In this episode of the Next Gen Builders podcast, host sits down with , VP of AI and Head of AI Innovation at Indeed, to discuss what it really takes to make AI work at scale inside a global enterprise.
The conversation covers how Indeed is helping its 11,000 employees get AI-ready, why small pilots matter, the dangers of chasing every shiny new tool, and the enduring importance of human judgment in an AI-driven world.
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The three lenses of AI at Indeed
Hannah starts the discussion by exploring her three distinct focus areas at Indeed:
- Macro: How AI is shaping the global labor market
- Product: How AI can improve the job-seeker and employer experience
- Internal: How AI can make work more productive and rewarding for employees
Her job? To connect the dots between all three.
“Job seekers and employers don’t want a bunch of AI hype—they want AI that’s actually going to make the product experiences better for them.”
From AI adoption to business impact
In 2023, the goal was simple: Get AI into people’s hands and see what sticks. But in 2024, the bar moved higher: AI adoption only matters if it moves the business.
“Are we saving people time? Are we improving the quality of decision making? Are we improving customer satisfaction scores, for example? AI is great—but why is AI great?”
The reality is that early usage almost always comes from a small group of power users. To get everyone else on board, you need training that’s specific to their role—not generic AI 101, but “Here’s how this tool makes your work easier today.”
Overcoming “too many tools” syndrome
Hannah has seen that resistance doesn’t always come from fear. Sometimes it’s due to choice overload.
So Indeed takes a different tack: Keep the door open for exploration, but provide a shortlist of approved tools, each with paved paths for support and training.
To paraphrase Hannah, “Some people want to experiment with everything. Others just want the one button that works.”
AI tool recommendations come from Slack channels, grassroots testing, and yes, the occasional “boardroom suggestion.”
Hannah’s team evaluates each one: What problem does it solve? Is it meaningfully better than what we have? Will it drive measurable results at scale? If the answer’s yes, they move fast. If not, it’s a training opportunity, not a procurement one
Building for a moving target
In AI, what’s state-of-the-art now will be outdated in months.
Indeed’s answer is the “Lego block architecture”—modular components (such as moderation, evaluation, memory, etc.) that can be swapped in and out as better options surface.
The trick is knowing when to switch and when to commit. Constantly chasing the latest and greatest burns time and resources. For Hannah, strategic patience wins.
“[The right choice we make for today] is almost certainly not going to be the right choice in three years. We all just need to be comfortable with that—we are architecturally optimizing for long-term flexibility.”
Humans + AI: The winning formula
AI should augment, not replace. Although this may end up being cliché, it is still undeniably true. Indeed supports over 600 million Job Seeker Profiles and 24 million jobs in more than 60 countries. Getting recruiting right matters—and human judgement is key.
Hannah laid this out for Francois:
- AI-only outreach = efficient but impersonal
- Human-only outreach = personal but slow
- AI + human outreach = high-quality candidates in multiples
AI does the heavy lifting, surfacing matches and drafting messages. The human recruiter then adds context and nuance. Together, they outperform either one alone.
“AI can go out and scan 600 million profiles in the job database. It can do the matching and comparison. It can think about what we know about the jobseekers’ wants, needs, profiles, and experiences. But at the end of the day, there is so much more in the recruiter’s brain, about what a great candidate for the role is going to look like.”
Change management > Cool tools
Hannah is blunt about what it takes for large-scale AI adoption: “Technology is less than half the battle.”
Leaders have to champion the change, but employees have to want to make it. That means creating safe spaces to experiment, sharing wins, and being transparent about what AI might mean for roles—all while equipping every employee with the skills to thrive alongside AI.
“We tell our employees that the world is changing; you’re going to need to be ready. And we’re going to make you ready. We’re going to put every single one of you through function-specific training. We’re going to give you best-in-breed tools. And we’re going to make sure that you are making this transition into this new, AI-powered economy with us.”
Talk about transparency.
Hannah’s “oh sh*t” moment
When running a venture studio, Hannah backed a startup that built an app for SNAP benefits. A few months in, New York City sent it a cease-and-desist letter.
- Lesson one: If you’re working on something that affects people’s ability to feed their families, “Move fast and break things” is not an option.
- Lesson two: In systems that aren’t going anywhere—like government, or a large company—success depends on making other people want to own the change.
Hear more from Hannah Calhoon
These are just a few insights that Hannah shared on Next Gen Builders. From rethinking training to designing for flexibility, her mental playbook for AI adoption is full of practical application—and plenty of good stories.
Tune in to the for more.
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