Optimizing AI Literacy to Enhance Food & Beverage (F&B) Operations

Optimising AI literacy in F&B operations starts with a simple principle: buying technology is not the same as building capability.

Too many operators still approach AI as a procurement exercise rather than an operational transformation discipline. The result is predictable — tools are purchased, dashboards are launched, but adoption remains shallow because frontline and middle-management teams were never properly prepared to use them with confidence, judgement, and consistency.

The implementation gap doesn't live in the technology. It lives in the human layer beneath it.

Closing that gap requires structured readiness, not enthusiasm alone. Leadership must first define where AI genuinely improves outcomes — forecasting, labour planning, menu engineering, cost control, guest communication — rather than deploying broadly without operational clarity. Teams then need practical, role-specific training focused on decision-making, not system features. And implementation must be supported by clear SOPs, defined accountability, and measurable KPIs, so adoption is tracked as rigorously as financial performance.

In hospitality and F&B, technology only creates value when people trust it, understand it, and know when to challenge it.

AI literacy is not a soft skill. It is an operational capability and, increasingly, a leadership responsibility that cannot be delegated to a vendor onboarding session.

Addressing the AI literacy gap in Food & Beverage, requires acknowledgement of a harder truth: the sector is generally under-teched and under-skilled across the board. While the broader hospitality industry has had decades to iterate, F&B is being forced to "grow up" into technology at an accelerated rate. It wasn’t long ago that table reservations at scale were considered a "strange concept," or that a Point of Sale was seen as the only tool required to operate.

We need to stop treating F&B like a simple service industry and recognize it for what it is: short-timeline manufacturing. There is nothing low-tech about a business requiring precision in demand forecasting and high-pressure production within minutes. AI is not an outlier; it is simply the latest piece of a digital reality that the workforce isn’t yet equipped to handle.

Ensuring AI adoption requires a cultural shift toward Competitive Advantage. We must:

Remove the "Black Box": Prioritize transparency so teams understand the why behind AI outputs.

Mentor Leaders: Shift operational mindsets to view technology as a strategic asset, versus an administrative burden.

Amplify Humanity: Leverage AI for transactional tasks, freeing teams for high-touch human connection that defines our industry.

We cannot build a high-tech future on a low-tech foundation.

AI only works if the team actually uses it as part of their daily routine.

If staff understand it, trust it, and see that it makes their job easier, it will deliver results. If not, it just becomes another tool that sits unused.

Belmond’s approach to AI in Food & Beverage is grounded in a clear belief that AI delivers value only when the underlying ecosystem is already connected. Rather than introducing AI as a standalone solution, Belmond has spent several years building an integrated F&B technology foundation designed to support intelligence at scale.

That foundation connects guest dining preferences and allergens, reservations, POS, purchasing, recipe management, and financial analytics. Guest profiles carry preferences and dietary requirements across platforms. Reservation data flows into POS to support service delivery. Items sold are linked to costed recipes, with automated data transfers from purchasing systems into menu engineering tools. Menu platforms update dynamically as ingredients or allergens change, improving accuracy, compliance, and guest trust.

With these building blocks in place, AI tools such as Copilot can now act as accelerators, not fixes. They help summarise performance, surface trends, support menu engineering, and make insight accessible to more operational leaders without replacing existing processes.

For Belmond, AI is not the strategy; it is the multiplier. The lesson for F&B technology leaders is simple: build the ecosystem first, then let AI amplify what already works.

Chefs rightly focus on their food craft, which is what customers value, but may not have the best use of written language compared to other departments, and may not have English as their first language. A run through with an IT Dept-approved Generative AI tool can give confidence to correct typos and grammar for menu design. Predictive AI (data pattern matching) has been embedded in scheduling tools for a while and can support budgeting, in the same way that it has been core to RMS tools for room pricing help.

In my Soho House days, we used RPA via UiPath and the SI Centelli, to automate menu updates. Oracle Simphony is the POS there, and it requires the use of Oracle's EMC, Enterprise Management Console, to make menu changes, including even basic price changes. EMC is a beast of an application, and a mistake can take out an entire menu structure. The IT support team initially made the changes, but that consumed about 20% of our effort. We used RPA to allow chefs to update simple spreadsheets, and then the RPA took that data, validated it, and pushed it into whichever constructions within EMC needed it.

Buying AI in 2026 will not fix broken operations if your SOPs and data architecture are still stuck in 2006. The real roadblock isn't just "literacy"; it's the "implementation tax" affecting capability. How can you expect a team to use AI to predict brunch orders for rosé if it isn't plugged into real-time data across systems?

Too many hotels keep their operating models static while technology evolves at breakneck speed. That is a recipe for failure. To achieve any "superhuman gains" that are possible with AI, you’re going to have to redesign your entire operating model around these tools first. Along the way, literacy will occur.

That’s why we don't just create technology; we create outcomes with our customers. We’ve built an ecosystem that helps customers accelerate the evolution of their operating models while still fitting their unique operational anatomy. We take care of the ordinary so your team can deliver the extraordinary. For instance, our job is to "tame the beast of AI" for you, moving past the clunky, rigid experiences within legacy tech. True adoption only happens when technology provides the cognitive ease that lets your staff stop fighting systems and return to the art of human empathy.

AI investments in F&B deliver value only when adoption is treated as seriously as the technology. The implementation gap is rarely caused by weak tools. It is usually caused by weak translation between strategy, tech implementation and frontline execution.

To drive real outcomes, technology and operations must partner closely and focus on a few practical disciplines:

1. Start with operational outcomes, not features. Each AI use case should map to measurable goals: reducing waste and stockouts, improving labor-to-sales alignment, strengthening menu profitability, elevating guest experience.
2. Make AI literacy role-based and workflow-native. General AI training isn’t enough. Managers and teams need practical guidance tied to daily decisions: what the system is recommending, why it matters, what action to take, and when human judgment should intervene. Particularly when local conditions change.
3. Build trust with transparency, guardrails, and feedback. Adoption improves when teams understand inputs and decision thresholds, know who owns exceptions, and see their feedback incorporated into continuous improvement.
4. Reinforce usage through operating rhythms and measurement. Embed AI outputs into weekly planning and daily routines, and track adoption (active use, recommendation acceptance, exception handling) alongside financial and service KPIs.

AI doesn’t improve operations on its own. Value is created when people trust the tools and apply them consistently. That is how AI moves from experiment to operating advantage.

AI literacy in F&B does not start with tools. It starts with understanding what the operation actually produces.

Most teams I see try to adopt AI before they can even describe what their workflow does step by step – mostly for the lack of SOP. The result is adoption without architecture. Then, the hyped AI tools, get used for isolated tasks but never compound into anything systematic. The shift that made the biggest difference for us at Nara was treating AI as an infrastructure decision, not a productivity shortcut. We did not ask how do we use AI to write better Instagram captions. We asked, "What does the guest journey need to look like across five brands for one team of three?" AI decisions followed from the answer, not the other way around. 

F&B operators who build this way end up with something the others do not: a system which AI output should get better the longer you use it as it takes into consideration your brand, guest touchpoints, actual voice, guest profile, and tone. That is AI literacy in practice. Not a certification. A design decision.