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Home > Consumer >  Foodservice >  Artificial Intelligence (AI) in Foodservice – Thematic Research

Artificial Intelligence (AI) in Foodservice – Thematic Research

Incumbents in virtually every industry face disruption from AI, and those that fail to make AI a priority will risk extinction. The foodservice industry is finally beginning to realize the benefits this technology can bring to the sector across the value chain. The market for AI in foodservice is rapidly growing, with GlobalData forecasting that AI platforms revenue alone will reach $753m by 2024, growing at a CAGR of 22% from 2019.

AI has many sector-specific use cases that can help tackle some of the sector's long-term challenges, from predicting consumers’ tastes to reducing costs.

AI can improve customer engagement via conversational platforms, help businesses understand consumer demands using machine learning, and improve personalization using data science. Furthermore, Computer vision, which attempts to replicate the way humans see and understand the world, can help perform quality checks and minimize food waste.

This report explains AI’s numerous technologies, analyses sector-specific applications, and provides an in-depth understanding of how AI is disrupting the foodservice industry. As well as providing essential insights for vendors and other industry stakeholders it enables foodservice companies to identify how they compare to competitors in AI adoption, the areas of their business most in need of AI, the specific services that will help and the strategic partners needed to make their vision a reality.

Scope

Global market size and growth forecast (2019-2024) for AI platforms in the foodservice sector.

Briefings on AI’s seven most important technologies: machine learning, data science, conversational platforms, computer vision, AI chips, smart robots, and context-aware computing.

Analysis of how different AI technologies contribute to solving sector-specific challenges, including targeting growth, predicting consumers’ foodservice preferences and expectations, and adapting to changing business models.

Case studies, supported by executive interviews, analyzing concrete sector-specific applications of AI including Starbucks’ use of machine learning to develop a personalization engine and Domino’s computer vision pizza checker.

GlobalData’s thematic sector scorecard ranking the activity on foodservice companies in AI and other vital themes disrupting their industry. This is informed by GlobalData’s comprehensive tracking of AI-related deals, job openings, patent ownership, company news, financial and marketing statements.

Key Highlights

The foodservice industry has historically been slow to adopt emerging technologies relative to other industries. A 2020 GlobalData survey on emerging technology trends revealed that the foodservice sector was the least ready for technological disruption, with 35% of respondents admitting to being unprepared. However, over half of the executives surveyed were planning on increasing investment in disruptive technologies such as AI.

Investment in AI will primarily come from food aggregators, delivery companies, and large quick-service restaurants (QSRs) that have both the financial capabilities and datasets necessary for success. McDonald’s, for example, acquired Apprente and Dynamic Yield, while Starbucks and Deliveroo developed their own in-house capabilities.

COVID-19 has accelerated innovation in foodservice, forcing businesses to get creative and transition more quickly. While AI applications in the foodservice sector had previously focused on targeting growth, the attention has shifted to hardwiring business agility into companies’ business models. Whether it is forecasting demand, being smart in terms of menu choices, or automating processes, foodservice companies that successfully deploy AI will acquire a competitive edge that will help them both in the short and long term.

Reasons to Buy

Prioritize investments in the areas of AI, which will deliver the best results for your business. Informed by GlobalData’s technology and foodservice analysts, we also provide recommendations on the area of the value chain you should focus on and highlight the parts you can confidently ignore.

Support your internal business case for investment in AI by including an independent view of how you rank against competitors in this area, examples of competitors who have made these investments, and case studies of successful relationships in foodservice. This includes Domino's partnership with Dragontail and Starbucks' partnership with Microsoft.

Efficiently source AI specialists and potential partners by accessing a long list of over 20 vendors including Microsoft, Google, and Dragontail Systems, a straightforward summary of their specialisms, and information on existing relationships with competitors in your industry.

Develop marketing messages and value propositions for your AI services that will resonate with prospective clients in the foodservice industry by uncovering the business challenges they face in targeting growth, predicting customers’ preferences, and transitioning to digital business models.

Quantify the global sales opportunity for AI services to the foodservice industry by accessing GlobalData's market size and forecasts (2019-2024), produced by our foodservice and technology analysts.

Companies Mentioned

Aramark, Autogrill, Brinker, CFA Properties, Chipotle, Compass, Darden Restaurants, Deliveroo, Dine Brands Global, Doctor’s Associate (Subway), Domino’s, Dunkin’ Brands, Haidilao, Inspire Brands, Jollibee Foods Corp, McDonald’s, Restaurant Brands Int (RBI), Seven & I Holdings, Sodexo, Starbucks, Swiggy, Uber, Wendy’s, Whitbread, Yum! Brand, Alibaba, Alphabet, Amazon, Apple, Baidu, Cambricon, Facebook, IBM, Intel, Microsoft, Nvidia, Tencent, 7Shifts, Dragontail Systems, GoParrot, Keenon Robotics, Kerry, Medallia, Miso Robotics, Orbisk, Picnic, PosIQ, Say2Eat, Tastewise AI, Valyant AI, Winnow.

Table of Contents

Contents

Executive summary

AI value chain

Foodservice challenges

The impact of AI on foodservice

Case studies

Market size and growth forecasts

Mergers and acquisitions

AI timeline

Companies

Sector scorecard

Glossary

Further reading

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