AI Trends 2025 or How to Stop Worrying and Get Timeless Benefits From AI

Often characterized as an evolving or emerging technology, AI is under intense scrutiny from potential adopters and business leaders. Every new development and shift is dissected, reviewed, and examined for next-level value. As a result, artificial intelligence is frequently surrounded by marketing buzz, which can distort perceptions and expectations among executives and stakeholders. 

How can decision-makers cut through the noise and stay informed about meaningful changes in AI? In this article, we provide a detailed outlook on key industry trends and the future of artificial intelligence. 

AI market trends: why is it too early for AGI?

Although there are many enthusiastic predictions about the imminent arrival of Artificial General Intelligence (AGI) and the subsequent achievement of technological singularity, these claims should be taken with a grain of salt for several reasons. 

• Insufficient resources
  Existing AI models consume immense amounts of resources and energy. According to the Association of Data Scientists, the energy required to train ChatGPT-3 is equivalent to what an average American household consumes over 120 years. Given that AGI is expected to match human-level decision-making and critical thinking, it will likely require even more parameters than ChatGPT-3’s 175 billion. To meet such demands, vendors will either need to develop new hardware and energy sources capable of supporting these requirements or find ways to significantly reduce energy consumption. Neither scenario appears entirely feasible in the near future.
  
  
  
• Unresolved AI issues
  Building a perfect artificial general intelligence (AGI) presupposes a perfect understanding of artificial intelligence itself—yet that understanding is still a work in progress. For instance, while newer AI models show a modest 3% reduction in hallucinations, the issue remains unresolved. There is still no clear understanding of why, how, or when AI models hallucinate, which poses reputational and operational risks for organizations investing in generative AI. Until these models are fully understood and there is complete transparency into their inner workings, the path toward AGI will remain riddled with blind spots and unknowns.
  
  
  
• Lack of certain purpose
  The question is not whether AGI is possible, but rather what purpose it is meant to serve. While the idea of transcending current technological limits is inspiring, the practical applications of AGI remain vague. In theory, AGI is expected to handle all intellectual tasks currently performed by humans. But what would such a synergy look like in reality—especially amid ongoing concerns and skepticism surrounding the current use of AI and generative AI? There is no clear vision for this future, which makes the potential value and return on investment in AGI highly uncertain. 
  
  

Ultimately, the greatest challenge in realizing AGI lies in capturing the vast complexity and limitless pathways of the human mind within a finite, constrained framework. According to researchers at Radboud University, replicating the intricate logic and neural pathways of the human brain may be impossible—even under ideal development conditions. While it may be technically feasible to build a self-learning system, can the same be said for a system that forms deep associations with its own memories and effortlessly recalls critical information? 

As it stands, too many unknown variables continue to obstruct the path to AGI—and resolving them is likely to take far longer than current projections suggest. 

AI and machine learning trends

One of the trickiest aspects of following AI trends is that artificial intelligence comes in various forms—natural language processing (NLP), large language models (LLMs), machine learning (ML), and more. Each type excels at specific categories of tasks. As a result, the key to effectively navigating AI trends in healthcare, BFSI, or any other sector lies in understanding what changes your enterprise needs to make in order to benefit from GenAI or an ML model. That’s why it’s essential to explore AI trends that influence both employee development and business models.

Dmytro Romenskyi, BSA at Trinetix 

The prevailing sentiment around AI industry trends is that progress is slowing. While some view this as anti-climactic or disappointing, others see it as a natural phase of development, influenced by factors such as limited access to fresh data and the constraints of current computing power. At its core, artificial intelligence is not a sci-fi miracle brought to life, but a tool that operates within the boundaries of existing hardware. Today’s processors, memory, and energy resources are simply not sufficient to meet all the expectations of AI vendors and stakeholders. 

That said, the advancements already achieved have undeniably reshaped the world. Industries and businesses now operate in a new reality—one where traditional processes can be transformed into streamlined workflows, and data can be reimagined as a powerful strategic asset. But where does this leave teams and enterprises? How have these changes influenced their work and approaches? 

To answer these questions, it's necessary to break down the latest AI trends and examine their real-world impact. 

