The emergence of artificial intelligence in the workplace, particularly the rise of AI “agents” which can autonomously complete tasks previously performed by humans, represents a revolutionary leap forward.
Though AI won’t replace human thought, this new form of intelligence entering our economy at scale will dramatically change the world of work.
Though many have raised concerns that AI will cause mass layoffs, this shift is not a threat to be feared, but a force to be harnessed. Thriving in this new hybrid workforce, where human judgment is amplified by AI collaborators, requires a fundamental change in our mindsets, skillsets, and strategys. It requires moving from being a sole performer to an orchestra conductor, from a doer to a leader of a team that now includes both humans and intelligent machines.
Here’s how workers can thrive in the AI era of work:
Think like a manager
The most profound change required by the agentic shift is the move from being a doer to being a conductor of others, including yourself.
In the age of AI, there is a danger that AI becomes a black box that produces outputs for which no one feels fully responsible. To counter this, we must extend our sense of ownership to the entire process, including the tasks we delegate to our new AI collaborators. Your success is no longer defined by the code you personally write or the analysis you single-handedly produce, but by your ability to manage a hybrid team of human and AI resources to achieve a desired outcome.
As I’ve observed leaders throughout my career, the best ones are those who bring their team along, who seek to be surrounded by people smarter and more effective than they are. In this new context, your team now includes AI agents, and they must be guided and supervised with the same approach you would apply to a new, junior colleague.
Get creative
The second critical mindset shift is to view AI not merely as a tool for execution, but as a genuine thought partner—a collaborator that can help challenge assumptions, brainstorm novel solutions, and refine your own mental model. While AI excels at refurbishing existing knowledge, its true creative potential is unlocked by human curiosity.
AI models are trained on a vast corpus of existing human knowledge, and more and more, corporate knowledge. They are, by nature, backward-looking. But true, disruptive innovation comes from asking a question no one has asked before. Your role, therefore, is not to ask an AI tool to be creative. It is to use your own curiosity to pose provocative, non-obvious questions that force the AI tool to traverse its knowledge graph in new ways, revealing unexpected connections and sparking new possibilities.
Stay curious and current
Things are moving so fast in the AI space that the world can feel like it has changed week to week. In such a hyper-accelerated environment, continuous learning is not just a professional development goal; it is a fundamental, non-negotiable mandate for survival and success.
I’m reminded of Arthur C. Clarke’s short story, Superiority, which describes an arms race between two starship fleets. The story is about a technologically superior faction obsessed with developing a single, revolutionary new weapon, a complex space-time bender. While they struggle to perfect it, their adversary makes rapid, incremental iterations on existing, simpler technology and ultimately wins the war. The moral to me is clear: do not let the great be the enemy of the good, and do not become so dogmatic about a single new technology that you lose your adaptability.
The half-life of specific technical skills is shrinking dramatically. Mastery of a particular model’s API or a set of prompting techniques can become obsolete in a matter of months. Trying to master every new tool is a losing battle. The most durable and valuable skills are therefore not the “what,” but the “why”: the ability to critically evaluate new technologies in the context of the “job to be done,” to work backwards from the client’s needs, and to constantly iterate.
Build AI literacy and design for delegation
Effective delegation to AI requires a dual-pronged approach: clear communication on your part and a well-structured environment for the AI to operate within. The first part is about mastering the new language of work. I’ve found that large language models are extremely sensitive not only to the content but also to the tone of a prompt. A gentler, more empathic approach often yields more collaborative and meaningful answers. This is more than a curiosity; it suggests that the best prompters will be those who are the best communicators—clear, contextual, and considerate.
The second part is about designing your work for delegation. I’ve learned the hard way over many years that when it comes to system design, simplicity always wins at scale. A complex, messy system is difficult for humans to manage; for an AI agent, it’s a minefield. This is why building robust, standardized platforms is so critical.
This principle scales down to the individual level. An AI’s ability to assist you is constrained by what it can “see” and “do.” If your files are disorganized, your workflows are ad-hoc, or your code is messy, the AI tools have a very small and chaotic surface area to work with. Conversely, by adopting clean code, modular design, and clear documentation, you create a large, well-structured surface area for the AI tools to engage with. The act of organizing your own work is no longer just about personal efficiency; it is about designing an environment for effective AI delegation. The more scalable your personal setup, the more leverage you unlock.
