Agentic AI — systems capable of autonomously pursuing goals and taking independent actions — has been heavily promoted by major tech firms like Salesforce and Oracle, with billions invested under the promise of streamlining operations and improving profitability. However, Gartner warns that much of this investment may not deliver the anticipated returns.
Hype vs. Reality
“Most agentic AI projects right now are early-stage experiments or proofs of concept that are mostly driven by hype and are often misapplied,” said Anushree Verma, Senior Director Analyst at Gartner.
Verma noted that the immaturity of current AI models, along with their limited capacity to navigate complex tasks or follow nuanced instructions over time, has led to disillusionment in many organizations.
“Most agentic AI propositions lack significant value or return on investment,” she said, adding that many implementations are failing to meet real-world business needs.
Rise of “Agent Washing”
The report also highlights a troubling trend of "agent washing" — where vendors rebrand traditional AI assistants or chatbots as agentic AI solutions despite lacking true autonomous capabilities. According to Gartner, while there are thousands of companies claiming to offer agentic AI, only around 130 have legitimate agentic functionality.
Adoption Still Expected to Grow
Despite these warnings, Gartner forecasts notable growth in the sector over the next few years:
- By 2028, 15% of day-to-day business decisions are expected to be made autonomously through agentic AI, up from virtually 0% in 2024.
- Also by 2028, 33% of enterprise software applications will include agentic AI capabilities — a steep rise from less than 1% today.
Why It Matters
The findings signal a reality check for enterprises and investors looking to capitalize on the AI boom. While agentic AI holds long-term promise, misaligned expectations, premature deployments, and underdeveloped tech threaten to derail progress in the short term.
Gartner’s guidance suggests organizations should approach agentic AI with strategic caution — investing in targeted use cases, ensuring clear business alignment, and steering clear of hype-driven deployments.
As agentic AI continues to evolve, the distinction between genuine innovation and marketing spin may prove critical to long-term success in the enterprise AI landscape.