{"id":22129,"date":"2025-06-22T21:55:41","date_gmt":"2025-06-23T01:55:41","guid":{"rendered":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/?p=22129"},"modified":"2025-06-22T21:55:43","modified_gmt":"2025-06-23T01:55:43","slug":"ai-agents-help-professionals-but-only-with-their-grunt-work","status":"publish","type":"post","link":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/ai\/ai-agents-help-professionals-but-only-with-their-grunt-work.html","title":{"rendered":"AI Agents Help Professionals \u2014 But Only With Their Grunt Work"},"content":{"rendered":"\n<p><strong>Key Takeaways:<\/strong><\/p>\n\n\n\n<ul>\n<li>Professionals prefer AI agents for repetitive, low-stakes tasks but resist handing off judgment-heavy responsibilities.<\/li>\n\n\n\n<li>A disconnect exists between what AI experts say can be automated and what workers feel comfortable delegating.<\/li>\n\n\n\n<li>Respect for human agency and clarity around AI\u2019s role may be essential for sustainable adoption in the workplace.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p>Artificial intelligence agents continue to <a href=\"https:\/\/www.zdnet.com\/article\/ai-agents-win-over-professionals-but-only-to-do-their-grunt-work-stanford-study-finds\/\">advance<\/a>, capable of writing code, summarizing reports, and responding to customer requests in real time. But according to a recent Stanford <a href=\"https:\/\/arxiv.org\/abs\/2506.06576#link={%22role%22:%22standard%22,%22href%22:%22https:\/\/arxiv.org\/abs\/2506.06576%22,%22target%22:%22_blank%22,%22absolute%22:%22%22,%22linkText%22:%22study%22}\">study<\/a>, workers aren\u2019t eager to let AI take the wheel on everything. The message is clear: AI is welcome\u2014as long as it sticks to the boring stuff.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\">What the Study Found<\/h3>\n\n\n\n<p>The study surveyed over 1,500 professionals from various industries, asking which tasks they were open to automating and which they preferred to retain control over. The researchers also asked AI experts to rate which tasks they believed could technically be handled by autonomous systems.<\/p>\n\n\n\n<p>The contrast was stark. Most professionals supported using AI agents for low-complexity tasks:<\/p>\n\n\n\n<ul>\n<li>Drafting routine emails<\/li>\n\n\n\n<li>Taking meeting notes<\/li>\n\n\n\n<li>Sorting documents<\/li>\n\n\n\n<li>Collecting structured data<\/li>\n<\/ul>\n\n\n\n<p>But they were far less comfortable handing over tasks that involve human judgment, context, or creativity. That included decision-making in strategy, client communication, hiring, or legal analysis.<\/p>\n\n\n\n<p>The study found that while experts see greater automation potential, workers prefer to maintain authority in areas where nuance or accountability matter most.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\">Introducing the Human Agency Scale<\/h3>\n\n\n\n<p>To better understand the root of these preferences, researchers created a \u201cHuman Agency Scale.\u201d It measured how much control workers wanted to retain, even when tasks could be automated.<\/p>\n\n\n\n<p>In many cases, participants opted for more control than technically necessary. Even if an AI system could perform a task with high accuracy, workers still wanted final review or involvement.<\/p>\n\n\n\n<p>This suggests a strong emotional or professional connection to certain kinds of work\u2014especially those tied to expertise, responsibility, or reputation. And it reinforces a recurring theme: automation that undermines a worker\u2019s sense of agency is more likely to face resistance, regardless of performance gains.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\">A Use Case Divide<\/h3>\n\n\n\n<p>The results illustrate a practical way forward for companies building or adopting AI agents.<\/p>\n\n\n\n<p>Tasks with a clear structure, defined outcomes, and minimal human variability are ideal candidates for AI automation. These include:<\/p>\n\n\n\n<ul>\n<li>Scheduling logistics<\/li>\n\n\n\n<li>Inbox management<\/li>\n\n\n\n<li>Document classification<\/li>\n\n\n\n<li>Internal knowledge retrieval<\/li>\n<\/ul>\n\n\n\n<p>In contrast, professionals want human oversight on tasks involving:<\/p>\n\n\n\n<ul>\n<li>Strategic decisions<\/li>\n\n\n\n<li>Conflict resolution<\/li>\n\n\n\n<li>Client relationship management<\/li>\n\n\n\n<li>Personnel evaluation<\/li>\n<\/ul>\n\n\n\n<p>This divide doesn\u2019t mean AI is unwelcome\u2014it means its usefulness is task-specific. And understanding where workers draw the line is critical for adoption.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\">Implications for AI Builders and Buyers<\/h3>\n\n\n\n<p>The Stanford study has real implications for product designers, managers, and technology providers rolling out AI in the enterprise.<\/p>\n\n\n\n<ol>\n<li><strong>Start with the tasks people want help with.<\/strong> Instead of forcing agents into decision-making roles, focus on the tasks employees willingly offload. This improves adoption and trust.<\/li>\n\n\n\n<li><strong>Design for oversight.<\/strong> AI systems should enable human review\u2014not obscure it. Transparency and reversibility help users stay in control.<\/li>\n\n\n\n<li><strong>Support\u2014not replace\u2014judgment.<\/strong> The goal should be augmented intelligence, not autonomy for its own sake. Where judgment is involved, AI should be an assistant, not a substitute.