As we get underway in 2025, the artificial intelligence landscape is evolving at an unprecedented pace, presenting both challenges and opportunities for organisations across industries. Let’s explore the key AI trends that will shape the coming year and how Databricks and Datasmiths can position your organisation to leverage these advancements effectively.
1. Rise of Agentic AI Systems
Agentic AI, capable of performing tasks independently, is set to become a dominant force in 2025. These systems will collaborate to accomplish complex work, moving beyond content generation to more sophisticated applications. Gartner identifies AI agents as a key trend for the year ahead, with 32% of executives ranking AI agents as the leading trend in data and AI in 2025. The scalability and adaptability of agentic AI will enable companies across industries to perform tasks more quickly and easily, from personalised customer engagement in retail to predictive analytics in finance.
Databricks offers several features to capitalise on this trend. The Mosaic AI Agent Framework provides tools to help developers build enterprise-ready agents in Python, supporting integration with third-party agent authoring libraries like LangChain and LlamaIndex. Databricks also offers an AI Playground for rapid prototyping of tool-calling agents using a low-code UI. Additionally, Databricks’ field AI assistant, built on the Mosaic AI agentic framework, demonstrates practical applications by integrating multiple data sources to provide customer insights, data hygiene alerts, and sales collateral for sales teams.
2. Multimodal AI Integration
Multimodal AI integration is advancing rapidly, combining text, image, audio, and video processing to create more sophisticated and intuitive AI systems. This integration enables AI to understand and process information across multiple sensory inputs, mimicking human-like perception and cognition. For businesses operating in data-rich environments, this approach offers unprecedented opportunities to extract deeper insights and create more engaging user experiences.
Databricks is at the forefront of this trend, offering powerful tools to leverage multimodal AI:
- Mosaic AI: This unified platform supports the development of multimodal AI applications, integrating various data types seamlessly.
- LLaVA Integration: Databricks provides support for LLaVA, a multimodal model combining vision encoding and language processing.
- Twelve Labs Partnership: Databricks has partnered with Twelve Labs to enhance video understanding capabilities, allowing for advanced multimodal analysis of video content.
- GPU-enabled Clusters: These clusters support the computational demands of processing multiple data modalities simultaneously.
- Vector Search: This feature enables efficient querying and retrieval of multimodal data representations.
By leveraging these Databricks features, organisations can build sophisticated multimodal AI systems that process and analyse diverse data types, leading to more comprehensive and accurate AI-driven insights and applications
3. Evolution of Retrieval Augmented Generation (RAG)
The evolution of Retrieval Augmented Generation (RAG) systems in 2025 is marked by significant advancements towards multi-method frameworks. These sophisticated systems now employ adaptive retrieval mechanisms that dynamically adjust based on query complexity and user intent. For instance, multi-stage retrieval pipelines incorporate contextual re-ranking, where initial results are refined using semantic filters, leading to a 15% improvement in retrieval precision for many use cases.
A key development is the integration of hybrid indexing, combining dense and sparse representations to achieve both depth and breadth in retrieval. This approach allows RAG systems to capture semantic meaning while maintaining effectiveness for exact matches. Additionally, the implementation of retrieval confidence scoring has been seen to reduce irrelevant retrievals by 20%, enhancing the accuracy of AI-assisted recommendations.
Looking ahead, RAG systems are moving towards self-improvement through reinforcement learning, refining retrieval strategies based on user interactions. This iterative feedback loop will make RAG systems more intuitive and aligned with domain-specific needs, particularly in fields like healthcare and legal analysis.
Databricks offers a comprehensive suite of tools to capitalise on this trend, including MLflow for managing the complete machine learning lifecycle of RAG models, Delta Lake for ensuring data quality and reliability in retrieval processes, and Databricks Runtime for Machine Learning to accelerate the development and deployment of advanced RAG systems.
4. AI-Enhanced Security Systems
AI-Enhanced Security Systems will become a cornerstone of cybersecurity strategies in 2025. As cyber threats evolve in sophistication and frequency, AI will be instrumental in strengthening defensive capabilities, rapidly identifying potential threats, and significantly reducing response times to security incidents. This advancement will lead to more robust protection against complex, AI-driven attacks that can simulate legitimate traffic and bypass traditional security measures.
However, this progress will inevitably spark an “arms race” between cybersecurity defenders and malicious actors. As defensive AI systems become more advanced, attackers will leverage AI to create more sophisticated and harder-to-detect threats. This escalation will drive continuous innovation in AI security technologies and strategies.
Databricks offers several features to help organisations capitalise on this trend, including their comprehensive AI Security Framework (DASF). This framework provides a defence-in-depth approach to AI security, offering 53 prescriptive controls for mitigating AI security risks, including AI-specific controls like model serving isolation, prompt tools, and centralised LLM management. Additionally, Databricks’ unified data platform enables real-time threat detection and response, crucial for staying ahead in this AI-driven security landscape.
5. AI in Scientific Research
AI tools are poised to revolutionise scientific research in 2025, particularly in addressing global challenges. In materials science, AI is accelerating the discovery of sustainable materials, with Meta and Hugging Face releasing massive datasets and models to simplify and speed up research. This trend extends to drug discovery, where AI’s ability to analyse complex data and predict outcomes is streamlining the research and development process. The rapid development of the COVID-19 vaccine showcases AI’s potential to significantly reduce drug development timelines and costs.
Databricks offers a comprehensive suite of tools to capitalise on this trend, including its Unified Data Platform for integrating and processing multimodal scientific data, MLflow for managing the complete machine learning lifecycle in research projects, and Delta Lake for ensuring data quality and reliability in AI model training for scientific applications.
Contact us to find out how you can leverage Databricks’ capabilities, together with Datasmiths to stay at the forefront of AI innovation, turning these emerging trends into tangible business.