What does it mean to be AI-native?
At Textio, we were first to market with AI for HR, so I've been thinking about how AI will reinvent work for a long time. Over the last couple of years, more and more products have been built with AI. In some cases, these are old products trying to bolt AI on to their legacy offering, but the most exciting new products are AI-native.
Like Textio ten years ago, these products are being created from the ground up with AI at the center of their experience. As these products take hold in the market, organizations are increasingly trying to rebuild their internal operations with AI at the center too.
For instance, most of the exec operator roles I'm hearing about these days (like HR, legal, finance, COO) are looking for someone to dismantle their current operations and rebuild with AI at the center. What's more, the roles are increasingly not just for people with traditional functional backgrounds but for people coming out of product and R&D. In other words, the race is on not just to build AI-native operations, but to find AI-native leaders to do the building.
But what makes a leader AI-native?
Using ChatGPT to write your LinkedIn posts does not make you AI-native 🔥
Every day, my LinkedIn feed is filled with people complaining about em dashes. "At least edit them out!" say these savvy posters. As though "bad AI use" is pasting stuff from ChatGPT into your LinkedIn post editor, and "good AI use" is doing that same thing but remembering to remove the em dashes first. This is not what it means to be AI-native.
There are five characteristics that imo define an AI-native leader.
#1: Table stakes: AI-native leaders use AI to accelerate their own thinking.
Plenty of people use Gemini or Claude or ChatGPT as a brainstorming partner. AI has tremendous value as a personal summarizer, pattern-finder, and question-asker. Many people engage AI in conversation the way they might engage a challenging and honest colleague.
While this is something that AI-native leaders do, many other people do it too. AI 101.
#2: AI-native leaders understand the metrics that define success in their functions, and they lead by applying these metrics.
Now we get into the interesting stuff.
Domain knowledge matters, and the most important domain knowledge is driven by metrics. In finance, this means you're optimizing for things like forecast accuracy. and changing your operations to achieve the metric. In talent acquisition, the metric might be time-to-hire or new hire performance 12 months into the job.
The foundation of being AI-native is understanding the metrics that matter, and constructing your system to rigorously measure and optimize for them. These are the metrics you will stay accountable to as you rebuild your system with AI at the center.
#3: AI-native leaders are builders with combo skills.
Yes, domain knowledge matters, but it actually isn't enough anymore.
In an AI-native environment, the killer combination is someone who 1. knows the domain and 2. has previously built technology. AI-native leaders build automation in to their systems from the beginning. In other words, constructing the right system in HR, finance, or marketing is now a technical skill, not just a functional skill.
People with product and technology backgrounds know how to build technical systems. Combine that with domain knowledge in a field like HR or legal, and that's a powerful combination. It will become an essential combination in the near future.
#4: AI-native leaders have a clear POV on data, privacy, and security.
It used to be that functional executives in fields like HR, marketing, and finance would rely on privacy lawyers and IT partners to identify data requirements and risks. While these experts continue to be important, AI-native leaders can independently define essential requirements and risks for their areas. They understand what needs to be proprietary and can think through the functional tradeoffs of sharing data outside the company firewall.
#5: AI-native leaders understand where agents work better than people, and where people work better than agents.
AI-native systems include both people and AI as workers. AI-native leaders understand which workers go best with which tasks.
In many disciplines, this means radically rethinking how work gets done. This means looking at every task in your current workflow and systematically asking what would be gained and lost if you swapped out the human worker for an AI one. This can be uncomfortable.
This is why it's so important to have clear metrics defined for your workflow; that's the objective yardstick you can use to answer the "human or AI" question, task by task. And of course when you look at the workflow end to end too, some tasks may go away entirely.
The bottom line: The new generation of AI-native leaders are fascinating hybrids. They both have deep operational domain knowledge and experience building technology products. Over the next five years, executives and operators at the most successful companies will increasingly take on this profile.
Kieran
I’m a former founder and CEO who helps ambitious leaders operate like one, whether or not they have the title. My coaching clients include startup CEOs, C-suite execs, and ambitious leaders inside large organizations, all leading with founder-level clarity, urgency, and ownership. Ask me about becoming a client!
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