The average return on investment for corporate AI initiatives has nearly tripled since 2022, prompting CIOs to go beyond experimenting with AI to helping organizations realize scale and value quickly and with trust.

Many of these changes are technological: For example, how to manage rising complexity or generative-AI infused cyber threats. But many are cultural as well.

Learn how today's leaders are overcoming barriers like institutional change and lack of skills to turn early wins with AI into lasting competitive advantages. ​

Enterprises that possess high-quality data and governance, and attest to the trust-worthiness of their data among stakeholders, have doubled the ROI from their AI.

Learn how leading organizations are implementing an open and trusted data foundation to secure and maximize the value of their data, accessing siloed data across hybrid cloud, cost-optimizing growing data workloads, and preparing and delivering high- quality, governed data for AI.

Technology is advancing faster than ever, driving up both IT complexity and costs. Today, the average enterprise uses multiple clouds and thousands of physical assets while generative AI is predicted to bring about over 1 billion new applications.

Increasing complexity leads to inefficiencies, risk and waste, while limited visibility across disparate systems limit our ability to optimize processes, performance and tech spend.

Learn how IBM’s complete suite of AI-powered automation capabilities enable CIOs and their teams to build, deploy, maintain and fund critical technology investments

Organizations tasked with implementing generative AI in their workflows face challenges when it comes to inferencing costs, AI trustworthiness, domain-specificity, and being able to leverage enterprise data effectively and securely. This is why an open, trusted and targeted approach provides the best options for organizations looking to expand their footprint in AI.

Learn how to simplify and scale the development of AI applications through a comprehensive suite of developer-focused capabilities that integrates with your data, models, tools and enterprise systems

By the end of the decade, AI models will soon be composed of modules with different cognitive abilities, enabling them to exhibit behavioral norms empowering organizations with a broad variety of use cases that require effective human-machine collaboration.

Quantum computing will also have achieved an inflection point, significantly extending the complexity and size of workloads they are capable of handling. Learn about the intersection between these two emerging trends in computing and how your organization can prepare to take advantage.

By now, most of us have experienced virtual assistants of some kind. But what separates a prompt engine from an AI-assistant? Critically, it's the ability to do real work.

Learn how the latest AI assistants empower teams with needed expertise, turning piles of data into personalized insights and timely, automated actions; and using natural language understanding and machine learning techniques to automate business processes.

Scale AI that employees embrace because they can trust its outcomes, understand its rationale, and use and customize easily.

IT in the age of AI requires using all data, technology and compute resources across multiple clouds, on-premises and across edge-devices efficiently. But where to wisely invest for short-term and longer-term value is complex.

A “hybrid-by-design” approach to people, platforms and processes helps make IT an innovation center of the business. It enables consistency and standardization across environments while improving time to value of infrastructure investments. Meet the needs of growing AI data through improved accuracy and transparent governance—with performance levels you expect.