Artificial Intelligence, Quantum Computing

IBM Think 2026 to Showcase Quantum Computing

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May 4, 2026 by Ivy Delaney

IBM CEO Arvind Krishna will open IBM Think 2026 on Tuesday, May 5th at 8:30 a.m. ET, outlining a vision for how artificial intelligence and quantum computing are reshaping the enterprise. The company’s annual conference expects to host over 5,000 senior business and technology leaders representing more than 80 countries, signaling broad industry interest in these rapidly developing technologies. Krishna is scheduled to unveil what IBM describes as his most comprehensive set of enterprise AI announcements to date, with a particular emphasis on transitioning organizations from limited AI testing into full-scale deployment. Drawing on IBM’s internal experiences, the conference will explore how hybrid cloud, AI, and quantum computing are converging to create new competitive advantages for businesses.

Arvind Krishna to Detail Enterprise AI and Quantum Convergence

This commitment extends beyond experimentation, aiming to demonstrate how businesses can actively deploy AI across all facets of their operations, data management, and decision-making processes. Sessions will focus on agentic AI, trusted data strategies, and automation, with a particular emphasis on achieving measurable results through real-world implementations. Client speakers including Dr. Serpil Erzurum, Executive Vice President, Chief Research and Academic Officer at Cleveland Clinic, and Ratnakar Lavu, Executive Vice President and Chief Digital Information Officer at Elevance Health, will share insights into their organizations’ adoption of these technologies. The conference agenda also includes live demonstrations of agentic AI managing complex enterprise workflows and presentations from IBM Research detailing progress towards achieving “quantum advantage.” Krishna will address the economic realities of AI implementation, outlining effective strategies and quantifying associated costs.

IBM Think 2026 Showcases Agentic AI, Hybrid Cloud Deployments

The convergence of artificial intelligence and cloud computing is a present reality for many enterprises, with a growing emphasis on translating pilot projects into scalable deployments. IBM Think 2026, commencing Tuesday, May 5 at 8:30 a.m. ET, arrives at a pivotal moment as businesses integrate these technologies to gain a competitive edge. This commitment includes demonstrating how organizations are embedding AI across operations, data, and decision-making processes, underpinned by a hybrid cloud infrastructure. IBM is showcasing examples of early adoption, including Aramco, Cleveland Clinic, and Elevance Health, all of whom are deploying AI and exploring quantum computing applications. IBM Research will present milestones in the pursuit of “quantum advantage,” suggesting a near-term horizon for commercially viable quantum solutions. The event’s sponsor list, including Adobe, Salesforce, AWS, and Google Cloud, reflects the collaborative ecosystem driving this technological shift and highlights the breadth of partnerships supporting IBM’s vision for the future of enterprise computing.

Drawing on IBM’s own transformation, Krishna will show how enterprises are embedding AI across operations, data, and decision making – and why hybrid cloud, AI, and quantum are converging into a new source of competitive advantage.

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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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