SAS AI Agents to Bridge Sector-Specific Expertise Gap

The Silicon Valley sprint driving the AI industry is leaving a talent gap in sector-specific expertise. 

As industry players struggle to scale AI agents and models, SAS is bridging out a suite of purpose-built solutions to address the industry’s toughest challenges. 

The expansion is a part of its US$1bn investment in industry solutions where it aims to enhance its portfolio of accelerators throughout 2026 to meet the rising demand. 

Manisha Khanna, Global Market Strategy Lead, Applied AI at SAS, says: “When organisations are left stitching together ad-hoc AI frameworks and experiments, they often fail to achieve the competitive edge they’re looking for when they invest in AI”.

Streamlining global supply chains

Leading the charge in accelerating operational intelligence is the SAS Supply Chain Agent. It streamlines supply and operations planning (S&OP), a crucial process retailers and manufacturers use to manage inventory.

Traditional S&OP is a strenuous multi-day process that relies on complex spreadsheets to predict inventory needs for the coming year.

Because of the time and resources required, most organisations only run this planning process once a month.

SAS’ new agent comes to rescue by continuously balancing demand and operations, allowing users to optimise supply chains in near real-time. 

Business users can interact with the agent through a chat experience to explore multiple scenarios with possible outcomes, such as what would happen if there’s a 15% drop in demand.

The Supply Chain Agent also provides explanations on how it arrived at specific decisions to ensure transparency and trustworthiness.

Kathy Lange, Research Director at IDC’s AI, Data and Automation Software practice, says: “Current pre-packaged agents tend to tackle basic processes; with Supply Chain Agent, SAS is compressing a very complex process, which could deliver significant value. This offering positions SAS to bring its longstanding supply chain knowledge to a new generation of agentic AI solutions.”

Transforming operations and safeguarding workers

SAS is using digital twins and synthetic data to improve operational efficiency in industry environments. The company creates virtual replicas of factory floors using Unreal Engine, allowing teams to simulate scenarios without risking physical assets. 

First debuted at SAS Innovate 2025, the digital twins create a proving ground to explore the ‘what if’ questions. 

For example, a major medical device sterilisation provider is using a SAS digital twin to identify bottlenecks that slow down lifesaving services.

SAS Worker Safety also uses synthetic data and digital twins to train computer vision models on rare but plausible dangerous workplace accidents.

Data from 2024-25 revealed that 124 workers were fatally injured in the UK, with falls and machinery accidents making up a significant number of these injuries.

The new offering creates realistic footage for training computer vision models on specific workplace safety scenarios, allowing organisations to model forklift collisions or equipment failures without involving real employees.

Using synthetic data also ensures that no personally identifiable information is exposed during the training process.

Fighting fraud across financial services

To address rising financial fraud, SAS is launching Fraud Decisioning for Payments to help deliver real-time fraud detection across various financial transactions.

According to a study by SAS and the Association of Certified Fraud Professionals, 75% of anti-fraud professionals report a surge in financial fraud and scams targeting consumers. 

Additionally, 55% of these professionals anticipate a significant increase in deepfake social engineering and Gen AI document fraud over the next two years.

Amid this rising threat, global banks, insurers and financial services organisations are trusting SAS for its financial fraud detection models. These agents protect consumer assets and identities across the financial services sector. 

The Fraud Decisioning for Payments models are trained on patterns from a broad dataset contributed via consortium by major global financial institutions, spanning credit card, debit card and ATM fraud.

It also covers digital wallet fraud and emerging vectors like money mule detection and by deploying these models on the SAS platform, institutions do not have to start from zero. 

By providing accelerators designed for specific functions that integrate with existing workflows, SAS aims to bridge the gap between experimentation and practical deployment.