
From a data management perspective, and as AI continues to become a core part of business strategies, companies require sophisticated systems that can make connections across large and unstructured data sets.
With organizations estimated to spend an average of $21 million on AI in 2024, the need for AI technologies to integrate and work together efficiently has never been so critical. Connecting the dots and contextualizing data with graph technologies is a critical enabler for executing data-driven strategies in the Age of AI. Graph-based approaches enhance and simplify data management with significant cost savings. They also drastically improve the ability to extract value and insights from data and incorporate them into business decisions and processes.
From a data management perspective, and as AI continues to become a core part of business strategies, companies require sophisticated systems that can make connections across large and unstructured data sets, whether in legal analysis, financial, or customer interactions.
By accelerating access to data, business users and data leaders alike will continue to adopt solutions such as knowledge graphs to simplify and improve data integration and governance across data silos, conduct searches quickly and accurately, and scale data integrations while maintaining customizability to meet specific workflow and business requirements.
Top 2025 AI-related Data Management Trends
As a result, in 2025, expect to see data leaders leverage the following data management trends:
1) Organizations will look to enterprise AI readiness to make data great again
Even with the dawn of AI, organizations still face issues such as siloed heterogeneous data, governance concerns, and poor data quality; all of which hold back enterprise AI projects. However, it’s becoming clear that AI will be the carrot to drive better data quality & governance forward. In 2025, data leaders will no longer be able to deny that the main hurdle isn’t AI models but the lack of AI-ready data enriched with business context, trust, and rich metadata. Accordingly, data leaders will take a proactive and holistic approach to enterprise data readiness for AI in the new year.
Organizations will look at the data fabric pattern even more closely in 2025 to create unified data access across the enterprise, integrating structured and unstructured data with metadata as the connecting glue. Enterprises will also realize that for better AI governance, they must use their proprietary knowledge and data assets to help their GenAI applications bring more reliable results. The re-alignment of GenAI projects to enterprise data and knowledge management initiatives will be critical in the coming year and key to making data great again.
2) Governance and Return on AI (ROAI) will be on top of the mind for data leaders
As AI becomes pervasive, so are the risks. In 2025, organizations will double down on the need for strong governance to address data bias, ethical privacy, and hallucinations. Taking a strategic approach to AI adoption, with deep commitments to AI governance will be critical to emerge victorious in the AI race. To succeed next year, organizations will continuously access the potential ROI in any AI effort and ask themselves key questions before getting started such as: Do the factors and use cases warrant allocating a budget for the AI project? How can we decide and estimate costs and ROAI from the AI initiatives, and how can we measure the success of these investments? Can we rapidly adjust to changing tech landscapes if needed? How do we balance innovation with associated risk in AI investments? Where is the ROI, and what are the critical metrics? Specifically, what zone will host our AI initiative, and to what metrics are we going to hold ourselves accountable?
3) Enterprises IT teams will outsource GenAI implementation risks and run proof of value projects with short iterations
Throughout 2023 and 2024, many enterprises struggled with GenAI pilot projects, failing to deliver tangible value, with some on the 3rd round of GenAI initiatives without getting production quality grade outcomes. In 2025, we predict a shift in enterprise GenAI strategies: Outsourcing Implementation Risks: Enterprises will outsource GenAI implementation to consultants to accelerate time-to-value and mitigate risks. In July, NYTimes and FT reported that Accenture and BCG make more money from Generative AI than OpenAI and Anthropic combined.
Aligning GenAI with Data Management in 2025 means enterprises will integrate GenAI projects with data management initiatives to ensure data quality, accessibility, and compliance. By adopting these strategies, enterprises can harness the power of GenAI while minimizing potential pitfalls.
See also: Key Lessons for Building Effective RAG Systems
4) RAG will evolve towards multi-method frameworks that orchestrate retrieval agents to answer complex questions and optimize cost and performance
Retrieval Augmented Generation (RAG) enhances LLMs by incorporating external knowledge. While “chunky RAG” (retrieving document chunks) is popular, it limits the complexity of queries. In 2025, organizations will recognize that RAG is not a black box that magically helps LLMs leverage external information sources. They will require a more comprehensive RAG approach that allows flexibility for the different sub-tasks, including Document Chunk Retrieval, Entity Retrieval, and Knowledge Graph Retrieval, and answer NL questions based on relevant texts and data.
Enterprise architects will recognize that LLMs are a must only for the last of these tasks. For all the others their performance is almost always subpar and answering more complex questions often requires multi-hope retrieval.
Next-generation RAG platforms that support diverse query methods and flexible retrieval workflows will dominate the market in 2025. They will also be credited for enabling agents to interpret and respond dynamically across various inquiries, from pinpointing related concepts to analyzing extensive datasets. Equally important, solution architects will be able to evaluate alternative retrieval tools to optimize cost and performance.
5) The use of standard data schemas and open knowledge will bring more intelligent, reliable, and accurate conversational AI without increasing cost and complexity
Conversational AI struggles with complex questions due to LLM limitations in understanding data schema. Graph RAG, using knowledge graphs with semantic data schema and domain knowledge, offers a solution. By leveraging Semantic Web standards and publicly available resources like Wikidata and Schema.org, organizations can easily build knowledge graphs and avoid the need for additional fine-tuning. This “plug-and-play” approach empowers LLMs with semantic context and factual data, making complex question-answering and data self-service applications a reality in 2025.
As enterprises pour millions into AI investment, organizations will look to implement critical knowledge graph infrastructures that ensure enterprises can realize the technology’s full potential, that the data is trusted, and can be implemented at scale.
The combination of knowledge graphs and semantic AI technologies will enable enterprises to optimize their return from scalable AI solutions that can provide deeper insights, automate processes, and improve decision-making, creating an essential foundation for further innovation.