2024 was another year of unparalleled speed of AI adoption across organizations. As we move into 2025, companies are increasingly leveraging AI to automate, predict, and innovate, creating tangible value while navigating challenges like data privacy and security. So, what are the top trends to watch for in 2025?
The answer often depends on perspective. In my experience working with industry leaders, including highly regulated sectors like pharma and animal health, certain trends stand out. From the rise of specialized small language models to AI-powered workflows and local AI innovations, these technologies are reshaping the future of work.
This article presents my personal take on the most impactful AI trends shaping business applications in the year ahead.
Agentic Workflows & AI Agents
For many readers this topic may feel familiar—AI Agents have been discussed for a while now. These intelligent programs are designed to perform certain tasks in the organization, enabling the automation and optimization of business workflows. They can have varying levels of autonomy (from strictly planned and developed flows using tools to fully autonomous agents that plan and execute activities). AI Agents can execute tasks like data analysis, decision-making, and communication across systems with minimal or reduced human intervention.
In 2024, we already heard a lot about AI agents and agentic workflows. AI models are getting better and better at performing tasks within these workflows, using tools, planning executions, and more. There’s a good reason for all the attention—AI agents are poised to significantly influence business process automation and, even further, transformation.
AI agents are already transforming industries. In loan underwriting, they’ve reduced cycle times by 20-60%, according to McKinsey, by automating complex processes like document preparation and financial analysis. Marketing teams use these agents to manage campaigns more efficiently, while in IT, they modernize legacy codebases, saving time and reducing errors.
The growing adoption of AI agents signals a shift toward smarter, more adaptive workflows. As businesses face increasing complexity, these agents offer scalable solutions that improve productivity and decision-making.
With their ability to handle diverse tasks across industries, AI agents are set to become indispensable tools in the competitive landscape of 2025.
Small Language Models: Fine-Tunable and Specialized Models
In 2024, we observed the emergence of new, highly efficient small language models (SLMs) like Gemma 2 or Phi-3, which drove their adoption in companies. Their ability to run in local environments— preserving privacy and security—and on edge devices (e.g., in manufacturing sites) makes them particularly suitable for many enterprise use cases. Enterprises exploring language models are prioritizing SLMs due to their scalability and lower operational costs.
SLMs are particularly valued in regulated industries where sensitive data must remain on-premises. For example, healthcare organizations use SLMs for patient data analysis, ensuring compliance with GDPR and HIPAA standards.
The rise of SLMs addresses two major challenges in AI: accessibility and data security. By being resource-efficient and capable of local deployment, SLMs allow smaller companies and regulated industries to harness AI without compromising sensitive information.
In 2025, we can expect the growth of the SLM trained for specific domain or task (e.g., financial document analysis for domain-specific use cases or Named Entity Recognition for task-specific applications). Additionally, companies will likely build multi-agent platforms where individual AI agents will utilize different, specialized models.
The ability of SLMs to be effectively fine-tuned for specific tasks makes them a practical and scalable solution for businesses and places them among AI trends for 2025 and beyond.
AI Applications Security & Privacy
As AI becomes a bigger part of how businesses operate, ensuring its security and protecting user privacy have become top priorities. This trend is all about creating systems that safeguard AI from threats while following data regulations like GDPR and CCPA.
A McKinsey survey found that 57% of businesses see data privacy as the biggest challenge to scaling AI, and over 40% of companies are investing in AI-focused cybersecurity to prevent issues like data tampering and adversarial attacks.
For AI to thrive, businesses need to build trust. A data breach or weak security can cause serious harm to a company’s reputation and bottom line.
With AI becoming more central to how organizations work, prioritizing security and privacy isn’t optional—it’s essential. That’s why we’ll see even greater focus on this area in 2025.
Personal Productivity with Local and Embedded AI
Local and embedded AI refers to AI models that can run directly on user devices, like laptops or smartphones, instead of relying on cloud servers. Tools like Apple Intelligence and multiple Copilots offered by Microsoft (especially Copilot+ PC) are early examples, making AI features seamlessly available within everyday apps. Also, local AI keeps data on the user’s device, reducing concerns about sharing sensitive information with third-party servers.
