Disclaimer: The views expressed in this article are those of the author and do not necessarily reflect the views of the Economic Times – ET Edge Insights, its management, or its members

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Fueled by data, AI is transforming how businesses operate and deliver value, while improving efficiencies across the organization. With workflows becoming more intelligent, future outcomes can be shaped, interactions can be more personalized, and organizations can adapt at the speed of change to meet their customer needs. As a business, if you are already reshaping your business models in your digital transformation journey, then adoption of AI will be crucial. AI helps you unlock the value of your data and gives you the ability to design intelligent workflows that are predictive in nature, allowing you to make better decisions. Workforce resiliency, enterprise agility and cost reduction have all become top of mind for business leaders. According to “Covid-19 and the future of business” by IBM Institute of Business, 66 percent of surveyed organizations say they have completed digital transformation initiatives that previously met resistance.

Today’s AI is narrow. While many AI models deliver value in specific, well-defined situations, applying those same models to new challenges requires an immense amount of new data and training. Enterprises need AI that is fluid and adaptable, capable of applying knowledge acquired for one purpose to new domains and challenges. They need AI that can combine different forms of knowledge, unpack causal relationships, and learn new things on its own. In short, enterprises need AI with fluid intelligence — and that’s exactly what we’re building. Know more

But at the same time how do you make sure AI is trusted, reasonable and scalable ? The solution? An agile and resilient cloud-native platform that enables clients to succeed with AI irrespective of their unique data and cloud landscape, through capabilities such as ready access to data at its source and elimination of data siloes, DataOps to create trusted virtual data pipelines for self-service  and a unified set of data and AI cloud services that are open and easily extensible. Know more

IBM Cloud Pak for Data is a fully integrated data and AI platform that enables organizations to work through all of the steps of the analytics and AI lifecycle to collect, organize, and analyze data; then infuse AI throughout your business. Cloud-native by design and built on Red Hat® OpenShift® Container Platform, IBM Cloud Pak for Data takes advantage of its underlying resource and infrastructure optimization and management. The solution fully supports multicloud environments such as AWS, Azure, Google Cloud Platform, IBM Cloud® and private cloud deployments. Know more

IBM Cloud Pak for Data enables you to:

  • Automate how your business can build, run and manage AI models at scale.
  • Speed time to value with a single platform that integrates data management, data governance and analysis for greater efficiency and improved use of resources.
  • Reduce storage and resources costs by eliminating the complexities of integrating data and structures using powerful data virtualization techniques.
  • Accelerate insights to predict and shape outcomes and infuse intelligence into your workflows with a suite of pre-built applications to build, run and manage your AI through world’s leading AI technology powered by IBM Watson.
  • Bring all data personas to work together on a self-service collaborative team platform with a governed view of all data and AI workloads.
  • Deploy the platform that’s built on Red Hat OpenShift container, in almost any environment, whether on-premises or on the cloud. Know more

Get started

Register for a free trial of IBM Cloud Pak for Data.

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Read more about IBM Cloud Pak for Data

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Measure Outcomes: Read Forrester’s Total Economic Impact of IBM Cloud Pak for Data

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Read about What’s next in AI on IBM AI Research

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Read report from IBM Institute for Business Value : Covid-19 and the future of Business

Disclaimer: The views expressed in this article are those of the author and do not necessarily reflect the views of the Economic Times – ET Edge Insights, its management, or its members

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