AI Can Help Analyze Software Licensing. It Shouldn't Decide It.
Artificial intelligence has become the defining technology story of the last several years. Every software vendor is racing to announce AI-powered features, AI-driven recommendations, and autonomous agents capable of making decisions with minimal human involvement. Software Asset Management is no exception. Across the industry, vendors are embedding AI into reporting tools, contract analysis platforms, optimization engines, and workflow automation systems.
Many of these innovations are valuable. In fact, we believe AI will ultimately play an important role in the future of Software Asset Management.
However, there is a growing difference between using AI to assist experts and using AI to replace them.
At LicenseFortress, we believe that distinction matters.
AI Already Has Legitimate Uses in Software Asset Management
AI is also well-suited for identifying patterns within large datasets. It can surface potentially unused licenses, detect anomalies, identify optimization opportunities, and highlight trends that may warrant further investigation. Used appropriately, these capabilities can accelerate analysis and improve operational efficiency.
The distinction matters. Retrieving data and interpreting data are not the same thing. Most organizations would be comfortable allowing AI to identify all SQL Server instances in an environment. Far fewer would be comfortable allowing AI to determine whether those deployments are properly licensed without expert review.
None of these use cases concern us. In fact, we expect many of them to become standard features across the SAM industry over the next several years. The discussion becomes much more complicated when AI moves beyond gathering information and identifying patterns and begins making licensing, compliance, or contractual determinations on behalf of the organization.
The Problem Begins When AI Starts Making Licensing Decisions
The challenge arises when AI moves beyond assisting experts and begins replacing judgment.
Software licensing is rarely straightforward. Enterprise environments contain years of negotiated contract amendments, legacy entitlements, mergers and acquisitions, virtualization architectures, cloud deployments, exceptions, and vendor-specific licensing rules. Determining whether an organization is compliant often requires far more than reviewing deployment data.
It requires interpretation.
That distinction is important because AI models do not actually understand licensing agreements. They predict likely answers based on patterns found in data. While those predictions can be remarkably useful, they are not the same as expertise.
Consider a common Oracle licensing scenario. An organization may have multiple ordering documents, historical contracts, support agreements, and custom amendments negotiated over many years. A licensing position may depend on a single clause buried within an agreement signed a decade ago. Missing that detail could dramatically alter the compliance outcome.
In situations like these, "probably correct" is not good enough.
A software vendor does not care whether an AI model was 95% confident in its recommendation. If a compliance position is challenged, the organization must be able to explain how the conclusion was reached, what evidence was considered, and who ultimately approved the analysis.
That responsibility still belongs to people.
Accountability Matters More Than Automation
One of the questions we rarely hear discussed in AI marketing materials is also one of the most important:
Who is accountable when the AI is wrong?
If an AI-powered assistant incorrectly identifies unused licenses, the consequences may be limited. If it recommends a flawed compliance position that results in a seven-figure exposure during a software audit, the situation becomes very different.
An algorithm cannot participate in a vendor negotiation. It cannot defend a contractual interpretation. It cannot testify to its reasoning. It cannot assume liability for an incorrect recommendation.
Ultimately, accountability rests with the organization making the decision.
This is one of the reasons LicenseFortress continues to place experienced licensing professionals at the center of our services. Our clients are not simply looking for answers. They are looking for answers that can be explained, defended, and supported when challenged by software vendors, procurement teams, legal counsel, auditors, and executive leadership.
That level of accountability still requires human expertise.
The Security Question Deserves More Attention
There is another aspect of AI adoption that deserves greater scrutiny: data security.
Many AI platforms require information to be transmitted to third-party services for processing. Depending on the use case, that information may include software inventories, infrastructure details, contracts, entitlement records, cloud consumption data, compliance assessments, or financial exposure calculations.
For many organizations, these represent some of the most sensitive datasets in the enterprise.
As a provider of Software Asset Management services, we believe any discussion about AI should include a discussion about security, governance, and data handling practices. The question should not simply be whether AI can perform a task. The question should also be whether the benefits outweigh the risks associated with exposing sensitive operational and licensing information.
This perspective is one reason LicenseFortress recently completed its SOC 2 Type 1 examination. Security, governance, and operational discipline are not marketing initiatives for us. They are foundational requirements for handling customer information responsibly.
When we evaluate AI technologies, we apply the same standards.
Our Approach to AI
To be clear, LicenseFortress is not opposed to AI.
We actively evaluate emerging technologies and recognize the significant value AI can provide in areas such as research, workflow automation, reporting, data analysis, and administrative efficiency. We expect AI to continue improving and to become an increasingly important tool for SAM professionals.
What we are not willing to do is allow AI to independently determine software compliance positions, interpret contractual obligations, calculate audit exposure, or make recommendations that carry significant financial or legal consequences without expert review.
Those decisions deserve more than automation.
They deserve accountability.
For that reason, our approach remains straightforward: use AI where it improves efficiency, maintain human oversight where expertise is required, and ensure that every recommendation delivered to a customer can be explained, validated, and defended.
Trust Is Still the Most Important Technology
The technology industry often rewards speed. New capabilities are released quickly, competitors rush to match them, and marketing departments race to promote the latest innovation.
There is nothing wrong with innovation.
But in Software Asset Management, trust remains more important than novelty.
Organizations rely on SAM data to support software audits, contract negotiations, budgeting decisions, cloud migrations, compliance reviews, and vendor disputes. The quality of those decisions depends not only on the tools being used, but also on the expertise behind them.
AI will undoubtedly become part of the future of Software Asset Management.
The question is not whether organizations should adopt it.
The question is where human judgment must remain in the process.
At LicenseFortress, we believe that line is clearer than many vendors are willing to admit.
