IFS: How its Pricing Model is Aiding AI Deployment

2026-04-14 · Source: AI Magazine · Field: Manufacturing & Industrial — Manufacturing Operations & Management, Smart Manufacturing & Industry 4.0 · Depth: Intermediate, short

Summary

IFS, a leading provider of industrial AI, has introduced an innovative asset-based pricing model designed to remove traditional cost barriers to AI adoption in complex industrial operations. This new model shifts away from conventional "per user" licensing, instead allowing companies to pay based on the number of assets (e.g., machines, components, infrastructure) rather than the number of employees interacting with AI systems. This change aims to make AI deployment more financially predictable and scalable, enabling organizations to expand their AI projects without incurring prohibitive costs associated with user-based licensing. The company states this approach allows customers to deploy AI wherever it creates value, aligning software investment directly with operational assets and fostering greater accessibility and value realization.

Key takeaway

For CTOs and procurement leaders evaluating AI investments, IFS's asset-based pricing model offers a significant shift, allowing you to scale AI deployment across your industrial operations without the financial constraints of traditional user-based licensing. This approach ensures your AI costs align directly with operational value, enabling broader adoption and maximizing your investment without worrying about fluctuating staff numbers driving up expenses. You can now confidently expand AI initiatives to drive work and outcomes across all relevant assets.

Key insights

IFS's asset-based AI pricing model removes user-based cost barriers, promoting wider industrial AI adoption.

Principles

• Price the work, not the workers.
• Align software costs with operational assets.

Method

IFS's new model charges based on the number of assets (e.g., machines, vessels, infrastructure) managed by AI, rather than the number of users accessing the AI systems, ensuring costs scale with operational reality.

In practice

• Pay for 400 assets instead of 12,000 employees.
• Expand AI projects without user-based cost increases.

Topics

• Industrial AI
• Asset-Based Pricing
• AI Deployment Barriers
• Software Licensing Models
• Operational Scaling

Best for: CTO, Executive, Director of AI/ML, VP of Engineering/Data, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI Magazine.