AI Trends: With agentic AI comes workforce reskilling

By Q4 of 2024, approximately 51% of organizations worldwide were exploring opportunities to adopt AI agents, while 12% had already implemented them at scale. 

This trend has significantly impacted the global workforce. Previously, only about 5% of employees required reskilling each year. However, with the rise of AI—particularly agentic AI—that figure surged to 35%. 

The reason behind this rapid shift lies in the transformative capabilities of artificial intelligence. Rather than simply automating isolated tasks, AI opens the door to deeper operational changes and performance enhancements. But to fully capitalize on these opportunities, employees must adapt to the new workflows introduced by agentic AI. This adaptation demands the acquisition of new skills and familiarity with emerging tools. 

A key pattern emerging from recent AI trends is that executives are beginning to recognize where AI delivers the most value: by helping employees optimize their tasks and workflows. Many roles still rely heavily on critical thinking and decision-making—areas where AI falls short. However, these same roles often involve processing large volumes of data that are impractical to handle manually. The same applies to complex enterprise interactions. AI helps bridge these gaps, enabling teams to focus on high-value work rather than repetitive, monotonous tasks.

Dmytro Romenskyi, BSA at Trinetix 

To better understand the growing need for workforce reskilling, it’s helpful to explore the advantages that conversational AI trends such as agentic AI bring to the table. While the full potential of this technology is yet to unfold in the coming years, several current features already make AI agents powerful tools for enhancing enterprise operations: 

• Information retrieval
  What makes AI agents one of the most impactful AI market trends is their transformative approach to data retrieval. Rather than relying on traditional, linear search methods, intelligent agents for information retrieval operate as a coordinated system of multiple agents. These agents work together to rapidly extract insights from vast volumes of data, delivering responses that are not only faster but also more accurate and contextually relevant. 

Within such a multi-agent system, agentic AI significantly streamlines the process of information retrieval—making it faster and more efficient. This is especially critical for organizations that generate and manage large volumes of data on a daily basis. By automating and optimizing how information is accessed and filtered, agentic AI enables teams to make quicker, more informed decisions without being overwhelmed by data overload. 

• Knowledge collection
  Executing tasks within an agentic AI system relies heavily on access to vast repositories of enterprise knowledge—and this is precisely where knowledge-based agents come into play. These agents are responsible for supplying other AI agents with the accurate, context-specific information needed for reasoning and decision-making, thereby enabling intelligent and autonomous behavior. Knowledge-based agents maintain and update the knowledge base with new information, consult it to determine the most appropriate course of action, and act in alignment with the insights and recommendations it provides.  

• Dynamic workflow execution
  What sets agentic AI apart from other AI trends is its ability to take action and perform tasks independently. This capability is driven by dynamic agents, which can devise problem-solving strategies, adapt based on feedback, generate new solutions to challenges, and utilize tools to complete their tasks. Such versatility makes agentic AI especially valuable for managing a wide range of organizational tasks that don’t require human intervention or critical thinking. 
  
  
  
• Employee assistance
  Personalization is no longer solely a customer prerogative. Enterprise workers also benefit significantly from virtual assistants tailored to their specific tasks. These assistants help employees quickly get up to speed on the latest enterprise changes, recent project updates, and new policies. Agentic AI excels in this role, evolving from a simple conversational bot into a more interactive and responsive assistant. It can provide employees with essential information, direct them to the appropriate department or contact for problem-solving, and even send messages and reminders on their behalf. 

Enterprise communication has always been a challenge. The more departments and larger the teams, the harder it becomes to keep everyone connected. Frequent meetings can complicate matters further, as employees often have packed schedules, leading to missed valuable information or delayed feedback. AI agents can help solve this issue by keeping everyone informed, summarizing key takeaways, and delivering timely feedback and suggestions.

Dmytro Romenskyi, BSA at Trinetix 

With all the capabilities of agentic AI in mind, the next question is: what skills do enterprise employees need to develop or enhance? 

Broadly, these skills can be grouped into three categories: business skills, agent skills, and human skills. 

Business skills

Business skills focus on transforming AI capabilities into value drivers. On its own, agentic AI can’t solve enterprise challenges or deliver solutions—but it can empower employees to broaden their search and explore deeper layers of data to uncover relevant insights. 

