Employers are shelling out millions on artificial intelligence (AI) tools to boost productivity, but workers are still getting stuck using a tiny fraction of the tech\u2019s potential, according to a presentation from a top executive in the space who advises Fortune 500 companies on strategy and tech adoption.<\/p>\n
Allie K. Miller, the CEO of Open Machine, addressed the Fortune Brainstorm AI conference<\/a> last week in San Francisco. Speaking from decades of experience at companies including IBM<\/a> and Amazon<\/a> Web Services (AWS), she argued that AI actually has four different, increasingly useful interaction modes. Miller, who helped launch the first multimodal AI team at IBM, said that AI can be a microtasker, companion, delegate, or a teammate, depending on the desired outcome.\u00a0<\/p>\n The problem, Miller said, is that most users never get beyond the first mode, using AI as a \u201cmicrotasker,\u201d basically a glorified search engine, returning results for simple queries.<\/p>\n Her central critique focused on the rudimentary way that most employees interact with Large Language Models (LLMs). While traditional software (\u201cSoftware 1.0\u201d) required exact inputs to get exact outputs, AI allows for reasoning and adaptation. Mistaking the former for the latter adds up to a waste of your annual ChatGPT, Gemini, or other subscription, she argued.<\/p>\n \u201cNinety percent of your employees are stuck in this mode. And so many employees think that they are an AI super user when all they are doing is asking AI to write their mean email in a slightly more polite way,\u201d Miller said.<\/p>\n This roadblock is holding companies back from true productivity gains, added Miller.\u00a0<\/p>\n \u201cYour annual subscriptions are made worthless because people are stuck in this mode,\u201d she said, implicitly encouraging organizations to rethink their AI investment budgets.<\/p>\n Miller\u2019s ideas are backed with data. According to a November study from software company Cornerstone OnDemand, there is an increasingly split \u201cshadow AI economy\u201d\u00a0thriving beneath the surface of corporate America. The study found that 80% of employees are using AI at work, yet fewer than half had received proper AI training.\u00a0<\/p>\n To unlock the actual value of enterprise AI, Miller\u2019s presentation outlined a shift toward three more advanced modes: \u201cCompanion,\u201d \u201cDelegate,\u201d and the most critical evolution, \u201cAI as a Teammate.\u201d<\/p>\n By using AI through this interaction mode, the tech serves not as a reactive answer provider, but rather a collaborative partner that could be sitting in on meetings, fielding questions, as well as taking actions. Engineers at OpenAI are already doing this by incorporating the company\u2019s software engineering agent Codex into Slack and treating it essentially as a coworker, she added.<\/p>\n While a \u201cDelegate\u201d might handle a 40-minute task like managing an inbox, the \u201cTeammate\u201d mode represents a fundamental shift in infrastructure. In this mode, AI is not transactional but ambient, \u201clifting up a system or a group and not the individual.\u201d Miller predicted a near-future inversion of the current workflow: \u201cWe will no longer be prompting AI \u2026 AI will be prompting us because it will be in our systems and helping our team as a whole.\u201d<\/p>\n But even for non-AI companies, incorporating the technology in this way essentially makes it the foundation of the business tasks employees complete daily, making it more of a productivity booster than a stand-alone curiosity for trivia questions.<\/p>\n \u201cThe big difference for AI as a teammate is that AI is lifting up a system or a group and not the individual,\u201d she added.<\/p>\n To bridge the gap between rewriting emails and deploying autonomous systems, the speaker introduced the concept of \u201cMinimum Viable Autonomy\u201d (MVA), a spin on the old product-design principle of minimum viable product, or most market-ready prototype. This approach encourages leaders to stop treating AI like a chatbot requiring \u201cperfect 18-page prompts\u201d and start treating it as goal-oriented software.<\/p>\n \u201cWe are no longer giving step-by-step perfect instructions \u2026 we are going to provide goals and boundaries and rules and AI systems are going to work from the goal backwards,\u201d the speaker explained.<\/p>\n To operationalize this safely, the forecast suggested implementing \u201cagent protocols\u201d\u2014strict guidelines that group tasks into categories: \u201calways do,\u201d \u201cplease ask first,\u201d and \u201cnever do.\u201d The speaker recommended a risk distribution portfolio for these agents: 70% on low-risk tasks, 20% on complex cross-department tasks, and 10% on strategic tasks that fundamentally change organizational structure.<\/p>\n The Warning for the Next Decade<\/strong><\/p>\n The presentation concluded with aggressive predictions for the immediate future. The speaker forecasted that within months, AI will be capable of working autonomously for over eight hours uninterrupted. Furthermore, as costs drop, companies will move from single queries to running hundreds of thousands of simulations for every market launch.<\/p>\n However, these advancements come with a caveat for legacy-minded leadership. The veteran closed with a reminder that evaluating whether AI is \u201cgood or not\u201d is the new essential product requirement.<\/p>\n \u201cAI is not just a tool,\u201d Miller concluded, \u201cand the organizations who continue to treat it like one are going to wonder over the next decade what happened.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"Employers are shelling out millions on artificial intelligence (AI) tools to boost productivity, but workers are still getting…\n","protected":false},"author":3,"featured_media":452527,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[21],"tags":[691,6356,738,30959,8523,64061,242,158,62524,67,132,68,56211],"class_list":{"0":"post-452526","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-artificial-intelligence","8":"tag-ai","9":"tag-amazon-web-services","10":"tag-artificial-intelligence","11":"tag-ibm","12":"tag-machine-learning","13":"tag-natural-language-processing","14":"tag-tech","15":"tag-technology","16":"tag-the-future-of-work","17":"tag-united-states","18":"tag-unitedstates","19":"tag-us","20":"tag-workplace-wellness"},"share_on_mastodon":{"url":"https:\/\/pubeurope.com\/@us\/115733252470158453","error":""},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/posts\/452526","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/comments?post=452526"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/posts\/452526\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/media\/452527"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/media?parent=452526"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/categories?post=452526"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/tags?post=452526"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}