What’s next for AI and data platforms? Predictions for 2025

As we approach 2025, industry leaders predict a fundamental shift in how organizations manage data with the help of artificial intelligence (AI), moving from test cases to integrated systems with measurable outcomes.

This article explores five predictions for 2025, incorporating insights from Lenley Hensarling, Technical Adviser, and Daniel Landsman, Global Director of AdTech Solutions, to understand how businesses will take advantage of data retention, real-time insights, and the expanding capabilities of Generative AI (GenAI).

1. Retaining extensive data sets will be required for AI innovation

GenAI’s effectiveness relies on the breadth and depth of the data it can use. Hensarling emphasizes the importance of retaining data:

“Generative AI depends on a wide range of structured, unstructured, internal, and external data. Its potential relies on a strong data ecosystem that supports training, fine-tuning, and retrieval augmented generation (RAG).”

This perspective reflects a growing awareness that historical data — often discarded due to storage costs or perceived irrelevance — has untapped strategic value. In 2025, we expect enterprises to prioritize:

A) Scalable, long-term storage strategies

Organizations will invest in advanced storage solutions capable of handling diverse data types — structured, unstructured, and real-time — for training AI models.

B) Enhanced trend and pattern analysis

By combining historical and real-time data, businesses will get better at detecting inefficiencies and opportunities in time to do something about them. 

C) Industry-specific data models

Retaining domain-specific data will let companies create tailored AI models, offering competitive advantages in fields such as healthcare, finance, and manufacturing. This approach will transform data retention from a burden into an innovation driver.

2. Closing visibility gaps will accelerate GenAI adoption

Despite advancements in AI technology, many businesses face challenges in effectively deploying GenAI because real-time visibility into transactional, Internet of Things (IoT), and customer behavior data is often siloed. Hensarling highlights the importance of addressing these gaps:

“In 2025, enterprises will focus on filling visibility gaps by enhancing their platforms to support vector data, similarity search, knowledge graphs, and raw data stores.”

These enhancements are important for scaling AI operations. Developments in this area will include:

A) Vector-based search and analysis

The growing adoption of vector embeddings will revolutionize how organizations store, index, and search their unstructured data, delivering faster and more accurate insights.

B) Knowledge graph implementation

Knowledge graphs will map relationships between data sets so enterprises can use complex connections to make sound business decisions. 

C) Unified data ecosystems

Breaking down silos between structured and unstructured data will help businesses operate better and analyze data across business functions. By bridging these gaps, companies will be better positioned to move from pilot projects to integrated GenAI systems.

3. Real-time data will become the lifeblood of GenAI

Integrating real-time data into GenAI systems will make them more effective. Hensarling predicts this trend will shape enterprise applications in 2025:

“GenAI must be supplemented with specific real-time data, such as vectors and graphs, to maximize effectiveness. In 2025, leading vendors will begin rolling out applications that leverage these advancements.”

The impact of real-time data on AI effectiveness will be profound:

A) Rapid decision-making

Enterprises will use real-time insights to respond quickly to market changes, customer behavior, and operational disruptions. 

B) Personalization at scale

AI systems will deliver more targeted recommendations, adapting outputs based on user actions or changing real-time conditions.

C) Predictive monitoring and intervention

Real-time analytics will let businesses anticipate and address issues such as equipment failures or supply chain bottlenecks before they worsen. This will make GenAI an important tool in helping businesses grow and compete. 

4. AI will transform multi-channel advertising strategies

AI’s ability to analyze complex data sets will revolutionize how brands engage with customers across multiple channels. Landsman highlights this shift:

“In 2025, advertisers will have even more sophisticated tools for connecting with consumers at every stage of the funnel.”

This evolution will be marked by:

A) Hyper-targeted campaigns

AI-powered predictive analytics will help advertisers anticipate consumer behavior, delivering personalized messages at the right time.

B) Integrated marketing ecosystems

Breaking down silos between data platforms will create unified systems and manage campaigns across channels.

C) AI-assisted content creation

Marketers will take advantage of AI to generate and optimize content, freeing creative teams to focus on strategy and innovation. These changes will make advertising more effective and help brands build stronger, more meaningful relationships with their audiences.

5. Marketing will prioritize collaboration through unified data ecosystems

Beyond advertising, Landsman foresees a broader shift toward cross-departmental collaboration driven by integrated data systems:

“We’ll likely see an increase in collaboration across departments — marketing, sales, IT, and customer service — facilitated by integrated data systems.”

This trend will reduce complexity and get better results in several areas:

A) Streamlined workflows

Shared data platforms will allow marketing, sales, and customer service teams to collaborate more effectively, reducing delays and miscommunication.

B) Data-driven decision-making

With access to consistent and comprehensive data, teams will make faster, more informed decisions that improve campaign performance.

C) Enhanced ROI tracking

Unified systems will better measure marketing investments, tying campaigns more closely to business objectives. By fostering collaboration, organizations will move toward a cohesive, customer-centric approach to marketing.

6. Enterprises will scale AI across critical systems

GenAI will become embedded in core business operations by 2025. Hensarling emphasizes the scale of this transition:

“2025 will be a turning point as enterprises begin to scale GenAI across critical systems like customer support, supply chain, manufacturing, and finance.”

Scaling AI will require enterprises to overcome several challenges:

A) AI governance and compliance

Organizations must establish frameworks to monitor AI performance, use it ethically,  and handle data transparently. 

B) Modernizing legacy systems

To support advanced AI applications, businesses must upgrade legacy infrastructure for real-time processing and analytics.

C) Tailored AI applications

Customized AI tools will address industry-specific needs, from automating manufacturing processes to optimizing financial forecasting.

By embedding AI into these systems, businesses will become more efficient and innovative and maintain a competitive edge in a rapidly evolving marketplace.

Preparing for the data-driven future

2025’s predictions make it clear where AI and data platforms are going. AI will shift from a novel experiment into an indispensable tool by focusing on data retention, filling visibility gaps, taking advantage of real-time data, and working across business units.

Organizations must prepare to take advantage of these trends as we approach the new year or risk falling behind.