Leveraging AI for operational efficiency
16 September 2025
Reading time: 4 minutes
AI is everywhere right now. While the headlines chase breakthroughs, many businesses – particularly in B2B – are already getting on with the practical side of things, quietly building AI into the tools and processes they use every day. It’s not always visible from the outside, but it’s there: helping teams forecast more accurately and clear the small blockers that slow things down. In many cases, it’s simply improving the systems that are already in place.
AI is often framed as revolutionary, but that framing can distract from its most useful role: making everyday operations more efficient. According to BCG, 74% of companies struggle to scale AI beyond pilot projects, often because they treat it as a standalone innovation rather than a tool to enhance existing processes (BCG, 2024).
The truth is, the biggest gains usually come from the least flashy applications. In fast food and retail, chains like McDonald’s, Domino’s and Starbucks are using AI to forecast demand and manage inventory. They’re using point-of-sale data and even local weather trends to make smarter stock decisions and avoid unnecessary waste (Business Insider, 2025).
In manufacturing, companies like Coca-Cola and Siemens Energy are using AI-powered predictive maintenance tools. These tools flag early signs of wear or failure based on sensor data, helping teams avoid costly downtime and keep production running smoothly. For B2B operations that rely on strict service levels or supply chain dependability, that kind of uptime is critical. In some cases, service costs have dropped by over 20% (Business Insider, 2025).
The same thinking applies across industries. Penske Truck Leasing, which serves a wide range of business clients across logistics, manufacturing and retail, uses AI to stay ahead of vehicle maintenance. Their system processes millions of data points daily from vehicle sensors, flagging potential faults before they cause disruption. This helps reduce breakdowns and support the operational uptime their B2B customers rely on (Business Insider, 2025).
Amazon’s use of machine learning is a good example of this in action. It’s built into how they plan stock levels and manage distribution, including across their B2B logistics and partner operations. It’s part of their day-to-day, and not treated as a separate innovation project (Amazon Science, 2023).
More organisations are moving past the idea of a separate AI strategy, instead focusing on how it can make a tangible difference within everyday operations.
All these examples share one thing in common: they don’t treat AI as something separate. It’s a means to an end embedded into the systems that keep operations moving and business performance on track.
For B2B leaders especially, the value of AI should show up in the metrics that already matter: reduced downtime and tighter SLAs. It shouldn’t sit off to the side, but rather strengthen the performance you’re already measured on.
Getting started doesn’t need a bold transformation plan. The better route is to find one pain point, fix it with AI, and build from there. That’s often how real and scalable change begins.
Where to focus
- Start with the real blockers. Focus AI efforts on the operational pain points that are slowing teams down.
- Tie AI to outcomes that matter to your business. If it’s not impacting core KPIs, it’s just noise.
- Leverage the data you already have. Many organisations sit on years of operational data that can power AI, without needing to start from scratch.
- Build from small, proven wins. A successful pilot in one process or business unit can be the launch pad for wider transformation.