Retailers and global enterprises are set for a year of rapid change in the way they handle supply chains and data, as technology such as artificial intelligence and machine-driven decision-making reshapes customer experience and operational strategy.
Retail experience
Retailers are witnessing a surge in consumer use of AI-powered tools, impacting both customer service and supply chain efficiency. The growing use of chatbots and predictive analytics is shifting how customer demands are met across all stages of order fulfilment. Companies face mounting pressure to upgrade ageing infrastructure and align their supply chain strategies with personalisation and convenience.
"Growth in tech continues to define the direction of the logistics and supply chain industry. In 2025, the use of AI and ChatGPT proliferated significantly in retail and supply chain operations. Customer experience continues to drive all developments in supply chain and as we create more convenience and personalisation at the retail level, aged infrastructure and poorly developed supply chain strategies will fall behind," said Travis Erridge, CEO and Co-Founder, TMX Transform.
The convergence of customer data platforms with supply chain technology will move operators towards fully connected operations that significantly alter how businesses maintain market share and move goods from production to shelf.
Predictive logistics
Integration between customer data platforms and supply chain technology is enabling faster, more accurate responses to consumer demand. Predictive supply chains analyse real-time data to inform inventory positioning, fulfilment, and last-mile delivery. Retailers that adopt these tools are able to anticipate shopping trends and plan logistics accordingly.
"Customer Data Platforms are now being integrated with supply chain technology, enabling predictive decision-making that responds to customer demands in real-time. Another area of impact to consider is data security. In 2025, we saw many companies impacted by data breaches either directly or through third parties. Because of this, companies will be hyper-focused on data security in 2026, to ensure adequate contingency planning," said Erridge.
"What elevates this transformation is how it's driving decision-making across every operational layer - from production planning through to final delivery. Companies are leveraging data and personalisation to inform not just what customers want, but when and how they want it delivered. This predictive capability will continue to revolutionise inventory positioning, fulfilment strategies, and last-mile delivery in ways that were theoretical only a few years ago," said Erridge.
Security focus
With the expansion of interconnected systems and increased use of AI, companies are placing greater emphasis on data security and compliance. Recent incidents of data breaches, often through third parties, have led organisations to review and strengthen their contingency measures.
Infrastructure evolution
Other dynamics, such as US tariff policies and ongoing labour shortages, are creating further challenges for supply chain managers. Property market adjustments, particularly in industrial real estate, present both obstacles and opportunities for companies seeking to modernise their supply chain infrastructure. The overall outlook is shaped by the continued global growth in technology and increased intelligence throughout supply chain networks.
Geopolitical disruptions, particularly US tariff policies, continue to reshape trade flows and investment patterns. Labour challenges persist globally, while property markets are rebalancing, creating opportunities to strategically target industrial property.
"All these areas in 2026 will be impacted by the global growth in technology and intelligence in everyday supply chains," said Erridge.
AI-centric data
Within enterprise IT, the roles of AI and data infrastructure are also shifting. AI agents are quickly becoming the primary consumers of enterprise data. This movement is pressuring organisations to rethink their data architecture, governance, and compliance frameworks, as machine-driven analytics begin to outpace traditional human analysis.
"We're moving toward a world where data platforms won't primarily serve people anymore; they'll serve machines. The new consumers of data are AI agents, which will increasingly drive decisions, generate insights, and automate processes at speeds humans can't match. These AI agents will require direct, governed, real-time access to all enterprise data to reason, generate, and act effectively. As AI agents become the primary consumers, enterprises must decide whether their data governance models empower or constrain them. This shift fundamentally changes everything about how we build and operate data infrastructure, from architecture and pipelines to governance and security, demanding a new approach that prioritizes machine-first accessibility without sacrificing trust or compliance," said Justin Borgman, CEO and Cofounder, Starburst.
Hybrid infrastructure
As performance and regulatory needs diverge across markets, many organisations are pushing AI workloads back from the cloud to hybrid or on-premises environments. This trend is being driven by the costs associated with AI inference, data gravity, and evolving data sovereignty laws, particularly in highly regulated sectors. Companies are building internal 'AI data factories', allowing them to tightly control their information while enhancing operational efficiency.
Data trust
Metadata management and data governance have become central to enterprise AI implementation. With new standards for open table formats, the competitive edge now lies in managing data access, explaining AI outputs, and maintaining unified compliance policies. Companies lacking robust metadata and policy systems face increasing risks around AI compliance and trust.
DevOps adaptation
The evolution towards machine-first operations means that DevOps teams are now required to govern real-time data exchanges between AI agents and enterprise systems, alongside their traditional responsibilities. This involves new forms of monitoring, reliability checks, and policy enforcement to protect data quality and ensure consistent decision-making outcomes.
"DevOps is evolving beyond its traditional focus on deploying applications. DevOps for machines means governing the real-time interaction between AI agents and enterprise data, with the same rigor once reserved for production apps. Modern teams will now treat data and AI pipelines as mission-critical workloads, ensuring that AI agents have real-time, governed access to enterprise data while maintaining reliability, security, and observability at scale.
"DevOps for machines is about managing the data-to-action lifecycle, not model training pipelines. Humans remain responsible for defining access, policy, and safety nets.
"For example, tomorrow's DevOps teams will monitor not only application uptime, but also AI decision health to ensure agents operate within defined parameters. This evolution requires a new mindset: one where DevOps teams are responsible for orchestrating an ecosystem in which machines, not just humans, can operate safely, efficiently, and autonomously," said Borgman.
This article was originally published in ChannelLife on December 5, 2025.
Watch Travis Erridge's full 2026 prediction video here: TMX Transform CEO, Travis Erridge, on what 2026 has in store | TMX Transform