Jon Harmeling automated 13 of his restaurant's 15 onboarding steps and freed up ten hours a week doing it. He owns and operates a Chick-fil-A in Miami Shores with about 90 people on his team, where only four or five of them ever touch a laptop, and that ten hours became a test case for a different question: what does freed-up time actually owe you?

In this conversation, Noah and Jon get into how a business built on shift work and thin margins decides what to automate first, the difference between using AI for management versus using it for leadership, and why the hardest part isn't building the tool. It's deciding, on purpose, where the time it saves actually goes.

Topics covered:

• Automating the repetitive admin work that eats a frontline operator's week
• Using AI to surface leadership moments, not replace them.
• Where AI hits its limits: why scheduling a team well requires human context no model has been able to replicate

Chapter List
(00:00) What do you do with the time AI gives back?
(01:07) Using AI when your team doesn't sit at a desk
(02:23) From West Point systems engineering to running a Chick-fil-A
(07:40) The first win: AI takes over 13 of 15 onboarding steps
(13:26) What not to feed AI, and how much to trust it
(17:07) Catching bad reviews while you can still fix them
(19:03) The setup: one laptop, no servers, one step at a time
(23:45) From management to leadership: AI that finds who's being overlooked
(28:09) Keeping AI-prompted moments genuine
(32:53) Where AI failed: scheduling needs human context
(36:36) Humans caring for humans: the retention flywheel
(40:44) Jon's book, "Frontline AI Leadership," and where to find him

Resources
Valet — Episode Sponsor
Valet is the founding sponsor of the Serious People Podcast.

AI is getting very good at making useful things: dashboards, analyses, internal websites, and small tools. Making those things available to the rest of a team is a separate problem.

Valet lets you publish AI-created artifacts to a stable URL. New sites are private by default, and members of your organization can access them after signing in.

Try Valet at https://valet.dev

Connect with Jon on LinkedIn: https://www.linkedin.com/in/jonharmeling
For more info on Frontline AI Leadership: https://www.jonharmeling.com/book/

Connect with Noah on LinkedIn: https://www.linkedin.com/in/noahlevin/
Noah's Website: https://www.noahlevin.com/
Serious People Website: https://seriouspeople.ai/

• (00:00) - What do you do with the time AI gives back?
• (01:07) - Using AI when your team doesn't sit at a desk
• (02:23) - From West Point systems engineering to running a Chick-fil-A
• (07:40) - The first win: AI takes over 13 of 15 onboarding steps
• (13:26) - What not to feed AI, and how much to trust it
• (17:07) - Catching bad reviews while you can still fix them
• (19:03) - The setup: one laptop, no servers, one step at a time
• (23:45) - From management to leadership: AI that finds who's being overlooked
• (28:09) - Keeping AI-prompted moments genuine
• (32:53) - Where AI failed: scheduling needs human context
• (36:36) - Humans caring for humans: the retention flywheel
• (40:44) - Jon's book, "Frontline AI Leadership," and where to find him

Christina Johnson has been shown a 33x return on ad spend and kept a straight face. She has sat with a retail media network selling hundreds of millions of dollars in advertising whose own sales team couldn't explain to buyers whether the money had worked. And she can point to a supplier that ran a flawless campaign in cities where the retailer has no stores.

Christina Johnson spent years inside Walmart's retail media business before founding Advisar, an intelligence tool for retail media buyers. In this conversation, she and Noah break down why so many brands are flying blind on their ad spend, why a metric like ROAS can look great while sales go nowhere, and what happens when AI gives you a confident, completely wrong answer.

If your business runs any kind of performance marketing, this episode will change how you look at your own numbers.

You'll hear:

• Why more ad spend rarely means better marketing
• How a "perfect" campaign can drive zero real sales
• What questions to ask before trusting a marketing metric
• Why AI can be confidently wrong, and how to catch it

Timestamps:
(00:00) More ad spend doesn't mean better marketing

(00:34) Meet Christina Johnson, founder of Advisar

(03:08) Why retail media is "wildly backwards"

(08:37) The meeting where the sales team couldn't explain their own numbers

(10:18) The question every retail marketer should ask

(11:49) The auto-bid campaign that spent $0 and sold nothing

(13:26) Why Christina built Advisar

(15:18) The "33x ROAS" that wasn't real

(18:11) ROAS explained, and why it can lie to you

(19:09) Using Claude for analysis, and where it breaks

(33:20) The AI mistake that changed how she builds

(39:31) One thing to do tomorrow with your ad spend data

Resources:

Valet — Episode Sponsor

Valet is the founding sponsor of the Serious People Podcast.

