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GLOBAL RESEARCH ARCHIVE

Replay: Autonomous food delivery - Last-Mile Shift (Monthly Thematic Call - July)

Published: 2026-07-15Institution: BarclaysPages: 13Original language: 英语Evidence page: 2

Research evidence excerpt

Replay: Autonomous food delivery - Last-Mile Shift (Monthly Thematic Call - July)

Barclays | Replay: Analyst Access

deployment. The team reviewed the key technologies underpinning the market, compared SDR

and drone deployment pathways, and assessed what recent partnerships suggest about the

direction of travel for major global delivery platforms. The call also examined the economic

opportunity, including the potential for autonomy to reduce delivery costs, improve platform

profitability and expand the food delivery addressable market.

The discussion concluded with an assessment of the competitive landscape across delivery

platforms, robotics providers and drone operators, highlighting the companies best positioned

to benefit from broader autonomous adoption.

Key messages from the discussion include:

•• Autonomous delivery is the next structural shift in food delivery economics, addressing

one of the sector's largest cost components: last-mile delivery.

•• The long-term model is likely a multimodal network, combining robots, drones and riders

rather than replacing one with another. While SDRs are best suited to dense urban areas,

drones are most effective in suburban areas where rider economics become inefficient.

•• One of the main findings from the field research was that the technology already works;

software, data and fleet orchestration are now more important than hardware.

•• Data a competitive moat, with operators that fulfil more orders and travel more kilometres

improving performance and reliability over time.

•• Feedback from consumers and merchants was broadly positive, highlighting greater

convenience, more predictable service, lower delivery failures and improved workflow

automation.

The English excerpt is extracted automatically from the cited source page and may contain layout or recognition errors. It is never batch translated.

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