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Appetizer: QSR challenges

The AI wave has swept the Quick Service Restaurant (QSR) industry, with AI-related investments expected to increase by 38 percent over the next five years. To get the most value out of the AI hype, QSRs should strategically plan and prioritize solutions that address the pain points of the industry. 

WWT's work with leading QSR brands allows us to have a pulse on the biggest challenges facing the industry:

A list of QSR challenges including rising food costs, labor gaps, supply chain volatility, inconsistent customer experiences and cumbersome store development.

The power of data-driven analytics can play an important role in addressing the above challenges, from providing real-time visibility into financial and operational metrics to forecasting demand and managing the supply-chain process. Analytics can play a pivotal role in helping QSRs accurately measure performance, effectively cut costs and successfully enhance the consumer experience. 

A data buffet: Leveraging analytics across business functions and use cases

Analytics can be leveraged across different business functions and maturity stages, depending on the needs and AI-readiness of QSRs. The ideal approach is to look for quick wins and demonstrate incremental value without "boiling the analytical ocean." The exhibit below outlines a crawl-walk-run framework for planning data-driven use cases at QSRs.

Drawing from our experiences of working with industry leaders, we highlight two key successes below:

A slide that shows WWT's work with advanced operational business intelligence. We partnered with business functions to build an end-to-end pipeline that tracked daily transaction data across locations and menu item performance before visualizing the data in Power BI, enabling the leadership team to monitor business performance.
A slide that shows WWT's work with demand forecasting. We developed a sales forecasting algorithm powered by AI concepts to predict two grains of data: product-level predictions that provided insights into ingredients purchasing to optimize unit economics and Same Store Sales (SSS) predictions considering recent trends and sales momentum to guide corporate planning.

The secret sauce: GenAI for QSR innovation 

There likely hasn't been a board meeting in the past six months without the mention of GenAI and LLMs (Large Language Models), but what role does GenAI play in QSR operations? Instead of assuming the archetypical use case of a typical Chatbot, GenAI should be viewed as a versatile "jacket" that can be dressed on different analytics use cases to provide the business with greater efficiency, personalization and innovation. We see GenAI playing its most important role in four primary areas:

Setting the table: Data essentials for AI revolution

Even with the recent advancements with GenAI (e.g., ChatGPT), the principles of generating sustainable business value from data and AI remain unchanged. One key principle remains constant – analytics and AI are at the mercy of the data they are fed, and so to embark on an AI journey that is quick but also sustainable in delivering the ideal outcomes, clients need to establish a strong and continuously enhanced data foundation. Building a unified data platform – short for an integrated set of processes, people and technologies supporting an organization's collection, storage and access to data – should be the focus of investment in addition to pursuing quick-win use cases. 

The data essentials for AI revolution include preparing and processing (pre-requisites) before applying AI.

WWT has extensive experience in laying these data foundations for clients. To explore more of our practice's insights into different data capabilities, follow the links below:

Data PreparationData Preparation FrameworkPreparing Data for AI

Data GovernanceData Governance Case Study for QSRData Governance & GenAI Success

Data Analytics and Visualization6 Basic Steps of Data Analytics, Data-driven Decision & Visualization

Monitor the check: Buy versus build

Given the range of tooling advertised in the market, framing an AI strategy centered around business outcomes is now more crucial than ever. Planning should identify business problems and opportunities before deciding on technological solutions that can transform the organization. Prioritization should involve estimating expected complexity, potential business value and the cost of full ownership of each solution.

Once the use cases have been decided, the next step is to align on the right solution. QSRs should look for the least complex solutions that can address 90 percent of the problem. All business opportunities and data-driven solutions should be viewed holistically to understand how they will collectively transform the business. 

With the rapid increase in off-the-shelf products available, a typical decision point we see customers reach is deciding between off-the-shelf solutions and hyper-custom solutions built from scratch. We usually see the sweet spot somewhere in between – a "Practical AI middle ground" - that balances both precision and speed-to-outcome. More details are outlined in the exhibit below: 

WWT's Practical AI approach prioritizes precision and speed-to-outcome. This approach recommends buying where available and building what is necessary.

Last bite before you go

While the AI revolution in the QSR industry is undeniably exciting, it is crucial to recognize that the AI journey is a complex and demanding one. Given the varying sizes, technical capabilities and strategies of each QSR, there is no "one-size-fits-all" AI solution. The methodologies discussed in the article are distilled from our extensive work with clients across various industries, where we have witnessed consistent success with our tailored AI solution implementations. Our team of industry experts and consultants leading AI innovation remains committed to partnering with organizations to help them not just understand but thrive in the dynamic landscape of AI. 

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