Windows Copilot already integrates AI into the operating system, helping users with tasks like summarizing emails, creating documents, or managing schedules—all without leaving their desktop.
Apple is also emphasizing local AI with features like on-device text prediction in iOS 17, smarter offline Siri, and its Neural Engine powering it all. It’s all about keeping your data private while making your apps smarter and faster.
Local AI makes powerful tools more accessible and user-friendly. By embedding AI directly into the apps people already use, it removes barriers and makes the technology feel intuitive. This trend also addresses privacy concerns by processing data locally.
AI trends for 2025 suggest we’ll see more applications from various vendors embedding AI features directly into end-users interfaces (stay tuned for upcoming new features in our AdaptiveGRC).
Model Fine-Tuning: Tailoring AI for Business Needs
Model fine-tuning is the process of customizing pre-trained AI models using specific datasets from an organization. This process is a more advanced alternative to RAG architecture or can enhance its results from. It allows businesses to adapt generic models to their unique needs, embedding company knowledge and improving the relevance and accuracy of the model’s outputs.
Fine-tuning pre-trained language models can lead to significant improvements in domain-specific tasks. For instance, a study on a retrieval-augmented generation framework fine-tuned for the telecom domain reported a 30% increase in accuracy over the base model, achieving an overall accuracy of 81.20% in a downstream question-and-answer task.
Businesses are using fine-tuned AI for customer support (e.g., chatbots trained on company-specific FAQs), fraud detection in financial transactions, and personalized marketing campaigns.
Gartner predicts that by 2027, over 50% of generative AI (GenAI) models used by enterprises will be tailored to specific industries or business functions, up from approximately 1% in 2023. This trend is closely linked to the rise of SLMs, which are in general much cheaper for fine-tuning.
Data: The Lifeblood of AI
While fine-tuning allows businesses to tailor AI models to their specific needs, the quality and relevance of the underlying data ultimately determine success. In AI and machine learning, data isn’t just input—it’s the foundation for building models and driving meaningful outcomes. High-quality, well-structured (and often labeled) data enables AI systems to learn, adapt, and perform effectively, making data a critical asset for any organization adopting AI.
Poor-quality data is cited as the top barrier to AI adoption by 56% of enterprises, according to McKinsey.
Even as GenAI models promise pre-trained capabilities, the value of data has only increased. Many organizations are now investing heavily in data curation for fine-tuning models, ensuring relevance and accuracy in outputs.
In 2025, businesses that prioritize data as a strategic asset will lead the charge in AI innovation, gaining a significant competitive edge and building the foundations for effective and fine-tuned models.
Additional 2025 AI Trends to Watch
While the trends we’ve covered will significantly shape AI in 2025, other exciting developments are also worth keeping an eye on:
- Reasoning in GenAI models
We can expect that in 2025, there will be further improvements in state-of-the-art models for reasoning tasks. These models will be used for planning and executing agentic workflows, where individual tasks may be performed by smaller, more specialized models. - GenAI in Industry-Specific Applications
From healthcare diagnostics to retail personalization, tailored GenAI models are unlocking new opportunities in specialized fields. - Edge AI
Models running on IoT devices and edge systems are enabling real-time processing and decision-making, especially in areas like smart cities and industrial automation. - AI Sustainability
As the environmental impact of AI grows, companies are prioritizing energy-efficient models and “green AI” initiatives to minimize carbon footprints. - Ethical AI and Transparency
There’s an increasing focus on making AI systems fair, explainable, and compliant with ethical standards, especially in sectors like finance and law.
AI Trends for 2025: Key Takeaways
AI is moving fast, and the multitude of new AI terms linked to the trends shaping 2025 shows just how much potential it has to transform businesses. From smarter workflows powered by AI agents to compact and efficient SLMs, these technologies are already changing the way companies operate. With an increased emphasis on privacy, personalization, and the strategic use of high-quality data, it’s clear we’re just scratching the surface of what’s possible.
But let’s not forget the other exciting areas on the horizon—things like edge AI, sustainable models, and efforts to make AI more ethical and transparent. The bottom line? Companies that embrace these trends and keep an eye on the bigger picture won’t just keep up; they’ll lead the way into a smarter, more connected future.
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