Drive Thru AI Has Grown Up

CES has a special talent.

It can make anything feel inevitable. Flying cars. Quantum fridges. And, last year, voice AI calmly taking fast food orders while the rest of the world quietly nodded and said, “Yes, of course this will work.”

Last year, drive thru AI felt exactly like that.

Voice everywhere. Clean demos. Confident scripts. The quiet assumption that the most chaotic, human, high-pressure moment in restaurant operations was finally solved.

We saw what everyone saw.

Then we did something deeply unfashionable.

We slowed down.

Because hype moves fast. Reality sends contracts. And lawyers. And invoices.

Over the last year, drive-thru AI was one of the most talked about subjects across CES, MURTEC, FSTEC, QSR Evolution, the NRA Show, and RFDC.

We saw and heard genuinely exciting things. Real progress. Better accuracy. Faster interactions. Clear momentum.

And then something subtle happened.

The questions matured.

The conversation stopped being “Can this work?” and became “What happens when this never turns off?”

What data is actually being captured. What is retained and for how long. Who owns it. Who carries liability. And whether the economics survive once the novelty wears off and the system has been listening to humans mispronounce menu items for eighteen hours straight.

That shift mattered more than any demo.

Over the past year, Inc Tank GTM went far beyond expo floors and pitch decks. We looked at what happens after installation. After rollout. After the honeymoon ends. After the renewal conversation gets quiet.

Cost structures. Hardware constraints. Security posture. Language nuance. Operational friction. Legal exposure.

And one question that quietly eats every other question for lunch.

Who is holding the risk when this scales?

Before going further, this needs to be said plainly.

Drive-thru voice AI is brutally hard. The teams building in this space are smart, motivated, and doing real work. This is not a teardown.

This is an explanation.

After more than a year of diligence, Inc Tank GTM chose Audivi as our best in category partner for AI drive thru voice ordering.

We make that choice once per category.

Anyone who has lived inside restaurant operations knows the rule.

Touch the drive-thru and you touch everything.

Revenue. Labor. Brand trust. Customer patience. And now, statutory liability.

The Carpenter v. McDonald’s litigation made this impossible to ignore. The court allowed claims to proceed based on allegations that AI drive-thru systems mechanically analyzed customer speech in ways that plausibly constituted the collection of voiceprints under biometric privacy law, even without traditional recordings or explicit identification intent.

Translation: capturing audio creates real problems if you don’t build correctly from day one, and no a sign up that says we record isn’t a get out of jail card.

Once AI processes voice at the drive-thru, you are no longer in software land. You are standing knee deep in biometric law, hoping no one notices your shoes are off.

And biometric law is not known for its sense of humor.

Illinois’ Biometric Information Privacy Act carries statutory damages that scale per violation, per person, with private rights of action and fee shifting. Texas law mirrors the posture with consent requirements, strict retention rules, and civil penalties that escalate quickly.

This is not theoretical exposure.

This is active risk.

Now layer in the economics.

Most drive thru AI systems are always on. Always listening. Always inferencing. Always consuming compute.

This is not idle SaaS. This is a system that never sleeps, never blinks, and never stops turning electricity into tokens.

Compute costs scale with traffic. Token usage compounds. Even at premium pricing, margins erode quietly.

This is where Warren Buffett becomes uncomfortably relevant.

Always on AI is a receding tide.

When unit economics do not work, behavior changes.

Data retention gets justified. Secondary use gets rationalized. Risk slides downstream to operators who did not sign up to be beta testers for someone else’s margin problem.

We were not willing to pretend that never happens.

Architecture decisions are not philosophical.

They are autobiographical.

Audivi’s discipline around real time processing, zero retention, and margin protection did not come from theory. It came from leadership that has lived inside systems where failure is not a tweet. It is a contract problem.

Jason Riggs , formerly General Manager at PAR Technology, understands enterprise QSR reality intimately. He has worked inside environments where uptime, renewals, franchise trust, and vendor accountability decide survival. That experience shows up in Audivi’s refusal to build a system that depends on perfect execution or pushes risk downstream and hopes no one notices.

Dr. Brent Field, a former Princeton professor with deep grounding in AI and statistical modeling, brings a different kind of restraint. In a category tempted to extract every possible signal, his influence is visible in what Audivi chooses not to collect. No stored audio. No transcripts. No identity level inference. Not because they cannot, but because they understand how quickly intent recognition drifts into biometric exposure and legal trouble.

Mark Dutton , Global Data Science Lead at Infosys, adds the scar tissue of large scale enterprise deployment. He knows what happens when models leave the lab and start living in noisy, messy, always-on environments. That perspective informs Audivi’s focus on predictable compute usage, controlled inference costs, and unit economics that improve with scale instead of quietly imploding.

Together, this leadership mix explains why Audivi behaves differently under pressure.

Compute costs stay sane because unnecessary processing is avoided. Legal exposure stays contained because data is not retained. Margins stay intact because the business does not rely on escalating token burn.

These are not features.

They are consequences of experience.

Supporting that leadership is a broader bench of PhDs and ABDs across artificial intelligence, linguistics, and applied data science focused on durability over novelty. Accuracy under noise. Performance under load. Behavior under real world speech, not curated datasets where everyone speaks politely and never interrupts.

Speed improves throughput. Margin sustains businesses.

Audivi consistently surfaces non PLU items, reinforces high margin modifiers, and improves attach rates humans miss when the line is long and the headset battery is dying.

AI does not forget to upsell. It does not panic during a rush. It does not bring a bad mood to the window.

The result compounds quietly.

Higher average check. Better mix. Revenue lift that accrues one transaction at a time.

All without introducing new legal or economic exposure.

At Inc Tank GTM, we operate by a simple rule.

One category. One product. One bet.

Not because others lack talent. Because operators deserve clarity, not hedging.

Audivi is our partner for AI drive thru voice ordering.

Full stop.

Audivi’s CEO summarized the partnership this way:

That promise is refreshingly unromantic.

Voice AI that works. That holds up under legal scrutiny. That makes economic sense at scale. And that does not quietly create problems someone else has to clean up later.

In a category still learning where its guardrails are, that alignment mattered.

This partnership is not about novelty.

It is about adulthood.

Responsible deployments. Sustainable go to market execution. Clear education around real AI tradeoffs. Helping the category grow up without burning itself down.

The market is done pretending.

So are we.

And that is why we are backing Audivi.