
The state of AI in 2025 was recently published, and we immediately compiled the most surprising AI trends businesses are leveraging this year. In this article, we’ll cut through the noise and spotlight the most groundbreaking AI trends already shaping the way companies operate this year. Ready to see what’s driving this revolution? Let’s jump in.
Top Shocking AI Trends Businesses Are Using in 2025
Synthetic data usage on a daily basis – Businesses will increasingly rely on synthetic customer data, which is cheaper and, in some cases, more reliable than real data.
Consumer behavior data will be the new gold – AI-powered digital marketing will blend with traditional methods, unlocking new perspectives and adding significant value.
The rise of micro LLMs – Smaller, specialized language models will gain traction, proving that less is often more in the AI landscape.
The beginning of A2A (Agent-to-Agent) – The demand for automation and micro LLMs will drive the emergence of A2A, where AI agents interact with autonomous systems.
AI agents for workflow automation – Intelligent AI tools will evolve into indispensable partners, streamlining workflows, managing operations, and enhancing employee productivity.

Synthetic Data usage on a daily basis
Throughout history, information has been one of humanity’s most valuable assets, and its importance is increasing faster than ever. However, strict data regulations have made it challenging for businesses to leverage personal data, leading to the rise of synthetic data as a practical and cost-effective solution. By 2025, the adoption of synthetic data is anticipated to become a dominant trend among businesses.

What is Synthetic Data?
Synthetic data is artificially generated data created using real data and trained models that replicate the patterns and structure of the original dataset. Designed to produce results similar to the original data when analyzed, it serves as a scalable and privacy-safe alternative.
The process of creating synthetic data, known as synthesis, employs techniques like decision trees or deep learning algorithms. For instance, Generative Adversarial Networks (GANs)—commonly used in image recognition—pair two neural networks: one generates synthetic data, while the other evaluates its authenticity, creating highly realistic results through iterative refinement.
Synthetic data can be categorized into three types based on its source:
From real datasets – Directly generated from existing data.
From expert knowledge – Built on insights and rules defined by analysts.
Hybrid – A mix of real data and expert input.
Synthetic data is especially valuable in situations where real-world data is difficult, expensive, or time-consuming to collect. It can be rapidly generated at scale and tailored to specific needs, making it ideal for machine learning, software testing, quality assurance, and transfer learning. Furthermore, synthetic data plays a critical role in privacy assurance by preventing the leakage of sensitive personal information from the original dataset.
Real-Life Examples of Synthetic Data Projects
Enaks Market Intelligence leverages synthetic data to better understand customer behaviors and craft messaging that resonates emotionally. Their audience simulator, SaaSy by Enäks, generates synthetic data simulating real C-level managers in the SaaS industry with 96% accuracy. SaaSy provides marketers with insights into preferences, opinions, and sentiments, helping them address customer needs more effectively. This innovative tool is publicly available and free to use.
Erste Bank used synthetic test data to design and develop their mobile banking app. By anticipating UX/UI issues before launch, they created a more user-friendly experience while saving substantial time, money, and resources compared to traditional user research methods.
JPMorgan employs a synthetic data sandbox to expedite data-intensive proof-of-concept projects with third-party vendors, enabling faster experimentation and development while maintaining compliance with privacy regulations.
Consumer behavior data is the new goldÂ

In 2025, hidden insights such as sentiment analysis, psychological factors, personal motivations, and social listening will become essential in data analysis and marketing. A major shift is coming to digital marketing, leading to the resurgence of traditional marketing methods.
In 2024, many businesses realized that the consumer insights traditionally provided by digital marketing—something that has been the standard for over 15 years—are no longer sufficient to maintain a competitive edge. In 2025, we will see a new wave of marketing intelligence and consumer behavior research, with marketing intelligence companies replacing traditional digital marketing agencies.
This new era of marketing will bring a fresh approach, emphasizing emotionally-driven strategies grounded in solid, fact-based consumer behavior data. It will demand advanced skills in data analysis, modeling, AI and statistical evaluation.
The Rise of Micro LLMs