<\/li>\n\n\n\n<li><strong>Manage expectations.<\/strong> Just because a model can technically perform a task doesn\u2019t mean it should\u2014especially in sensitive areas like healthcare, legal, or finance.<\/li>\n<\/ol>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\">A Measured Approach to Adoption<\/h3>\n\n\n\n<p>This study also reinforces a broader pattern emerging in AI deployment: early gains come from automating repetitive work, not replacing high-value expertise. It\u2019s a reminder that usefulness and acceptance are not the same.<\/p>\n\n\n\n<p>While AI agents have demonstrated impressive capabilities in lab settings, their integration into real work environments depends on social and organizational dynamics\u2014not just raw performance.<\/p>\n\n\n\n<p>Professionals aren\u2019t necessarily anti-AI. They\u2019re cautious, practical, and motivated by control, quality, and accountability. When AI helps them save time without sacrificing judgment, they embrace it. When it pushes into areas they consider core to their role, they pull back.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\">Conclusion<\/h3>\n\n\n\n<p>AI agents are advancing rapidly\u2014but the pace of workplace adoption will be shaped more by human preference than technical ability.<\/p>\n\n\n\n<p>The Stanford study underscores an important truth: people want help with the tedious parts of their jobs, not to be displaced from the meaningful ones. That means builders, leaders, and adopters of AI should focus first on the mundane\u2014automating grunt work while safeguarding the decisions, relationships, and creativity that people value most.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<p><strong>Le<em>arn how AI Agents can supercharge your company\u2019s profits and productivity at&nbsp;<a href=\"http:\/\/www.tmcnet.com\/\">TMC\u2019s&nbsp;<\/a><a href=\"https:\/\/www.aiagentevent.com\/\">AI Agent Event&nbsp;<\/a>in Sept 29-30, 2025 in DC.<\/em><\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image\"><a href=\"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-content\/uploads\/2025\/06\/ai-agent-event-logo.webp\"><img loading=\"lazy\" decoding=\"async\" width=\"1170\" height=\"630\" src=\"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-content\/uploads\/2025\/06\/ai-agent-event-logo-1170x630.webp\" alt=\"\" class=\"wp-image-20922\"\/><\/a><\/figure>\n\n\n\n<p><em>Rich Tehrani serves as CEO of&nbsp;<a href=\"http:\/\/www.tmcnet.com\/\">TMC<\/a>&nbsp;and chairman of&nbsp;<a href=\"http:\/\/www.itexpo.com\/\">ITEXPO<\/a>&nbsp;#TECHSUPERSHOW Feb 10-12, 2026 and is CEO of&nbsp;<a href=\"https:\/\/www.rt-advisors.com\/\">RT Advisors<\/a>&nbsp;and is&nbsp;a Registered Representative (investment banker) with and offering securities through&nbsp;<a href=\"https:\/\/www.4pointscapital.com\/\">Four Points Capital Partners LLC&nbsp;<\/a>(Four Points) (Member FINRA\/SIPC). He handles capital\/debt raises as well as M&amp;A. RT Advisors is not owned by Four Points.<\/em><\/p>\n\n\n\n<p>The above is not an endorsement or recommendation to buy\/sell any security or sector mentioned. No companies mentioned above are current or past clients of RT Advisors.<\/p>\n\n\n\n<p>The views and opinions expressed above are those of the participants. While believed to be reliable, the information has not been independently verified for accuracy. Any broad, general statements made herein are provided for context only and should not be construed as exhaustive or universally applicable.<\/p>\n\n\n\n<p><em>Portions of this article may have been developed with the assistance of artificial intelligence, which may have contributed to ideation, content generation, factual review, or editing<\/em>.<\/p>\n\n\n\n<p>Striking this balance isn\u2019t just good user experience. It\u2019s the foundation for long-term trust and success in human-AI collaboration.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Key Takeaways: Artificial intelligence agents continue to advance, capable of writing code, summarizing reports, and responding to customer requests in real time. But according to a recent Stanford study, workers aren\u2019t eager to let AI take the wheel on everything. The message is clear: AI is welcome\u2014as long as it sticks to the boring stuff.<\/p>\n","protected":false},"author":44,"featured_media":22130,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[194],"tags":[],"post_mailing_queue_ids":[],"_links":{"self":[{"href":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-json\/wp\/v2\/posts\/22129"}],"collection":[{"href":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-json\/wp\/v2\/users\/44"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-json\/wp\/v2\/comments?post=22129"}],"version-history":[{"count":1,"href":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-json\/wp\/v2\/posts\/22129\/revisions"}],"predecessor-version":[{"id":22131,"href":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-json\/wp\/v2\/posts\/22129\/revisions\/22131"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-json\/wp\/v2\/media\/22130"}],"wp:attachment":[{"href":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-json\/wp\/v2\/media?parent=22129"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-json\/wp\/v2\/categories?post=22129"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-json\/wp\/v2\/tags?post=22129"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}