British businesses enter 2025 still grappling with the repercussions of global upheavals. Inflationary pressures persist, supply chain stability remains fragile, and talent shortages continue to drive up workforce costs. At the same time, demand across many sectors has become less predictable, challenging even the most resilient business models.
In this environment, one thing is clear: technology has transformed almost every industry, accelerating digital adoption in response to recent years’ disruptions. We are now five years on from the start of the pandemic, and the momentum towards tech-enabled services and data-driven decision-making shows no sign of slowing. Rather than just striving to be more efficient, leading organisations are using technology to reshape their commercial strategies entirely: unlocking new revenue streams, rethinking how they price and deliver value, and building deeper relationships with their customers.
Below, we explore three major themes shaping how innovative industrial businesses in the UK are approaching their monetisation models in 2025.
Emerging industrial marketplaces—often inspired by national initiatives like Made Smarter UK—offer a new blueprint for collaboration. These marketplaces enable manufacturers, service providers, and technology companies to “plug in” specialised capabilities—such as analytics modules, robotics support, or maintenance services—to create tailored value bundles for customers.
This approach is making pricing more fluid and adaptable. Instead of rigid one-size-fits-all contracts, businesses can experiment with subscriptions for analytics, usage-based billing for machinery upkeep, or premium tiers that give access to specialised resources. Through these flexible models, businesses can respond nimbly to shifting supply chain or cost pressures, providing the right solutions at the right time.
GE’s Predix platform highlights the power of such ecosystems. Focused on industrial IoT, Predix allows companies to capture and interpret performance data from a broad range of equipment—turbines, engines, sensors, and more. By packaging these insights into actionable dashboards and analytics, GE enables customers to optimise everything from asset maintenance to production planning. The outcome is not just greater efficiency but also new revenue opportunities as businesses discover additional ways to monetise or share these data-driven insights.
Industrial processes—whether in manufacturing, logistics, or construction—are generating staggering amounts of data. As companies look to diversify and strengthen revenues, many are discovering opportunities to sell data-driven insights, either as standalone offerings or bundled with existing products. By doing so, they help customers make informed decisions that can reduce costs, improve operational efficiency, or boost product quality.
For example, Siemens packages real-time performance dashboards, predictive maintenance analytics, and industry benchmarks through its MindSphere IoT platform. Meanwhile, Ocado Technology offers logistics-as-a-service by sharing data insights from its automated warehousing systems—demonstrating how information itself can become a profitable, mutually beneficial asset.
Potential Pitfall: Data monetisation hinges on open data sharing. If customers feel uneasy about privacy, security, or the accuracy of metrics, trust erodes. Clear communication about what data is collected and how it’s used is vital.
The ongoing volatility in manufacturing costs, raw materials, and workforce availability has pushed many companies to explore pricing models that directly align with customer success metrics. Known as outcome-based pricing, this approach means suppliers only get paid if they help customers achieve measurable results—such as improved energy efficiency or higher production yields. By tying compensation to results, both parties share the risk, and suppliers have a strong incentive to innovate.
For instance, Schneider Electric has performance contracts in building management systems that guarantee specific energy savings, with fees linked to the actual reductions achieved. In agriculture, equipment makers and crop protection manufacturers are trialling “pay per yield” models, where a farmer’s payment is calculated based on crop improvements—equally sharing the rewards (and risks) of new technology or methods.
By some estimates, over 40% of UK industrial firms will have converted at least part of their product portfolios into service-based offerings by the end of 2025. This transformation—or “servitisation”—creates more stable, recurring revenue streams while providing ongoing value to customers.
An early pioneer is Rolls-Royce’s “Power by the Hour,” charging airlines for engine flight hours instead of an upfront purchase. This shared-risk model incentivises both parties to boost efficiency and reliability. Komatsu similarly bundles its earthmoving equipment with predictive maintenance and support services to deliver additional value and more predictable invoice cycles for its clients.
Potential Pitfall: Outcome-based pricing and service-based models rely on accurate data collection and clearly defined metrics. Ambiguity around performance indicators can lead to disputes—so setting up robust monitoring and transparent communication channels is critical.
Facing ongoing uncertainties—from cost fluctuations in raw materials to shifting regulations—some businesses are forging deeper, more cooperative relationships with customers and partners. Innovative contracting models move towards sharing both risk and reward, using performance-based mechanism structures rather confrontational negotiations to drive return on investment.
The Project 13 framework by the Institute of Civil Engineers is a prime example, where all parties work together to contain costs and solve issues proactively. In renewable energy, Ørsted uses Power Purchase Agreements that align rates with performance outcomes, creating a partnership grounded in mutual trust and shared ambition.
No outlook for 2025 would be complete without mention of Artificial Intelligence. This will driver of efficiency in the commercial team (better outcomes, more quickly), but this year we will also see AI playing a bigger part in the commercial offer itself. This can take many forms: AI-powered systems can dynamically adjust prices in real time, responding to market shifts, supply chain bottlenecks, or changes in customer demand. Predictive analytics also help identify emerging issues—like a machinery part about to fail—allowing businesses to propose maintenance or upgrades before downtime becomes a problem.
The companies that succeed won’t be the ones that simply add AI to the feature list or their URL, but rather those that solve real problems and are genuine “value add” for customers. I’ve recently heard AI (as a product feature) likened to the first lightbulb or wheel: truly transformative, but on its own far from the peak in terms of value-to-customer.
Caterpillar is integrating predictive modelling into its spare parts pricing, automatically adjusting rates based on usage and inventory data to minimise stockouts and avoid production delays for its customers. AI-driven insights even help sales teams tailor their offerings to each client’s precise needs, reducing back-and-forth negotiations and building trust through transparency.
Potential Pitfall: While AI can be a powerful enabler of in pricing, a lack of transparency can undermine customer confidence. Communicating the rationale behind prices is essential, particularly in industries where customers have been in long-term rather than transactional relationships.
For most high-performing businesses, pricing is far more than a mechanism for covering costs. However, many of those deploying the fundamentals of value-based pricing still leave money on the table. As industrial businesses navigate economic uncertainty, those who adapt their pricing and service models to enhance, align to, and capture value will be best positioned to stay competitive.
