FEATURE Robotics
OVERCOMING THE OBSTACLES
Julian Ware, UK & Ireland Sales Manager, ABB Robotics, discusses integrated automation, and says it is the key enabler for next-generation logistics
O
nline sales currently account for between 25 and 30% of retail sales across the UK, and even more during the Christmas
period*. Recent polling also shows that British shoppers expect fast delivery at no extra cost, with 64% of people expecting to receive products the next day**. While such rising expectations are driving warehouse and distribution centre operators to boost performance, persistent workforce shortages are forcing them to wrestle with recruitment and retention challenges.
Using automation in logistics operations also helps drive sustainability. Robotic systems can help optimise energy use, reduce waste and errors, and allow for more compact warehouse layouts, leading to smaller environmental footprints.
Faced with pressure from all sides, more companies are turning to automation to maintain throughput, improve productivity, and keep a lid on costs.
Most logistics operators are already using fast and flexible robotic systems to speed up specific processes, but many now realise that they need to go further. That’s because improving the productivity of a typical warehouse or distribution centre is about optimising the overall flow of products from start to finish, not focusing on individual steps. Therein lies today’s biggest logistics challenge: a facility is only as fast and efficient as its slowest process. More than 50,000 warehouses globally were
already using robots at the start of 2026***, yet many of these systems are configured as islands of automation within a sea of manual or semi-automated processes. This can result in stuttering process flows and data
10 October 2026 | Automation
inconsistencies, which in turn create serious bottlenecks. All this points to end-to-end integration as the key enabler for warehouse and logistics operators looking to maximise value across their automated systems. As a result, the focus of automation is shifting from individual tasks towards the creation of fully integrated, end-to-end workflows. The proliferation of digital technologies,
such as AI, the Internet of Things, and data analytics, is making robotic deployments more flexible, modular, and ROI-friendly. These technologies simplify deployment, provide real-time operational visibility, and allow users to make predictive adjustments. Together, these cumulative advantages are helping to accelerate the pace of adoption across logistics networks. A platform that combines robotics, AI, and autonomous mobility can be used to build adaptive, intelligent, and scalable automation solutions that can respond effectively to the dynamic nature of logistics. For instance, bringing together leading-edge AI machine vision with the latest controller upgrades allows robots to adapt and handle the kind of unpredictable tasks that would previously have required human intervention. The latest systems can handle variability in SKU types, packaging, or layout changes, as well as seasonal peaks across tasks such as picking, depalletising, induction, sorting, and inspection.
Such developments mean that the common obstacles that have previously hindered the wider take-up of robotic automation in logistics are being systematically dismantled. The application of generative AI in
machine vision is arguably the biggest game changer, enabling a significant leap forward in the capabilities of robotic systems. It also dramatically reduces the calibration effort involved in establishing the level of machine vision that allows robots to work autonomously in complex environments. For instance, ABB’s generative AI, no-code vision platform delivers an 80% reduction in the time needed to train and deploy vision systems. Generative AI also enables in-field
retraining so that robots can adapt more easily to meet changing business needs. In practice, that might mean enabling robot vehicles (also known as Autonomous Mobile Robots, or AMRs) to navigate autonomously around rapidly changing layouts, including sharing spaces safely with people. Similarly, AI-enhanced machine vision can equip robots to adapt readily to variability in the formats they must handle in picking, sorting, or depalletising applications. The burden of integration is the next obstacle to fall, thanks to the availability of pre-engineered functional modules. Choosing to deploy well-proven modules significantly reduces project risk and accelerates time- to-deployment. Meanwhile, features such as no-code interfaces, intuitive tools, and solution-centred support all reduce robot users’ dependency on in-house skills by simplifying setup and integration. As market penetration deepens and industry experience accumulates, users can also refer to proven benchmarks that help make the business case for automation both less risky and more compelling. This is obviously easier if the supplier has a strong track record that allows them to draw on a
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