IBM is expanding its collaboration with Microsoft to help joint clients accelerate the deployment of generative AI – and deliver a new offering that will provide clients with the expertise and technology they need to innovate their business processes and scale generative AI effectively.
The partnership will focus on helping clients to implement and scale Azure OpenAI Service. The new IBM Consulting Azure OpenAI Service offering, which is available on Azure Marketplace, is a fully managed AI service that allows developers and data scientists to apply powerful large language models, including their GPT and Codex series. It aims to help businesses define an adoption strategy and an initial set of specific and value-add generative AI use cases.
“Businesses are looking for responsible ways to adopt and integrate multi-model generative AI solutions that augment the work their teams are doing in areas such as creative content and code creation, content summarization and search,” said Francesco Brenna, Global VP & Senior Partner, Microsoft Practice at IBM Consulting. “Our work with Microsoft is another example of IBM’s open ecosystem model designed to bring value to clients while helping them responsibly build and scale generative AI across their businesses.”
As part of the new solution, enterprise customers will also have access to IBM Consulting experts, including 21,000 data, AI and experience consultants, who can help them effectively implement generative AI models to advance their business transformation.
IBM Consulting takes an open and collaborative approach to plan, build, implement and operate generative AI solutions that embrace multiple models on multiple clouds from industry leaders.
An open ecosystem approach helps clients define the right models and the right architecture to deliver the desired outcomes. As part of this open approach, IBM Consulting works with clients across industries to assess their generative AI readiness, define the right strategies for their business and help them implement and responsibly govern generative AI in production.
Getting to enterprise AI at scale requires a human-centric, principled approach, and IBM Consulting helps clients establish guardrails that align with the organization’s values and standards, mitigates bias and manages data security, lineage and provenance.
To help clients prepare data to fuel their generative AI models, select IBM AI technology is currently available on the Azure Marketplace and can be deployed on Azure. Together we’re enabling clients to accelerate the impact of generative AI using their trusted data.
This work builds on recent momentum with IBM and Microsoft to help clients transform their businesses. IBM Consulting, which has a dedicated global practice focused on Azure Data and AI, has focused on training its consultants, who now have over 40,000 Azure certifications. Additionally, IBM Consulting brings expertise and capabilities to help Microsoft clients through its acquisition of Neudesic, which specializes primarily in Microsoft Azure.
“Together, Microsoft and IBM are collaborating to deliver innovative solutions, that will help customers responsibly accelerate deployment of generative AI,” said Dinis Couto, GM Global Partner Solutions, Microsoft. “As a leader in the delivery of generative AI and data solutions, we believe that partners like IBM are critical to enabling customers successful use of generative AI to advance business transformation.
IBM Consulting recently announced its Center of Excellence for generative AI, which includes more than 1,000 consultants with specialized generative AI expertise ready to help accelerate its clients’ business transformations with enterprise-grade AI, including technology from Microsoft, IBM and other ecosystem partners.
The Center of Excellence stands alongside IBM Consulting’s existing global AI and Automation practice and leverages proven methods like the IBM Garage for Generative AI, where IBM consultants apply a comprehensive, collaborative method to help clients fast-track innovation in the emerging category of foundation models for generative AI. That includes rapid use case ideation and prioritization, an open, multi-model approach to selecting architectures and training, as well as fine tuning and scaling models to unique business needs.