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’s potential, according to a presentation from a top executive in the space who advises Coins2Day 500 companies on strategy and tech adoption.
Allie K. Miller, the CEO of Open Machine, addressed the Coins2Day Brainstorm AI conference last week in San Francisco. Speaking from decades of experience at companies including IBM and Amazon 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.
The problem, Miller said, is that most users never get beyond the first mode, using AI as a “microtasker,” basically a glorified search engine, returning results for simple queries.
Her central critique focused on the rudimentary way that most employees interact with Large Language Models (LLMs). While traditional software (“Software 1.0”) 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.
“Ninety 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,” Miller said.
This roadblock is holding companies back from true productivity gains, added Miller.
“Your annual subscriptions are made worthless because people are stuck in this mode,” she said, implicitly encouraging organizations to rethink their AI investment budgets.
Miller’s ideas are backed with data. According to a November study from software company Cornerstone OnDemand, there is an increasingly split “shadow AI economy” thriving 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.
To unlock the actual value of enterprise AI, Miller’s presentation outlined a shift toward three more advanced modes: “Companion,” “Delegate,” and the most critical evolution, “AI as a Teammate.”
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’s software engineering agent Codex into Slack and treating it essentially as a coworker, she added.
While a “Delegate” might handle a 40-minute task like managing an inbox, the “Teammate” mode represents a fundamental shift in infrastructure. In this mode, AI is not transactional but ambient, “lifting up a system or a group and not the individual.” Miller predicted a near-future inversion of the current workflow: “We will no longer be prompting AI … AI will be prompting us because it will be in our systems and helping our team as a whole.”
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.
“The big difference for AI as a teammate is that AI is lifting up a system or a group and not the individual,” she added.
To bridge the gap between rewriting emails and deploying autonomous systems, the speaker introduced the concept of “Minimum Viable Autonomy” (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 “perfect 18-page prompts” and start treating it as goal-oriented software.
“We are no longer giving step-by-step perfect instructions … we are going to provide goals and boundaries and rules and AI systems are going to work from the goal backwards,” the speaker explained.
To operationalize this safely, the forecast suggested implementing “agent protocols”—strict guidelines that group tasks into categories: “always do,” “please ask first,” and “never do.” 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.
The Warning for the Next Decade
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.
However, these advancements come with a caveat for legacy-minded leadership. The veteran closed with a reminder that evaluating whether AI is “good or not” is the new essential product requirement.
“AI is not just a tool,” Miller concluded, “and the organizations who continue to treat it like one are going to wonder over the next decade what happened.”
