By Karthi Sepulohniam (Subramaniam), Head of Asia Pacific & Australia, Partners for Growth
Partners for Growth AI Series – Episode 5
Featuring Shannon Hautot , CEO & Co-Founder, and Scott Fox , Chief AI Officer & Co-Founder, HungryHungry
The best AI lessons come from the people shipping it.
This is the fifth instalment of our AI Series, where we sit down with technology leaders across our ecosystem to understand how they’re using AI in ways that are practical, non-obvious, and genuinely changing how they operate.
For this episode, I spoke with Shannon Hautot, CEO & Co-Founder, and Scott Fox, Chief AI Officer, at HungryHungry – a hospitality technology platform serving over 4,000 venues across 15 countries with online ordering, payments, kiosks, and marketing tools. HungryHungry has been a PFG portfolio company since 2020.
What made this conversation different from earlier episodes is that HungryHungry have built Otto – a customer-facing AI voice agent – and are deploying it into one of the most chaotic, low-tech environments you can imagine: a busy restaurant kitchen.
Chief AI Officer as a Signal
Scott Fox’s path to Chief AI Officer didn’t start in engineering. He came from operations – automating workflows with Zapier, building processes in HubSpot, running RevOps. When AI arrived, the role evolved naturally from enabling teams with tools to enabling teams with AI.
But Shannon made a deliberate decision to formalise the role with a title. Having a Chief AI Officer isn’t just an internal function – it’s a signal to the company, to customers, to partners, and to the market that HungryHungry is an AI-first business.
Day to day, Scott’s work has shifted from internal AI adoption to running Otto, the product that grew out of one of those early experiments.
Otto: The AI That Takes Phone Orders
Otto started with a simple observation from customers: restaurants can’t answer the phone fast enough during a busy service. Every missed call is a missed order. Every call that does get answered pulls a staff member away from the customers already in the venue.
HungryHungry had already built deep ordering integrations with point-of-sale systems – arguably the hardest part of the problem. Otto puts a voice layer on top of that existing infrastructure, allowing customers to call a restaurant and have an AI take their order, answer questions about the menu, or pass the call to a human if needed.
The idea is simple but making it work in a real restaurant is where it gets hard.
Why a Pizza Order Is Harder Than It Sounds
The complexity that Scott’s team deals with daily would surprise most people outside hospitality. Even a simple pizza restaurant presents a web of modifiers: size, crust type, gluten-free or not, swapping mozzarella for vegan cheese, adding toppings, removing toppings. Multiply that across a full menu and the permutations become enormous.
Early versions of Otto handled this by feeding the menu into a prompt – and the results were decent for simple orders. But as soon as customers started making modifications and asking edge-case questions, the system needed dedicated back-end tools: matching what the customer says to the correct menu item, verifying modifiers, confirming prices.
The core metric the team tracks is outcome success – did the caller achieve what they called for? If someone called to place an order, is there an order at the other end? That’s the metric that matters, not call duration or how human the AI sounded.
When the AI can’t handle something, it transfers to a human. Scott’s team tracks every transfer, uses AI to analyse the reasons at scale (they’re processing thousands of calls a week), and feeds those insights back into prompt and workflow improvements. It’s a self-reinforcing loop: AI improving the AI, similar to what we heard from Harrison.ai in Episode 2.
Don’t Sell AI – Sell the Problem It Solves
Shannon made a point that applies well beyond hospitality: restaurants don’t want technology for technology’s sake. If they had their choice, many wouldn’t use technology at all. They care about great food, great service, and a great environment.
So HungryHungry doesn’t lead with AI when selling Otto. They lead with the problem: how do you answer the phone dozens of times a night and still look after the customers sitting in front of you? Otto gives venues their time back to focus on what they actually care about.
It’s a framing worth noting for any company selling AI into a traditional industry: the technology is the mechanism, not the pitch.
What’s Next: Otto on the Kiosk
Otto is extending beyond the phone. HungryHungry are preparing to showcase a new kiosk at the Fine Food trade show in Melbourne, featuring an AI avatar built into the ordering screen.
Instead of navigating a standard digital menu, customers can ask the AI what’s popular, inquire about calories or macros, and get recommendations. When connected to a loyalty system, the AI can recognise returning customers and tailor suggestions based on their ordering history – whether they tend to stick with favourites or like to try something new.
Shannon’s vision is a personalised service experience that traditional hospitality staffing can’t deliver consistently. A different staff member might serve you every night. The AI remembers every visit.
Scott sees voice as the thread connecting all of it – phone, kiosk, potentially table-side ordering, even back-of-house communication between kitchen and floor. Voice, he believes, is just getting started in hospitality.
The Non-Obvious Use Case: Non-Technical People in the Codebase
Shannon added a perspective I hadn’t come across before: HungryHungry has started giving non-technical team members access to the codebase through AI coding tools. Their sales manager – someone with no coding background – is now one of the top three users by coding token usage across the company.
The next step is even more ambitious. Their head of customer success, who previously used AI for tasks within her comfort zone like querying HubSpot, is being given access to the code itself. The goal: she analyses the patterns across hundreds of support tickets each month, then goes into the codebase to identify where systemic fixes could reduce the workload for her team. She’s not writing the fix – she’s pointing the engineering team to exactly where the problem lives, with evidence.
Shannon’s analogy landed well: Canva democratised graphic design for the everyday person. AI is democratising software for the everyday person. Reading and writing code is no longer the barrier when AI can do it for you. Your AI is just an extension of you.
It’s a shift that pushes both sides out of their comfort zone – the non-technical people stepping into unfamiliar territory, and the developers opening up what has traditionally been their domain. Shannon thinks every organisation will be doing this within twelve months. Based on what we’re seeing across this series, he may be right!
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This is Episode 5 of AI: Where Innovation Meets Execution, a series where we profile how technology companies and investors across our ecosystem are deploying AI in practical, non-obvious ways. If you’re building something interesting with AI and want to be featured, reach out to us at karthi@pfgrowth.com.
HungryHungry: hungryhungry.com
Otto: callotto.ai
Featured companies may be clients or portfolio companies of PFG. No compensation has been provided for inclusion. This content is for informational purposes only and does not constitute investment advice or a recommendation.
The views expressed are my own and do not necessarily reflect those of my employer.
This content is for informational purposes only and does not constitute an offer to sell or a solicitation of an offer to buy any securities. Any such offer will be made only to qualified investors through confidential offering documents. All investments involve risk, including the possible loss of principal. Past performance is not indicative of future results.