While giants like Anthropic, Google, Meta, Windows, and OpenAI dominate the development of traditional large language models (LLMs), smaller models are gaining increasing attention from small and medium-sized enterprises. While large models are impressive, Micro LLMs are becoming more appealing to businesses looking to invest in generative AI.
Micro LLMs simplify the implementation process and reduce computing demands, making advanced AI more accessible to companies that were previously discouraged by high costs or complexity. These compact models offer better performance, greater accuracy, and fewer hallucinations. Their smaller size also reduces environmental impact, strengthens data control, and enhances cybersecurity, enabling deployment in localized, segmented network environments with diverse security needs.
The Beginning of A2A

The growing demand for automation and the emergence of Micro LLMs are gradually shaping a completely new business model: A2A (Agent-to-Agent), which reflects interactions between AI agents and other autonomous systems.
As AI agents become more complex and interconnected, the need for these models will continue to expand. The focus of A2AÂ will be on how AI agents communicate, negotiate, and collaborate with each other to accomplish tasks or deliver value. Autonomous decision-making and real-time data exchange will play a key role in this model. Task delegation and feedback loops will also be crucial when applying A2AÂ in practical scenarios.
Examples of A2A Interactions:
Supply Chain Management: AI agents in logistics systems communicate with warehouse inventory to manage stock levels and optimize delivery schedules in real time.
Financial Services: One AI system analyzes risk, while another evaluates market conditions, working together to adjust investment portfolios.
E-commerce: AI chatbots from different platforms collaborate to enhance cross-platform customer experiences, such as order tracking and price matching.
Healthcare: Diagnostic AI systems communicate with treatment planning AI to optimize patient care.
For A2A to function effectively, a highly stable ecosystem is necessary. This includes technologies like APIs, interoperability standards, multi-agent systems (MAS), and federated learning.
A2AÂ has the potential to reshape workflows across industries, especially as AI evolves from isolated tools into interconnected networks of agents working together.
AI agents for workflow automationÂ

AI agents are poised to redefine how we work, live, and solve global challenges in 2025. These intelligent tools will evolve from simple automation systems to essential partners capable of managing complex workflows, streamlining business operations, and supporting employees in new, meaningful ways.
Imagine customer support agents delivering consistent, omnichannel experiences across in-person, online, and mobile platforms. Internal agents will optimize processes, while creative agents will amplify design and production capabilities. At home, AI agents will simplify daily tasks, while on a global scale, they’ll tackle critical issues like the climate crisis and healthcare access.
This transformation is driven by advancements in AI models, including improved memory, reasoning, and multimodal capabilities. By becoming faster, smarter, and more efficient, AI agents will handle complex tasks such as generating reports, managing supply chains, and resolving IT issues—freeing up professionals to focus on higher-value work.
Some of the reasons why AI agents will lead in 2025:
Enhanced AutonomyAI agents are evolving to perform tasks independently, thanks to advanced reasoning capabilities and better data curation. Businesses can deploy agents to handle operations like inventory alerts, supplier recommendations, and sales order executions, seamlessly integrating them into daily workflows.
Accessibility for AllFrom no-code platforms like Copilot Studio to advanced tools in Azure AI Foundry, building and using AI agents will be accessible to everyone. This democratization of AI ensures businesses of all sizes can benefit from customized, efficient solutions.
Human Oversight as a PriorityWhile AI agents become more autonomous, human oversight will remain essential. Setting boundaries for what agents can and cannot do will be key to ensuring ethical and effective AI use.
How to start using AI for my business?
Preparation, transition, and harvest—this is the best way to start and how Enaks adapts to changes. Begin gradually in both the preparation and transition phases. First, identify areas suitable for AI automation, define desired outcomes, and execute or partner with someone to implement the automation.Â
Always A/B test alongside your current manual method. Once you’ve gathered sufficient data, analyze which approach performs better and optimize your new strategy based on user insights.