Here's What The Professionals Winning With AI Do Well ...
- Ram Srinivasan
- Jun 25
- 5 min read
Updated: 3 hours ago

The AI career shift is already underway. I’m seeing it firsthand inside Fortune 500 companies, where the focus has moved from tools to talent. The real conversation now is about the individuals creating momentum, building systems, and making AI part of everyday performance.
These professionals are stepping into a new kind of role. They are not defined by titles or technical stacks. They are defined by what they make possible when humans and machines work in sync.
I call them New Age Polymaths. Andy Kessler, in a recent Wall Street Journal article, calls them "High Agency Talent."
They have figured out how to make AI work for them, and they are becoming essential across functions. They no longer need deep coding expertise. They need curiosity, conviction, and tools that act as accelerants. They believe in positive change and can now build the change they want.
Three Capabilities That Define AI-Ready Talent
The people rising fastest today are not just learning how to use AI. They are shaping how their teams and organizations apply it. In my work, I consistently see three traits that set them apart:
They collaborate fluidly with AI in day-to-day workflows
They develop a sharp sense for data integrity and insight quality
They apply original thinking, domain experience, and creative judgment
These are not narrow skills. They are performance traits. A senior executive I work with put it well: “Our most valuable people know how to get results when the team includes both humans and intelligent systems.”
From Task Execution to System Design
Success now depends on more than technical contribution. The professionals creating impact are designing workflows, assigning responsibilities between tools and teammates, and adjusting systems as performance data comes in.
I hear this question from enterprise leaders all the time: “How do we find people who can lead AI just like they lead teams?” The answer is starting to take shape. It comes down to orchestration, situational awareness, and execution with intelligent systems.
In practice, this means knowing when to rely on human insight, when to use AI output, and when to swap models or methods. The people doing this well are not acting as engineers. They are directing systems, improving outcomes, and reducing inefficiencies across entire teams.
Microsoft's latest research captions them: the "agent-boss." A digital leader that autonomously manages workflows, enabling hybrid human-AI teams to work faster, smarter, and more efficiently.
Today’s AI‑ready professional designs workflows that delegate tasks to AI agents, overseeing systems end-to-end. This agent‑boss role reflects a shift from maker to orchestrator.
Deep Skills Still Matter
Soft skills have always played a role in leadership. Today, they need to be paired with technical fluency. Professionals leading AI initiatives must understand how systems function, where they can fail, and what design choices impact scale.
You don’t need to become a machine learning expert. But, you DO need to understand how models interpret inputs, how outputs are shaped, and where blind spots can emerge.
In every company I work with, the individuals earning trust and responsibility have this fluency. They don’t just use AI. They guide it. They have an intuition for it, built from application not from theory.
Continuous Learning Is the Edge
People ask me how to stay relevant as AI moves forward. The answer is simple. Learn actively and build personal systems that keep pace.
Mustafa Suleyman CEO of Microsoft AI recently noted that the biggest career accelerator going forward is to "get really, really good at learning." The individuals gaining the most from this shift are investing time into understanding how tools work, where they create lift, and how to improve them through experimentation.
Large organizations like Microsoft now treat AI use as a core competency. It’s built into performance metrics and career advancement, not a bonus, but a baseline expectation.
AI can take on routine tasks, but it only works well when humans define the process clearly. That handoff is becoming a strategic skill, and the return on learning is high.
My advice: dedicate a portion of your time and income to building your fluency. Try new tools, learn the basics of code and model behavior, and test your own workflows. Track what improves, what breaks, and what needs human oversight.
Next Steps for Professionals and Teams
If you are an individual contributor, start small. Choose one tool, apply it to a recurring task, and monitor the results. Build your system around speed, quality, and judgment.
If you are a leader, identify the people who work well across boundaries. These are the future AI directors inside your company. Give them room to build, test, and refine. Their output is often a leading indicator of team-wide transformation. If your teams are hesitant, leadership needs to signal that AI engagement is performance‑critical. Here are some useful links:
1. Build free custom agents here: Agent AI
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AI is already reshaping how performance is defined. Those who participate with curiosity and clarity are setting the pace. I see it in real teams, across real companies, in every industry I work with.
The momentum is here. Now it comes down to how we respond.
—Ram Srinivasan MIT Alum | Author, The Conscious Machine
Helping leaders navigate the edge of human performance and machine intelligence.
Published in HBR, Business Insider, Work Design, and MIT Viewpoints —
A Message From Ram:
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Disclaimer:
Ram Srinivasan currently serves as an Innovation Strategist and Transformation Leader, authoring groundbreaking works including "The Conscious Machine" and the upcoming "The Exponential Human."
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