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Delivering superior price-performance and enhanced data management for AI with IBM watsonx.data

5 min read - Businesses have accelerated their use of AI in the past year. Gartner has projected that by 2026, more than 80% of enterprises will have deployed AI APIs or generative AI-powered applications in production environments—up from less than 5% in 2023. However, the ability of an enterprise to get value from AI relies on the availability and quality of its underlying data. To unlock the full value of data for AI, enterprises must be able to navigate their complex IT landscapes to enable…

Announcing IBM watsonx Assistant for Z and D&B Ask Procurement, new generative AI assistants built with IBM watsonx Orchestrate

5 min read - The business outcomes from scaling AI are clear. Companies at the forefront of generative AI and data-led innovation are seeing 72% greater annual net profits and 17% more annual revenue growth than their peers. In addition, it's expected that productivity will be improved by at least 70% across knowledge workers with the use of generative AI. A key step toward unlocking these gains is the adoption of AI assistants to transform how people get work done across the enterprise. With…

New strategic partnerships from IBM offer clients a wide range of enterprise-grade model choices

2 min read - As clients adopt generative AI (gen AI), they seek the right model choices, robust platforms to integrate AI into applications, and a reliable partner to help scale and implement AI with minimal risks. IBM watsonx™ addresses a wide array of business requirements and use cases specific to each enterprise department, domain and industry. Our new partnerships with Mistral and the Saudi Data and AI Authority (SDAIA), announced today at THINK, are a testament to this commitment. These collaborations aim to…

How to establish lineage transparency for your machine learning initiatives

3 min read - Machine learning (ML) has become a critical component of many organizations' digital transformation strategy. From predicting customer behavior to optimizing business processes, ML algorithms are increasingly being used to make decisions that impact business outcomes. Have you ever wondered how these algorithms arrive at their conclusions? The answer lies in the data used to train these models and how that data is derived. In this blog post, we will explore the importance of lineage transparency for machine learning data sets…

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