Before You Hire an AI Team, Ask These 5 Questions

Everyone wants in on AI.

But if you’re hiring before you’re ready, you’re setting yourself up to fail.

We see it all the time: a founder feels the pressure, throws up a job post for a machine learning engineer, and hopes for magic. But hiring before you’ve done the strategic work isn’t just inefficient — it’s expensive.

Here’s what the experts had to say at our recent AI hiring webinar:

1. What is the AI in service of?

“It’s rarely the right solution to just bolt on a chatbot to your app.”

— Hal Tily, Advisor at Plenty (ex-Netflix, Oura)

AI can be transformative but it doesn’t change your north star, filling that need your users come to you for. More than one company has told us they launched an interactive AI-based feature but stickiness was low: users went back to the old UX because it didn’t make anything new possible or much easier. If your AI strategy is just being able to say you have AI, this will happen to you too. 

“What is the AI in service of? Is it improving your user experience in a transformative way? Unlocking new workflows?”

— Hal Tily

2. Most of the work behind good AI features isn’t done by the AI engineers 

AI isn’t a plugin. If it changes how users interact with your product, you’ll need product, design, and engineering to deliver. We’ve seen too many clients realize this too late and leave fully developed ML models on the shelf for months while other teams try to catch up.

“If integrating AI means changing how your users interact with your product, you need companywide effort to redesign around it. Otherwise, it’s just stapled on — and it’ll fail.”

— Hal Tily

3.Your data might not be as ready as you think it is

Even if your data is working for your current uses, it might not be good enough for AI. One client ran an expensive, months-long data collection to hit the ground running with their first AI hire, only to find they left out critical features and had to start over. Have a domain expert check. 

“Make sure your data is clean and data eng support solid before hiring — otherwise, your expensive ML engineer is just PM-ing a data cleanup effort.”

— Hal Tily

4. Where can GenAI actually deliver value — today?

“Where it takes humans out of the loop… unstructured data cleanup, internal workflow automation — these are often overlooked.”

— Hal Tily

“If you build GenAI into workflows, like quota setting or meeting transcription, it adds leverage. But only if culture supports it.”

— Travis Nixon, Chief Data Scientist at Microsoft, ML Leader at Meta, and Founder of SynerAI

Translation: You don’t need to slap a chatbot on your homepage. The best GenAI use cases might be invisible — buried in backend systems, workflows, and unstructured data that no human wants to deal with.

5. Is your company’s culture ready to use the time AI saves?

“You can automate workflows, but if your culture responds by adding more meetings, you’ve missed the point.”

— Travis Nixon

That’s the punchline. AI can free up time and unlock speed — but only if your team is empowered to act on that leverage.

Bottom line?

Don’t skip the foundation.

📌 Get clear on your strategy.

📌 Have an ML expert check your data.

📌 Build workflows worth automating.

📌 Allocate enough resources form other teams.

📌 Then — and only then — make the hire.

Because AI done right adds leverage.

AI done wrong adds overhead.

Want the full playbook?

📹 Watch the webinar or DM us for the recap.

Or better yet — forward this to someone who’s hiring too early.