Generative AI is everywhere. It has democratized data and accelerated the model-to-monetization cycle. Three out of four CEOs say their competitive advantage rests on it.
Companies at the forefront of generative AI adoption and data-led innovation—a group we call Generative AI Leaders (see Perspective, “Generative AI Leaders”)—are already reaping outsized rewards, reporting 72% greater annual net profits and 17% more annual revenue growth than peers. Momentum is spreading, with 92% of C-suite executives expecting to digitize their organization’s workflows and leverage AI-powered automation by 2026.
The challenge: while some organizations speed up, others can’t keep up. The widening gap between early adopters and hesitant businesses is creating a great divide—one in which organizations that struggle to embrace AI-driven solutions could lose ground in an increasingly technology-driven marketplace.
In response to these dramatic developments, the IBM Institute for Business Value (IBM IBV) has undertaken its most complex, far-reaching study on AI and automation. We surveyed more than 2,000 C-suite executives around the world, including Chief Automation Officers, about key strategies and investments as they advance intelligent workflows with AI and automation to improve connectivity and scale to value (for more information, see “Research and methodology” on page 27). We also highlight our Generative AI Leaders mentioned above. The discrete group is making critical investments in this advanced technology, enhancing AI and automation across their organizations.
Eight out of ten (82%) respondents overall agree that benefits from generative AI are worth potential risks. As all facets of society navigate this new terrain, it’s an opportune moment to investigate the impacts and potential that generative AI and automation create for organizations.
In the chapters that follow, we dig into four critical areas: data and preparedness; workforce talent and digital assistants; the IT opportunity; and investment priorities. Along the way, we share case studies of real-world impact. Finally, we present an action guide, with an 11-point blueprint for optimizing intelligent automation.
Generative AI is everywhere. It has democratized data and accelerated the model-to-monetization cycle. Three out of four CEOs say their competitive advantage rests on it.
Companies at the forefront of generative AI adoption and data-led innovation—a group we call Generative AI Leaders (see Perspective, “Generative AI Leaders”)—are already reaping outsized rewards, reporting 72% greater annual net profits and 17% more annual revenue growth than peers. Momentum is spreading, with 92% of C-suite executives expecting to digitize their organization’s workflows and leverage AI-powered automation by 2026.
The challenge: while some organizations speed up, others can’t keep up. The widening gap between early adopters and hesitant businesses is creating a great divide—one in which organizations that struggle to embrace AI-driven solutions could lose ground in an increasingly technology-driven marketplace.
In response to these dramatic developments, the IBM Institute for Business Value (IBM IBV) has undertaken its most complex, far-reaching study on AI and automation. We surveyed more than 2,000 C-suite executives around the world, including Chief Automation Officers, about key strategies and investments as they advance intelligent workflows with AI and automation to improve connectivity and scale to value (for more information, see “Research and methodology” on page 27). We also highlight our Generative AI Leaders mentioned above. The discrete group is making critical investments in this advanced technology, enhancing AI and automation across their organizations.
Eight out of ten (82%) respondents overall agree that benefits from generative AI are worth potential risks. As all facets of society navigate this new terrain, it’s an opportune moment to investigate the impacts and potential that generative AI and automation create for organizations.
In the chapters that follow, we dig into four critical areas: data and preparedness; workforce talent and digital assistants; the IT opportunity; and investment priorities. Along the way, we share case studies of real-world impact. Finally, we present an action guide, with an 11-point blueprint for optimizing intelligent automation.
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