SAS launches AI models after USD $1 billion investment push

SAS has released a set of artificial intelligence models targeting business processes that are traditionally labour-intensive and time-consuming.

The launch is part of a USD $1 billion investment by SAS in industry-focused data and AI solutions intended to streamline specific business bottlenecks.

The newly introduced models are designed either for immediate deployment or to serve as a foundation that organisations can quickly customise, enabling faster model training on internal data. SAS states that these packaged models are compatible with existing systems and suitable for organisations regardless of size.

Model Applications

The available models span several industries. For cross-industry use, SAS has presented AI-Driven Entity Resolution and Document Analysis. Within health care, there is a model focused on Medication Adherence Risk, while manufacturing clients have access to Strategic Supply Chain Optimisation. Public sector releases include Payment Integrity for Food Assistance and Tax Compliance for Sales Tax.

SAS has indicated that each model was developed according to its Data Ethics Practice guidelines. According to the company, this ensures the resulting outputs are easy for organisations to understand and explain. SAS asserts that users can improve productivity and return on investment as a result of deploying these solutions, while maintaining an adherence to principles of responsible use.

Kathy Lange, Research Director at IDC, commented on the nature and flexibility of the SAS Models portfolio. She said, "SAS Models are based on SAS' core assets, talent and intellectual property from its wealth of experience working with customers to solve industry problems. Between its scalability and seamless integration with existing environments, SAS Models are a great option for those looking to accelerate time to production who might lack the expertise or time to build models from scratch."

Future Roadmap

In addition to the models now available, SAS outlined plans for expanding its industry-specific AI library. Scheduled for later in 2025 are new models addressing needs in banking (Fraud Decisioning for Payments and Card Models), health care (Payment Integrity for HealthCare), manufacturing (Worker Safety Monitoring) and the public sector (Tax Compliance for Individual Income Tax).

SAS also announced it is working on "agentic" versions of its models, designed to increase autonomy within business-specific AI solutions. According to SAS, these forthcoming agentic offerings will aim to further reshape operational efficiency by introducing pre-built agents capable of automating complex data preparation processes.

Data Preparation Solution

The challenge of preparing large volumes of raw data so that it is suitable for AI model training is significant for many organisations. Data scientists often spend extended periods building and refining data lakes—repositories that structure raw customer data for analysis.

SAS is responding to this challenge with an AI agent designed to automate complex data preparation processes, thereby facilitating real-time model operation with minimal manual intervention.

"We believe the future of AI lies in agents that are not only intelligent but also responsible, ready-to-use and relevant," said Udo Sglavo, VP of Applied AI and Modeling, R&D at SAS. "Our new industry-specific models, built on decades of domain expertise and guided by our ethics-first approach, represent a bold step toward agentic AI: solutions that think with context, act with purpose and deliver real-world impact."

Delivery and Integration

SAS reports that organisations implementing its models have access to clear, simplified documentation intended to support transparent and responsible use. Each model can be tailored to meet sector-specific requirements and can supplement existing IT and business infrastructures.

The release of these new AI models represents the latest step in SAS' long-term development strategy, continuing its aim to address evolving customer priorities across a range of sectors.