To leverage this potential, employees must learn how to interpret data effectively, use AI-powered tools for data storytelling, and integrate AI assistance into their problem-solving and brainstorming processes. 

This shift requires a new mindset: employees need to view AI as a collaborative partner in analytical thinking and enterprise operations. 

 Equally important is strengthening critical thinking skills. While it may be tempting to delegate complex or repetitive tasks to AI agents, human judgment and perspective remain essential for organizing processes and addressing nuanced issues. 

AI and generative AI are powerful and convenient tools—but they’re not flawless. They can be prone to bias and are capable of making mistakes. Despite the common belief that AI can operate independently, it delivers the best results when guided by human oversight. Employees working with AI should consistently review its outputs to identify any discrepancies and provide immediate feedback. This not only ensures accuracy but also contributes to the ongoing improvement of the model.

Dmytro Romenskyi, BSA at Trinetix 

It’s important to remember that the primary goal of AI and generative AI is to help teams and employees optimize their time and make the most of their enterprise data. Ultimately, it’s human workers who remain in control—deciding how to interpret, apply, and leverage that data to drive meaningful outcomes. 

Agent skills

Agent skills include all the competencies required for effective collaboration between AI agents and enterprise teams. One of the most critical among these is AI literacy—a deep understanding of how AI works, how to interact with it effectively, and an awareness of its limitations and ethical considerations.  

92% marketing leaders believe AI literacy to be an obligatory skill in 2-4 years 

46% of organization leaders report offering mature AI upskilling  

48% of employees believe training to be the most vital aspect of AI adoption. 

What makes AI literacy a must-have skill for enterprise employees is that it goes beyond just understanding the technology. It brings the concept of AI into a realistic and explainable context—educating employees on the processes that influence AI outputs and helping them recognize which tasks AI can and can’t perform.

As a result, AI literacy plays a crucial role in reducing organizational resistance, enhancing security and reliability, and promoting effective, responsible use of AI across all departments. 

Human skills

Technology transforms everything—including people. As AI becomes integrated into enterprise processes, it often brings entirely new workflows and routines. Employees must be prepared for these shifts by adapting their skill sets and acquiring new knowledge. In this context, enhancing employee agility becomes a top priority. Enterprise leaders need to trust in their teams’ ability to adapt quickly and navigate change with confidence. Even if leaders believe their employee management strategies are ready for transformation, proactively assessing and strengthening that preparedness can ultimately save time and resources. 

Additionally, enterprise leaders and executives must actively promote data literacy across all departments to stay aligned with the evolving AI landscape. In the age of AI, the ability to interpret and analyze data—and transform it into compelling narratives—is a fundamental skill for employees across every function. To support this, leaders should implement a robust, AI-ready data strategy and ensure that all employees are informed and engaged in the process. 

Finally, one of the most essential skills for employees at every level is accountability. While AI models can answer questions and perform tasks, they are not responsible for the consequences of their outputs. Ultimately, it is human workers and enterprise representatives who are accountable for decisions and the organization’s reputation. That’s why it’s critical for enterprises to understand when and how to use AI responsibly—and to have clear protocols in place for managing potential risks or negative outcomes. 

AI Trends: Emergence of federated learning

Expected to go from $132.5 million in 2024 to $352.9 million in 2033, federated learning market is quickly growing in size. As a result, federated learning is currently among the most discussed AI market trends.  

The reason behind this surge is that federated learning represents a major breakthrough in AI model training—shifting from centralized data processing to collaborative learning across decentralized servers.  
 
With federated learning, each data source within a network can contribute to training an AI model without transferring its local data outside its environment. This approach eliminates the need for data centralization and sharing, making federated learning highly advantageous. 

At the core of federated learning lies collaborative model training that is executed in several key stages:  

The improvements federated learning injects in data privacy compliance, data security, and accuracy also make it one of the most prominent AI trends in healthcare as well as other sectors. 

Healthcare

Healthcare is one of the industries where private data safety and data accuracy are of uttermost importance, which makes federated learning a powerful player in digital healthcare transformation. In addition to multiple research papers and studies evaluating the impact of federated learning on AI adoption across the industry, there are real-life success stories of federated learning solutions and their positive effect on healthcare operations.  

