Nine in ten retailers globally are planning to raise their spending on artificial intelligence (AI) to optimise their e-commerce operations over the next 12 to 24 months, with online delivery execution a key area of focus. A total of 38% of European retailers identify speed, tracking and proactive communication around the delivery process as areas where AI can deliver the greatest impact.
We are now in a time of manufacturing where precision is more than a technical necessity; it's a business requirement. The more complex, globally dispersed and demanding things get, the less slack remains in the system. Under these circumstances tolerance management has become a decisive competence and affects competitiveness not only in terms of controlling costs, ensuring quality and improving production efficiency but also for long term market success.
The technology underpinning retail operations is under scrutiny in 2026 as fashion executives look to streamline systems with the aim to unlock efficiency, cut costs and meet consumer expectations for speed and personalisation in the shopping journey. At the retail event Lightspeed Edge on 12 January, Lightspeed - the unified point-of-sale (POS) and payments platform for SMEs such as Apricot Lane Boutique and Neal's Yard Remedies - convened industry leaders to explore the strategic imperative for integrated technology ecosystems over siloed systems.
"I've never felt this much behind as a programmer. The profession is being dramatically refactored as the bits contributed by the programmer are increasingly sparse and between. I have a sense that I could be 10X more powerful if I just properly string together what has become available over the last ~year and a failure to claim the boost feels decidedly like skill issue."
Industry professionals are realizing what's coming next, and it's well captured in a recent LinkedIn thread that says AI is moving on from being just a helper to a full-fledged co-developer - generating code, automating testing, managing whole workflows and even taking charge of every part of the CI/CD pipeline. Put simply, AI is transforming DevOps into a living ecosystem, one driven by close collaboration between human judgment and machine intelligence.
Manual database deployment means longer release times. Database specialists have to spend several working days prior to release writing and testing scripts which in itself leads to prolonged deployment cycles and less time for testing. As a result, applications are not released on time and customers are not receiving the latest updates and bug fixes. Manual work inevitably results in errors, which cause problems and bottlenecks.
The real cost of poor observability isn't just downtime; it's lost trust, wasted engineering hours, and the strain of constant firefighting. But most teams are still working across fragmented monitoring tools, juggling endless alerts, dashboards, and escalation systems that barely talk to one another, which acts like chaos disguised as control. The result is alert storms without context, slow incident response times, and engineers burned out from reacting instead of improving.
