Software License Optimization Guide for the new AI Era

In this article, let’s explore Software License Optimization – how it is changing due to AI, Common License Optimization Problems and a Step-by-Step License Optimization Checklist.

Software License Optimization (SLO) Guide for the new AI Era

For decades, software licensing was viewed primarily as a procurement or IT administration activity.

Organizations purchased licenses for:

• PLM systems,
• ERP platforms,
• CAD applications,
• Manufacturing Execution Systems (MES),
• Quality Management Systems,
• Collaboration tools.

Once the contracts were signed, attention shifted toward implementation and operations.

Unfortunately, many enterprises rarely revisited a critical question: Are we actually utilizing what we are paying for?

Today, that question has become more important than ever.

The emergence of AI, Agentic Systems, and Industry 5.0 is fundamentally changing how enterprise software is consumed.

Traditional role-based licensing models are gradually evolving toward rule-based and usage-based consumption patterns, creating both opportunities and risks for product manufacturers.

As AI agents begin interacting with enterprise systems, organizations must rethink how they measure, govern, and optimize software license utilization.

The concepts described in the transition from human role-based access to AI-driven rule-based access are already beginning to reshape enterprise software economics.

License Cost Was Always Important

Software licenses have historically represented one of the largest recurring expenses within engineering and manufacturing organizations.

Large manufacturers routinely spend millions annually on:

• PLM licenses,
• ERP licenses,
• CAD licenses,
• Simulation tools,
• Analytics platforms,
• Cloud subscriptions.

Yet many organizations discover:

• Inactive users,
• Duplicate capabilities,
• Excessive license tiers,
• Underutilized modules,
• Poorly aligned user assignments.

In some enterprises, low utilization rates are not uncommon. The challenge was always significant. The difference now is that AI introduces an entirely new cost dimension.

The Shift from Roles to Rules

Traditional enterprise systems were designed around people.

A Design Engineer received a Design Engineer license.

A Manufacturing Engineer received a Manufacturing Engineer license.

A Project Manager received a Manager license.

Access rights were tied to job roles.

However, AI is introducing a new operating model.

Instead of asking: “Which user should perform this task?”, Organizations are increasingly asking: “Which agent, workflow, or automated process should perform this task?”

This transition is moving enterprises from: Role-Based Access toward Rule-Based Access and Autonomous Actions.

AI agents can:

• Analyze BOM structures,
• Perform change impact analysis,
• Validate compliance,
• Generate reports,
• Trigger workflows,
• Coordinate between PLM and ERP systems.

The result is that software consumption is no longer driven exclusively by human users.

Why This Changes License Economics

Traditional licensing models typically rely on:

• Named Users
• Concurrent Users
• Functional Roles

AI introduces questions that many software vendors are still trying to answer:

• Is an AI agent considered a user?
• How should autonomous workflows be licensed?
• Should organizations pay per action?
• Per API call?
• Per transaction?
• Per outcome?

As organizations adopt Agentic AI, software utilization may increase dramatically even when headcount remains constant.

This creates a new optimization challenge.

Understanding License Utilization

License utilization simply means measuring how effectively purchased software licenses are actually being used.

A well-managed environment seeks to maximize:

• Business Value Obtaining measurable outcomes from licenses.
• Utilization Ensuring licenses are actively used.
• Cost Efficiency Avoiding waste and redundancy.

Optimization does not mean buying fewer licenses. It means buying the right licenses for the right users, workflows, and future operating models.

Common License Optimization Problems

Most manufacturers face one or more of these challenges:

1. Over-Licensing: Purchasing more licenses than required.
2. Under-Licensing: Insufficient licenses causing operational bottlenecks.
3. Role Inflation: Users over empowered with expensive licenses despite limited functionality needs.
4. Shelfware: Purchased modules that are never adopted.
5. Legacy Contract Structures: Contracts negotiated years ago that no longer align with current usage patterns.
6. AI Readiness Blind Spots: Licensing models that fail to account for future AI and agent-based consumption.

Why Product Manufacturers Face a Bigger Challenge

Product manufacturers operate highly interconnected environments involving:

• PLM
• ERP
• MES
• CAD
• SCM
• Quality Systems
• Digital Twins
• AI Platforms

Each system may have:

• Separate licensing models,
• Different usage metrics,
• Varying user classifications.

When AI agents begin interacting across these systems, software utilization becomes significantly more complex.

For example:

An engineering change request may involve:

• PLM workflows,
• ERP validation,
• Supplier collaboration,
• Compliance checking,
• AI-driven impact analysis.

One process can touch multiple licensed systems simultaneously. Without governance, costs can escalate rapidly.

Lack of Independent Consulting

One of the least discussed challenges in software license optimization is the lack of truly independent expertise.

Many enterprises depend heavily on guidance from:

• Software vendors
• System integrators
• Implementation partners

These organizations often possess deep product knowledge.

However, they may also have commercial incentives tied to:

• License growth,
• Cloud consumption,
• Module expansion,
• Platform adoption.

This does not automatically imply bad advice.

But it can create a bias.

At the same time, some independent consultants struggle to stay current with:

• AI licensing models,
• Cloud economics,
• Agentic AI architectures,
• Usage-based pricing,
• Evolving vendor contracts.

As a result, enterprises frequently receive either: Vendor-biased recommendations, or Outdated recommendations. Neither supports long-term optimization.

Building the Right License Optimization Team

The solution begins with governance.

Organizations should establish a cross-functional Software Economics Team comprising:

PLM Experts : Understanding engineering workflows.

ERP Experts : Understanding enterprise operations.

Enterprise Architects : Evaluating technical implications.

Procurement Specialists : Managing commercial negotiations.

AI and Automation Experts : Assessing future agent-based consumption.

Finance Representatives : Measuring ROI and cost impact.

Independent Consultants : Unbiased as well as Latest Technology Expertise.

Most importantly:

This team must have the freedom to challenge existing assumptions, legacy contracts, and vendor recommendations.

Their objective should be: Maximize business value, not software consumption.

A Step-by-Step License Optimization Checklist