Google Cloud cites the case of Kakao Healthcare, which developed a platform for consistent data standardization. On this platform, diverse anonymized medical data (blood pressure measurements, body temperature, MRI image descriptions) is systematized and available to other hospitals for their various objectives. The distinguishing feature of the platform is that it translates such data into appropriate datasets without disturbing the existing environment of the hospital. This case serves as a good example of how federated learning accelerates classification of medical data and empowers hospitals to build a robust and more advanced data management strategy.  

Barring data organization, federated learning ignites other equally positive changes in healthcare: 

Finance

As the BFSI sector continuously seeks solutions to enhance security and strengthen anti-fraud measures, it sees great potential in federated learning. By enabling data processing without centralizing sensitive information, federated learning significantly reduces the attack surface for hackers, making it a promising approach to safeguarding financial data.

This potential has attracted the attention of SWIFT, which is currently collaborating with Google Cloud to develop a solution capable of learning from historical fraud cases in order to quickly detect new schemes and criminal activities.

By leveraging collaborative learning and transforming diverse experiences into actionable insights, this platform enhances fraud prevention efforts across financial institutions worldwide.

In addition to fraud detection, federated learning also enables the BFSI sector to improve the following areas: 

Despite its data security advantages, federated learning is not foolproof. Cybercriminals may still be able to reconstruct data sent to the global server—especially when it arrives in small batches or if they manage to intercept traffic from local servers. Preventing such scenarios requires strict control over how federated learning is implemented, along with sufficient bandwidth to support secure operations. Therefore, while federated learning holds great promise, there are still challenges to overcome—challenges that will benefit from the insights and experiences of users actively engaged in its development and deployment.

AI Trends: SLMs as a new business norm

Amidst the options for addressing AI energy and resource consumption issues, Small Language Models (SLMs) have made it to the top AI trends as more streamlined and specialized AI systems.  

Similarly to LLMs, SLMs perform tasks and interact with users, but they use only 30 billion parameters, while LLMs can have above 175 billion parameters. However, such limited range makes SLMs more beneficial to enterprises.  

• Greater speed
  Fewer parameters enable SLMs to generate responses much faster than LLMs, which makes them a great fit for intense and dynamic enterprise environments that rely on instant feedback. For example, SLMs are highly useful for customer and user support as they can generate detailed and accurate replies instantaneously, maintaining high engagement and satisfaction rates.  
  
  
  
• Task-specific optimization
  Limited parameters enable greater focus on specific domain. Due to this, SLMs don’t provide general-purpose knowledge. Instead, they offer niche insights and commit to a limited range of tasks that is relevant to a particular enterprise department. As a result, SLMs offer more accuracy and efficiency to teams and employees working on niche tasks and processes.  
  
  
  
• Data security
  The compact architecture of SLMs and their focus on specific datasets allows for a more narrowed training that doesn’t involve sensitive business data, which considerably reduces risk of data leaks and all the harm they entail. Also, due to the narrow scope, SLMs are less prone to hallucinations, basing their responses only on the limited range of specially curated databases. 
  
  
  
• Cost efficiency
  Smaller processing power allows SLMs to consume fewer resources, making them a more lightweight and considerably less costly alternative to LLMs. This factor is particularly valuable for enterprises that seek to integrate AI into their processes but are concerned by price/effectiveness ratio.  

The advantages mentioned above placed SMLs among the rising AI investment trends as such organizations as UNESCO, Google, and Microsoft promoting and leveraging the technology. Such a large-scale interest confirm’s Garner’s prediction on SMLs gaining major traction across the enterprises by 2027. 

There is a common saying: less is more. In the context of AI models, it’s particularly truthful. You don’t always need an AI model that knows everything about everything. You need a model that can help you with your specific tasks, leveraging specific enterprise knowledge. SLMs fit right in.

Dmytro Romenskyi, BSA at Trinetix 

AI Trends: Business model transformation

One of the AI industry trends that affect organizations worldwide is the imminent change of business models. The possibilities for new services and products are at enterprise leaders’ fingertips – and yet, obsolete and rigid business models are holding them back from growth.  