Build your AI stack
The future of AI expertise lies not in mastering a single tool, but in orchestrating a suite of them. No single AI model will be the best at everything. Some will excel at coding, others at reasoning, and still others at data analysis or creative writing. The novice user will rely on a single, default tool. The expert will curate a personal toolkit of models and assistants, knowing which one to deploy for which task.
The most valuable work will come from your ability to conduct a multi-agent, multi-model workflow, synthesizing the outputs of specialized tools into a cohesive and superior whole. You can then become the master conductor of an AI orchestra.
Trust, but verify
A core leadership principle I’ve long held is to “trust and verify.” In the age of AI, this is not just a principle; it is an ironclad rule for survival. AI is a powerful collaborator, but it is fallible, and the human must always remain in the loop to review, test, and ultimately own the output.
Verifying AI output is not a simple spell-check. AI makes plausible-sounding errors—what we call hallucinations—that can only be caught by a true domain expert. In the age of Agentic AI, hallucinations translate into process errors, which, if left unchecked, can lead to potentially harmful actions. An AI might generate code that is syntactically perfect but contains a subtle, critical business logic flaw that only a skilled developer who understands the context would spot. It might summarize a financial report with perfect grammar while misinterpreting a key metric in a way only a seasoned analyst would detect.
This means that AI does not replace expertise—it demands it. The skill of verification is an active, cognitive process. It requires you to constantly ask: Does this make sense? What are the edge cases? What assumptions is the AI making?
A blend of deep domain knowledge and a detective’s healthy skepticism makes you the ultimate safeguard.
Be a systems thinker
To operate effectively in a hybrid human-AI workforce, you must zoom out and understand the broader system in which your work exists. This means thinking about not just your immediate task, but its downstream effects and potential failure modes. It’s about designing systems to reduce the “blast radius” of an incident, setting careful timeouts so you don’t starve resources, applying back-pressure to prevent cascading failures, and returning meaningful error codes.
This is the difference between speed and velocity. Speed is just magnitude of motion. Velocity is speed with direction and purpose. You can have teams operating at high speed locally, but without alignment to the broader system, the overall velocity can be low, or even zero. That applies to swarms of AI agents too. Without proper orchestration, your effective output can be zero. Systems thinking provides that alignment. It requires you to understand the “why” behind your work, not just the “how,” and to see the connection between your code and its ultimate commercial impact.
In an agentic world, this mindset has a new and urgent implication: every professional becomes a risk manager. AI agents can execute tasks at a scale and speed that is orders of magnitude beyond human capability. A single flawed instruction, a piece of bad data, or a misunderstood prompt can be amplified into a massive operational or reputational risk event almost instantaneously. Actions that were previously considered low-risk, like writing a simple script, become high-stakes when performed by an AI agent with broad permissions. Consequently, every knowledge worker must adopt a risk management mindset, constantly assessing the potential blast radius of their prompts and acting as a steward of their company’s data, controls, and compliance obligations.
Make AI your competitive edge
The strategic advantage of AI comes from a disciplined approach to its application. It’s about letting the technology handle the mundane and repetitive, freeing you to focus on high-impact, transformative work. To do this, you need a framework for prioritizing where and how to deploy AI tools.
Ultimately, the agentic shift is not a story about technology; it’s a story about humanity. By automating the “how,” AI liberates our most valuable and uniquely human talents to focus on the “why.” It amplifies the value of our judgment, our business acumen, and our purpose-driven work. AI can handle the “usual” work of processing data and executing tasks. This allows for the application of what one of our firm’s business principles calls the “unusual effort”—the above-and-beyond thinking in areas that truly differentiate us: building client relationships, exercising ethical judgment, and charting strategic direction. These are the domains where great leaders thrive. They are driven by a purpose greater than themselves. They listen, they tell inconvenient truths, they possess great instincts, and they are humble. These are traits AI can support with data, but never replicate.
This is why I remain an optimist. The future of work is not a dystopian one of replacement, but a collaborative one of amplification. It is a future where our success will be determined not by the raw power of our technology, but by the strength and wisdom of our humanity—our judgment, our purpose, and our unwavering commitment to building a future that serves our clients and our society. The playbook is in our hands.
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