IFS is challenging one of enterprise software’s most entrenched assumptions by shifting AI pricing away from named users and toward the operational assets customers actually manage. For CIOs and COOs wrestling with pilot purgatory and unpredictable license costs, the move is designed to make industrial AI adoption easier to fund and scale.
Analysis
What This Means for ERP Insiders
AI economics will become a core product differentiator. IFS’s asset-based pricing shows that how vendors charge for Industrial AI can be as strategic as the models themselves, forcing rivals to rethink per-user licensing that discourages broad deployment across operational roles and partners.
Asset-Based Pricing for Scaled AI Adoption
Under the new model, organizations pay for IFS.ai capabilities based on the number of assets they operate, such as turbines, aircraft, offshore rigs or production lines, rather than the people and systems that touch the data. This model is designed to better reflect where value is created, and removes the fear that each new AI-assisted user or workflow will trigger another license negotiation.
For technology leaders, they can deploy AI agents and decision-support tools wherever they make operational sense, without having to ration access by role or site. Maintenance planners, control-room operators and field technicians can use the same industrial AI services to optimize uptime and throughput as long as they are working against the same pool of assets.
The model benefits asset-intensive industries such as energy, aerospace, manufacturing and utilities, where a single offshore platform or fleet of aircraft may support thousands of workers, contractors and connected devices. In those environments, predictable AI costs mapped to asset portfolios could make it easier to budget multi-year transformation programs.
The change builds on IFS’s reported FY 2025 momentum, where Industrial AI adoption helped drive 23% annual recurring revenue growth, 30% cloud revenue growth and a 114% net retention rate as customers expanded deployments across more sites and business units. By decoupling pricing from headcount, IFS is betting those expansion dynamics will accelerate as customers no longer have to justify every incremental AI user.
Analysis
What This Means for ERP Insiders
Asset-centric models will reshape architecture and data strategies. Tying pricing to assets will push enterprise architects to define consistent asset hierarchies, converge ERP, EAM and supply chain records, and prioritize platforms that treat assets as the central organizing principle for AI, analytics and automation.
Governance, Evaluation Criteria and Daily Impact
For CIOs evaluating Industrial AI platforms, the new pricing structure adds another dimension alongside functionality and architecture. Buyers will need to assess how asset definitions are set, how shared assets across business units are counted and how pricing evolves as companies acquire or retire infrastructure. Clear rules here will matter as much as model accuracy or integration depth.
Operational leaders will also want to understand how asset-based pricing interacts with specific use cases like predictive maintenance, logistics optimization or workforce scheduling. If multiple AI capabilities draw on the same asset record, the model could incentivize teams to standardize on a common data foundation so that each additional use case delivers more value without increasing licensing.
IFS frames the shift as part of a broader push toward agentic AI, where software not only recommends actions but can orchestrate workflows across planning, execution and service. By aligning commercial terms with operational outcomes, the company aims to tie its own growth to customer success metrics such as reduced downtime, higher asset utilization and lower logistics costs.
The move also raises competitive pressure on other ERP and EAM vendors that still rely on traditional per-user or per-module pricing, especially as enterprises look to extend AI beyond a narrow group of specialists to frontline roles. For many, this will prompt fresh scrutiny of whether current contracts support the kind of AI saturation they envision over the next five years.
Analysis
What This Means for ERP Insiders
Industrial AI scale-out will accelerate beyond pilot pockets. With costs aligned to fleets and facilities rather than headcount, organizations can roll IFS.ai into more plants, rigs and logistics networks, turning early successes into network-wide programs that influence vendor roadmaps, partner ecosystems and future M&A across the ERP sector.