AI is getting very good at making useful things: dashboards, analyses, internal websites, and small tools. Making those things available to the rest of a team is a separate problem.

Valet lets you publish AI-created artifacts to a stable URL. New sites are private by default, and members of your organization can access them after signing in.

Try Valet at https://valet.dev

Christina Johnson operator field guide: https://seriouspeople.valet.run/spp-christina-operator-field-guide

Connect with Christina on LinkedIn: https://www.linkedin.com/in/christina-w-johnson

Advisars Website: https://www.advisar.io/

Connect with Noah on LinkedIn: https://www.linkedin.com/in/noahlevin/

Noah’s Website: https://www.noahlevin.com/

Serious People Website: https://seriouspeople.ai/

• (00:00) - More ad spend doesn't mean better marketing
• (00:34) - Meet Christina Johnson, founder of Advisar
• (03:08) - Why retail media is "wildly backwards"
• (08:37) - The meeting where the sales team couldn't explain their own numbers
• (10:18) - The question every retail marketer should ask
• (11:49) - The auto-bid campaign that spent $0 and sold nothing
• (13:26) - Why Christina built Advisar
• (15:18) - The "33x ROAS" that wasn't real
• (18:11) - ROAS explained, and why it can lie to you
• (19:09) - Using Claude for analysis, and where it breaks
• (33:20) - The AI mistake that changed how she builds
• (39:31) - One thing to do tomorrow with your ad spend data

Rebekah Stevens once spent months competing against an algorithm named Octopus, running weekly throwdowns to see who could staff a home care market better: her, with a spreadsheet and years of operating instinct, or a model built by her own data science team. She now leads Care Transformation at Honor, the senior home care platform, where that early contest became the blueprint for how the company decides what AI should touch, and what it shouldn't.

In this conversation, Noah and Rebekah get into how a business built entirely on trust and vulnerable moments decides where to let AI in. They cover why unstructured data doesn't automatically mean you need a language model, what it actually takes to turn a fuzzy client complaint into a precise, AI-ready metric, and why the hardest part of deploying AI is writing the goal correctly, not building the model.

You'll hear:

• How Honor defined the outcome before automating staffing decisions
• The real story behind Rebekah vs. Octopus, the human-versus-algorithm contest that built the system running Honor's staffing today
• How her team decided a language model was the wrong tool for a problem everyone assumed needed one
• Where Honor draws the line between AI working in the background and AI on the phone with a client

Timestamps:

(00:00) Introduction
(02:05) Inside Honor: senior care built like a tech platform
(10:06) Turning client churn into a math problem
(16:38) Rebekah vs. Octopus: human vs. algorithm
(23:53) Where large language models actually showed up in the product
(26:59) When not to use an LLM
(30:10) What's blowing her mind about agentic AI right now
(33:01) Directed Work: letting the system decide who does what
(45:01) How AI will reshape product teams next year

Resources:

Valet — Episode Sponsor

Valet is the founding sponsor of the Serious People Podcast.

AI is getting very good at making useful things: dashboards, analyses, internal websites, and small tools. Making those things available to the rest of a team is a separate problem.

Valet lets you publish AI-created artifacts to a stable URL. New sites are private by default, and members of your organization can access them after signing in.

Try Valet at https://valet.dev

Rebekah Stevens operator field guide: https://seriouspeople.valet.run/spp-rebekah-operator-field-guide/

Connect with Rebekah on LinkedIn: https://www.linkedin.com/in/rebekah-stevens-6751b41a/

Honor’s Website: https://www.honorcare.com/

Connect with Noah on LinkedIn: https://www.linkedin.com/in/noahlevin/

Noah’s Website: https://www.noahlevin.com/

Serious People Website: https://seriouspeople.ai/

• (00:00) - Introduction
• (02:05) - Inside Honor: senior care built like a tech platform
• (10:06) - Turning client churn into a math problem
• (16:38) - Rebekah vs. Octopus: human vs. algorithm
• (23:53) - Where large language models actually showed up in the product
• (26:59) - When not to use an LLM
• (30:10) - What's blowing her mind about agentic AI right now
• (33:01) - Directed Work: letting the system decide who does what
• (45:01) - How AI will reshape product teams next year