IBM Study Shows That AI Agents are Viewed as EssentialIBM Study: Businesses View AI Agents as Essential, Not Just Experimental
IBM announced that their new study by the Institute for Business Value reveals that enterprises are expected to significantly scale AI-enabled workflows, many driven by agentic AI, relying on them for improved decision making and automation. The AI Projects to Profits study, which surveyed 2,900 executives globally, revealed that respondents expect AI-enabled workflows to grow from 3% today to 25% by the end of 2025. With 70% of surveyed executives indicating that agentic AI is important to their organization's future, the research suggests that many organizations are actively encouraging experimentation. As the pace of digital transformation accelerates, enterprises can turn to AI agents as the next evolution of intelligent automation. 83% of respondents say they expect AI agents to improve process efficiency and output by 2026, and 71% believe agents will autonomously adapt to changing workflows. "We see more clients looking at agentic AI as the key to help them move past incremental productivity gains and actually gain business value from AI, especially when applied in their core processes like supply chain and HR," said Francesco Brenna, VP & Senior Partner, AI Integration Services, IBM Consulting. "This isn't about plugging an agent into an existing process and hoping for the best. It means re-architecting how the process is executed, redesigning the user experience, orchestrating agents end-to-end, and integrating the right data to provide context, memory, and intelligence throughout." These are the top five benefits of agentic AI systems that are driving adoption across industries, according to the report:
Though benefits were cited among those surveyed, concerns with agentic AI adoption are still present among leaders. Those surveyed indicate that concerns around data (49%), trust issues (46%) and skills shortages (42%) remain barriers to adoption for their organizations. Other key findings include:
Source: IBM media announcement |