Aible Announces New Features to Innovative AI Platform, Enables Enterprise Teams to Build an Integrated Business Central Nervous System

Aible, the pioneer in ROI-optimized AI, today announced dozens of innovative updates to its flagship product, enabling enterprises to deliver ROI from AI faster.

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Today, most AI is trained to maximize model accuracy, turning it into an academic exercise of little use to businesses, which need models that optimally impact sales, profit and growth. It’s no surprise that a recent survey by MIT-Sloan and the Boston Consulting Group found that a mere 10% of companies report seeing financial gains from their AI investments.

Aible is AI that systematically delivers business impact while respecting business constraints. With the Boomi partnership, Aible is accessible via 1500+ applications that business users leverage every day—at the moment when they make a decision. Users can inject AI into their everyday workflows, seamlessly improving data-driven actions.

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AI That Spans the Entire Enterprise

The new Aible features extend its innovative AI platform to enable teams to build an integrated Business Central Nervous System across related functions. Aible enables users across the enterprise to easily track and audit actions, encouraging everyone—from business users to IT and data science teams—to contribute to the end-to-end workflow of AI. By eliminating work silos, teams can go from raw data to AI recommendations in hours, all without ever developing a new skill set or waiting on each other.

Other innovative features available with Aible Enterprise include:

• Assure AI Fairness: New with Aible Enterprise, customers can now systematically ensure fairness with AI by proactively defining fairness objectives instead of reactively detecting and removing bias.
• Natural Language and Vision Services: New Natural Language and Image Processing features available with Aible Enterprise allow teams to capture intelligence from a broader set of sources. Businesses can leverage the services with end-to-end automation and without manual tagging requirements, avoiding time-consuming and error-prone processes.

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