Hiring an AI Agent: A Practical Guide
AI Agents are, unsurprisingly, one of the most talked-about concepts in the AI space today. But what do they actually do? Where do companies go wrong on this journey, and how should they get started? I wanted to share a few thoughts.
GPT talks. Agents get things done.
“What is an agent, and how is it different from GPT?” My answer is simple: GPT talks. Agents get things done.
GPT can list the best burger places around you. In other words, it connects data with intelligence and makes it conversational. An agent, on the other hand, will place the order for you, complete the payment, and make sure the burger arrives at your door. It understands the goal, builds its own execution plan, rethinks when it gets stuck, and keeps going until the job is done.
We’re essentially talking about a new kind of teammate—one that works alongside you, gradually understands the context of your work, takes ownership end-to-end, and produces results either for you or with you.
Where do agents create value in organizations?
Today, agents are most commonly used in repetitive, data-heavy, operational tasks. Customer communication and support, finance operations, sales operations, HR operations, production controls—areas that tend to exhaust people, are prone to error, and often don’t fully utilize human intelligence.
In one of our current client engagements, a team of 17 people is serving 200 customers. We’re transforming their operations into an agentic structure to unlock scalability. The key metric we’re tracking is simple: can the same team of 17 serve 500 customers instead of 200?
This is about both cost efficiency and workforce productivity—and it’s achievable. As agents take over time-consuming tasks, people can spend less time chasing reports and more time making decisions and creating value. Over time, the focus shifts from cost and efficiency to revenue and value creation. And that’s when the conversation changes entirely.
The biggest mistake companies make
A question I hear often: “What is the biggest mistake in AI transformation?”
Treating it as a one-off project.
This is not a digital transformation where you can say, “We digitized our processes, we’re done.” We are in the middle of a deep, tectonic shift. Just as global economic dynamics are changing—globalization giving way to localization—our ways of working are being fundamentally reshaped.
Until now, we designed work for humans. Now, we need to design work for humans and AI to collaborate.
And this is not a one-time change. It’s continuous. Some call it the “age of continuous disruptive innovation.” If this were just a digitalization effort, a multi-year program and a few tool selections might have been enough. But it’s not a technology problem. It’s about redesigning how work gets done—continuously.
The “Let me prepare the data first” trap
I hear this one a lot too. My answer is always the same.
Think about how you get the best out of a new hire. Do you say, “Go read our archives for months and start when you feel ready”? Of course not. You start working together from day one, teaching and improving through real work.
Agents are no different.
Remember, an agent is a new execution-oriented teammate. Treat it that way. Don’t wait to perfect your data structure before starting. Put agents to work as soon as possible and let them evolve with the work.
Who should own this transformation?
Definitely not just the CTO.
The CEO, CHRO, and technology teams must work together. One of the most common mistakes is assigning this entirely to the CTO. Yes, the CTO’s role is critical—but this is not just a technology initiative.
This is about how work gets done and how organizations operate. That’s why CHRO leadership is equally important. And since this is a matter of strategic survival, CEO ownership is essential.
When left solely to the CTO, what happens? Tools are selected, integrations are completed—but workflows are not redesigned, culture doesn’t evolve, and people don’t take ownership. It becomes “technology for the sake of technology.”
To build solutions that truly address real needs, business, technology, and culture must move together.
“Will AI take our jobs?”
Saying “no” is easy—but not entirely honest. I prefer a different perspective: AI is democratizing production. Just as the internet made access to information universal, AI makes creating and building accessible to everyone. That’s why Sam Altman’s statement resonates with me: “We will see one-person unicorns.” In other words, it will be possible to create massive output with far fewer people. From this perspective, AI is not eliminating work—it’s enabling more people to become creators and entrepreneurs. For established companies, this should be a wake-up call. You will face much smaller, much faster competitors. Instead of letting them emerge uncontrollably, it’s far smarter to build them yourself. Internal entrepreneurship is no longer optional—it’s critical. Without losing your workforce, enable your teams to build new solutions and ventures that can redefine your business.
Where should you start?
My answer is always the same: start with your biggest problem.
“Let’s do something with AI and see where we can use it” doesn’t work. When AI solves a real problem, it becomes sticky and sustainable inside the organization. Like all innovation, starting from a real problem accelerates both impact and transformation.
A 30-day starting framework
For those who want to get started in 30 days:
- Build a transformation leadership team
- Replace fear with curiosity—openly communicate your vision and drive broad engagement
- Develop AI capabilities across your workforce
- Select 1–2 high-impact workflows
- Redesign these workflows as agentic systems—and start
What about the first 90 days?
Don’t aim for big numbers right away like “we reduced costs by 30%.” Instead, aim for one simple feeling: “This agent genuinely made my life easier.” Once your team experiences that feeling, momentum builds naturally from within.
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