AI agents are performing roughly 1 in 3 actions in enterprise IT workflows (but that share is rising quickly), while human analysts are rejecting about one-quarter of AI-proposed actions (but that rate is falling), according to a new study of tens of thousands of human-AI interactions. Operational data, rather than underlying AI infrastructures, is often the culprit when things go wrong.<\/p>\n
Human analysts are approving the most consequential actions, managing exceptions, and supervising and shaping agentic systems, while AI agents are carrying out routine tasks and executions, automation platform provider Fixify found in the study.<\/p>\n
\u201cThat may sound less dramatic than replacing the help desk,\u201d Matt Peters, Fixify\u2019s co-founder and CEO, wrote in a blog post. \u201cIt\u2019s also a much more credible path to changing how IT work gets done.\u201d<\/p>\n
Fixify identified four steps of agentic work: Planning, proposing, approving or declining, then acting on approved steps.<\/p>\n
It analyzed nearly 18,000 plans and over 147,000 actions executed by agents across 40 companies over a three-month period, finding that agents are taking over one-third of IT actions, most notably in software, applications, security, and collaboration work where requests tend to be \u201crepeatable and easy to reverse.\u201d<\/p>\n
Tasks that are well understood and that present low risk are best suited for the current generation of agents, Peters wrote. Human analysts remain closely involved in higher-stakes areas like identity verification, setting up and removing IT access (onboarding and offboarding), and hardware environments.<\/p>\n
However, AI\u2019s share of the work is increasing as feedback loops improve: Over the three-month period, human approval of AI-proposed actions rose from 23% to 41%, and rejection fell from 27% to 16%, Fixify found.<\/p>\n
The company identified six types of actions in AI automation. Running a skill \u2014 actually doing something \u2014 accounted for 39.4% of all actions). Most of the rest were coordination: sending a message to the human requester (27.7% of actions), leaving an initial comment (13.2%), giving instructions to a human analyst (9.8%), or waiting (8.8%). Running entire workflows accounted for just 1.1% of actions.<\/p>\n
AI is building \u201cscaffolding\u201d that wraps around meaningful changes, often planning far more scenarios than the agent will execute. Typically, agents map out 15 possible actions but run only two, Fixify said.<\/p>\n
\u201cThe agent maps the paths a request could take, then walks down the path that makes the most sense as it meets reality,\u201d the study said.<\/p>\n
Peters pointed to one example where an AI agent identified which team needed access to process a high-volume type of ticket. Rather than fully automating the process, the agent did the initial triage, asked questions, then routed tickets to the team that had the information to act immediately.<\/p>\n
\u201cWe didn\u2019t need a world-ending hive mind,\u201d he said. \u201cWe just needed to point a little conversational intelligence in the right direction.\u201d<\/p>\n
IT automation typically involves analyzing tickets and moving them along; in other words, low-risk tasks.<\/p>\n
But agents do participate in areas like security (albeit only about 6%), most notably adding and removing people from groups or channels, unlocking accounts, resetting passwords, analyzing multi-factor authentication (MFA), provisioning (or deprovisioning) accounts, and assigning software licenses.<\/p>\n
However, this identity-lifecycle work is where agents failed the most, particularly in onboarding and offboarding and identity-access management (IAM), the study found. \u201cHardware and connectivity changes rarely fail; identity-lifecycle changes fail three-to-nine times as often.\u201d<\/p>\n