Fortunately, leaders acknowledge these barriers, with 62% of CEOs admitting the necessity for rethinking their playbook. They expect AI to support them in that transition as 89% of executives expect the technology to be the driving force behind innovative products and services.  

However, how are such new business models going to look in practice? There are several distinctive features that will characterize them:

• Focus on human-AI interaction
  Every change should be meaningful, including AI integration. AI-powered business models are expected to make human experience among their top priorities, which will entail detailed and thoroughly planned user journeys and organizational workflows.
  
  
• Robust AI governance
  Depending on the level of preparation, AI can be a boon or a security hazard. To avoid the latter, AI-ready enterprises should be reviewing their organizational structures and implementing strong frameworks establishing ethical AI use, security guidelines, and monitoring compliance with the latest AI regulations.
  
  
• Tables turned on value creation 
  At least 59% of company leaders are confident that artificial intelligence is going to change the way businesses create value. Their predictions include not just technological aspects, but positive changes in the very pillars of their businesses. Compared to old models, where new products and changes were introduced quarterly and strategy sessions occurred once per year, new models are expected to drive consistent innovation and replace linear growth with exponential scaling.  

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AI industry trends: how to keep calm and prepare?

The pace at which AI vendors introduce new products and models can be overwhelming. For that reason, it’s imperative for enterprise leaders and executives to lead with knowledge and stay focus on the factors and value drivers that will remain consistent throughout the change: 

• Evolving employee skillset 
  Innovations come and get normalized. The hype simmers down. But the impact remains. In the case of enterprises, it’s the talents and skills that emerge in the course of innovation adoption. Therefore, leaders need to make sure their teams can follow suit and glean new opportunities from the change. 

People also ask what is the most important about utilizing AI trends. In my opinion, it's remembering who creates value. Technology is just a tool. Your greatest asset is the people using that tool. This is why it is so important to approach AI investment trends from the perspective of improving the work of your employees and operations. To make the most out of AI trends, you must know the strong sides of the technology and your business and identify the areas where they overlap.

Dmytro Romenskyi, BSA at Trinetix 

• Focusing on bespoke experiences 
  Modern business models often center around experiences—both those of employees and customers. Therefore, when decision-makers make their way around AI industry trends and map their innovation journey, it’s important they build from such questions is “Will it help us meet client expectations better?” and “How will it impact our working environment?” 
  
  The reason for such an approach is the duality of average client opinions on AI. According to the latest research, the AI enthusiasts that accelerate adoption of the technology make up only 25% of consumers. Meanwhile, 50% are AI agnostics who are wary and cautious about artificial intelligence. The key to getting these 50% on board is addressing their concerns and understanding how their needs align with AI capabilities. 
  
  
  
• Narrowing down the scope 
  Although many AI industry trends give off the feeling of immense scale, it doesn’t necessitate massive spending or all-encompassing transformation. Organizations don’t have to implement artificial intelligence holistically in order to get the first tangible outcomes. An SML designed and trained for the right purpose can drive considerably more value than a large AI system integrated for departments that don’t currently need it.  
  
  
  
• Knowing the limits 
  AI explainability matters. Many assumptions stem from the lack of knowledge on how AI and GenAI work, how they generate responses, and what impacts their decision. While the processes of AI decision-making aren’t completely clear to developers and engineers, it’s known that datasets and human behavior patterns affect it. Due to this, enterprise leaders need to have a realistic outlook on AI, seeing the gaps, eradicating false concepts for their employees, and establishing proper frameworks on using the technology.  

Last, but not the least aspect of benefiting from AI trends and conversational AI trends is partnering with knowledgeable experts who can guide organizations through the abundance of choices and assist them with implementing AI into their ecosystem. Doing so reveals a rich network of opportunities and enables a smooth and predictable innovation journey that engages stakeholders and onboards teams from step one.  

If you’re looking for a reliable technology partner who is experienced with embedding the best of AI trends into enterprise structure, let’s chat! At Trinetix, our capable experts can help you navigate the variety of AI industry trends, choosing exactly what works for you and your business in the long run.