As a people-first industry, some within the hospitality sector have feared that technology will replace the human element of the experience. Others fear the potential disruption to service during new technology implementation, which is something operators simply cannot afford these days. However, with margins shrinking and costs continuing to rise, operators are progressively becoming more aware of the fact that digital transformation is a critical step needed to protect revenue and improve operational efficiency, among others.
As a result, AI is increasingly being adopted, yet implementation is uneven. Research from NIQ, powered by CGA and Sona, highlights that while almost half of the operators interviewed reported using AI for copy creation, emails and reporting, the percentages are much lower for marketing strategies (27%), stock optimisation (12%) and shift scheduling (27%). This disparity, perhaps, points to an uncertainty about how, and where, AI can provide the most measurable value. Arguably, it may also point to disjointed and rash implementation by some.
However, while it is important for operators to move quickly, a disjointed, fragmented approach to AI will not work in the hospitality sector. Instead, true return on investment will only be achievable if operators balance speed with a clear, long-term strategy.
To achieve this, operators must establish an understanding of what their digital landscape – or ‘world’ – looks like. This provides insight into the foundations that need to be developed or improved. With that in mind, here’s how operators can take a fast, but considered, approach to AI adoption.
Establishing the right foundations for AI
Many operators are feeling the pressure to move quickly in the face of disruption and spiralling costs. However, “moving fast” and “moving well” with AI and digitisation do not simultaneously go hand in hand.
Operators must start with a considered evaluation of the business’s entire digital system. What data exists, and where? What does that data tell the operator about their customers, suppliers, and their relationships? And, how does that all map into a system that defines what they do and how they do it?
This is where a business process and technology audit can help operators to gain an understanding of their systems. This includes the data they have, what is usable and digital and what is currently missing or offline. Operators can then establish a foundation for where data is consistent, connected and defined in a way that reflects how the business truly operates. This vital phase provides operators with the insight to develop an appropriate strategy to move forward.
The temptation to adopt technology without this critical step is where many can fall short. By adopting AI in individual, separate areas, the output is limited. For example, if it’s only connected to the booking system, the AI will simply be a reservationist. It won’t be able to communicate with staffing and scheduling platforms to truly impact shift performance.
Moving quickly in a disjointed fashion will only result in operators owning several different AI-enhanced tools which don’t speak the same language. The tools only become slightly more efficient in their own silo, and operators miss out on the significant benefits that properly mapped AI technology can provide – a stack that seamlessly shares data and learnings effectively.
How AI is holding open the front door
Once foundations are established and data communicates seamlessly, AI can then be adopted quickly across the business. Guest engagement is a great place to start.
AI can be adopted across digital communications, booking systems, guest communications and reviews. With AI helping to manage guest enquiries and bookings, teams on the ground can focus on delivering a great service that is supported with information shared by these systems. For instance, AI can organise reservations, pulling data from across the business (including multi-site brands) to optimise table allocations based on guest data, seating patterns and preferences, leading to improvements in efficiency and capacity.
By having AI analyse guest communications and reviews, the technology can identify upselling opportunities to increase spend per head, flag the guests most likely to cancel and send automated reminders to reduce no-shows. AI tools can detect feedback sentiment and context, then suggest responses to reviews, helping teams prioritise feedback and respond consistently.
In silos, the tools improve performance in singular areas. However, when AI can be joined up and communicate across all of these guest engagement areas, the value of all the tools compounds significantly. Each touchpoint informs the others, allowing teams to make better decisions around demand, guest preferences and revenue opportunities, whilst also reducing missed bookings and improving engagement before the visit.
The technology doesn’t have to be limited to front-of-house customer-facing touchpoints, however. Greater benefits can be unlocked if the data links and ‘speaks’ across both front-of-house and back-office traditional functions, with AI employed across the entire business.
Streamlining inventory management and service
Operators often struggle with forecasting and resource planning. After all, tracking stock levels accurately is a fine balance. Buy too little, and you risk running out and losing out on potential revenue. If you buy too much, you create waste, which also impacts a business’s bottom line.
AI can be used across back-office functions to address this. Predictive demand tools can analyse historical data alongside seasonal and marketing trends to forecast demand more accurately. This can identify top-performing items, refine menus and plan promotions with greater confidence, reducing waste while protecting revenue.
AI tools can also provide support with staffing and rotas. By having AI deployed across the entire business, data can be connected from reservations, expected demand and stock levels, allowing operators to plan rotas more effectively, ensuring teams are neither under- nor over-resourced throughout each shift in the day. Clearly, this considered approach with fast adoption across back-office systems – such as inventory management, staffing and ordering data – and front-of-house and guest engagement systems is where further advantages are found.
Conclusion
As AI adoption continues to grow, many operators will begin to see the benefits within individual parts of the business. However, if AI only sits within one platform, it will only ever function within that limited view. The moment it’s asked about other parts of the business without the right data, it will likely make assumptions and get things wrong.
Only by connecting systems properly can context be achieved. When connected, AI moves from optimising a single function to interpreting patterns across the wider operation, helping guide more effective service and business planning and execution. The impact of this is not about replacing the human side of hospitality, but strengthening it, allowing teams to be present on the ground, equipped with the insight they need to perform at their best.
Operators wishing to obtain this level of return must move quickly though. First, establishing a connected, wider view of the business. Then, once all systems are aligned, AI can quickly move beyond delivering small improvements, towards helping to create a more efficient, informed and responsive hospitality business that delivers consistently better guest experiences over time.
