Dun & Bradstreet and IBM Collaborate to Bring Trustworthy Business Insights to Fuel Responsible Generative AI Solutions Powered by watsonx

Dun-_-Bradstreet-and-IBM-Collaborate-to-Bring-Trustworthy-Business-Insights-to-Fuel-Responsible-Generative-AI-Solutions-Powered-by-watsonx

Dun & Bradstreet a leading global provider of business decisioning data and analytics, and IBM (NYSE: IBM), have announced a strategic collaboration that will bring together Dun & Bradstreet’s Data Cloud and IBM’s watsonx to help organizations responsibly expand their use of generative AI. Dun & Bradstreet also intends to leverage watsonx for its workflows and solutions, supported by IBM Consulting.

“The transformative power of generative AI for business is wholly dependent upon data that is accurate, trusted, and targeted for enterprise use cases”

Through this collaboration, Dun & Bradstreet and IBM aim to develop offerings for clients to use in their native workflows to drive generative AI adoption that can help achieve growth and efficiency across a variety of business functions. The offerings will leverage watsonx, IBM’s next-generation AI and data platform, and draw upon Dun & Bradstreet’s Data Cloud, containing more than half a billion private and public companies, market-leading Identity Resolution, including the D-U-N-S® Number and the company’s generative AI capabilities.

Dun & Bradstreet’s Ask Procurement™ is one of the new solutions that IBM and Dun & Bradstreet intend to bring to market. Ask Procurement will be designed to help empower procurement professionals to unlock new data and insights with a 360-degree view into all aspects of a company’s business relationships to help increase savings, reduce time, and mitigate the potential for risk. The solution is expected to leverage the D&B AiBE™ platform which will feature watsonx supported models and other generative AI capabilities fueled by Dun & Bradstreet’s vast Data Cloud. Slated for delivery in the first half of next year, Ask Procurement will be able to be integrated with Dun & Bradstreet solutions or an enterprises’ existing ERP or procurement solution.

“At Dun & Bradstreet, being a trusted data partner and a responsible AI partner to organizations are synonymous,” said Ginny Gomez, President, North America, Dun & Bradstreet. “As two trusted brands that bring nearly 300 years of combined experience to the businesses we serve, Dun & Bradstreet and IBM are ideally suited to help companies responsibly navigate the rapidly evolving generative AI space because we know their business environments and processes well. And with hundreds of thousands of organizations globally relying on us everyday, we believe there is no better company than Dun & Bradstreet to lead the industry and our clients into the future.”

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Through the collaboration, D&B.AI™ Labs, IBM Consulting, and IBM Expert Labs teams will leverage the IBM Garage methodology to build AI use cases, implement watsonx, and develop applications that help address employee productivity, enhance customer experiences, mitigate business-to-business risks, automate workflows, and optimize efficiency.

“The transformative power of generative AI for business is wholly dependent upon data that is accurate, trusted, and targeted for enterprise use cases,” said Rob Thomas, Senior Vice President, Software, and Chief Commercial Officer, IBM. “We plan to bring solutions to market that combine IBM’s best-in-class AI and data platform and consulting services with Dun & Bradstreet’s supply chain expertise and vast sets of trusted data. Together, we can empower clients to responsibly build AI-powered solutions and confidently scale their use of generative AI to achieve their business objectives.”

This relationship is underpinned by principles of trust, responsibility, transparency, and explainability. The foundation of this relationship is powered by Dun & Bradstreet’s trusted and proprietary data and innovative AI-informed solutions, and IBM’s decades-long work ushering powerful new technologies, including AI, responsibly and with clear purpose.

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