with the development of autonomous vehicles. Healthcare may benefit from AI-powered diagnostics and personalized treatment plans. 

In 2025, we’ll see more AI tools designed for specific industries, helping companies make smarter decisions, automate daily tasks, and find new ways to solve problems. As businesses adopt these tailored AI solutions, productivity will increase, and they’ll be able to offer more specialized services. This move toward industry-specific AI will better address the unique real-world needs of each sector.

Bottom Line

By the end of 2025, AI as technology will be significantly better than it is now: more intelligent, more agentic, more accessible, filling more aspects of daily life, integrated into various industries, capable of doing more things and doing them better. However, it is not going to replace every job. It will enhance jobs and make processes faster, but it'll also create new challenges and jobs. AI will continue to be a tool that augments human capabilities, rather than replacing them entirely. So, instead of fearing AI, get prepared to adapt to it and learn how to collaborate with it to enhance productivity and problem-solving.