AI as IP™ Framework

A Practical Guide to Classify, Protect, and Monetize AI Assets (Part II of II)

AI serves as a double-edged sword, presenting economic risks and the potential to disrupt and harm various industries, while simultaneously enabling significant innovation and growth across many sectors. Yet, for small and mid-sized enterprises (SMEs), the economic value of AI systems is often invisible in financial reporting, lacks legal protections, and is under-leveraged in financing and valuation efforts. In this two-part article, the authors provide a practical guide that builds on earlier articles in the AI as IP™ series and provides SMEs with a step-by-step framework for identifying, classifying, protecting, and monetizing AI assets.

Read Part I here.

In this second part, the authors discuss how SMEs should view and treat their AI, as part of their IP Portfolio, as well as the risk and governance architecture needed.

Building an AI IP Portfolio for SMEs

Overview and Strategic Objective

A well-structured AI IP portfolio should be made with the aim of applying a firm’s innovations into measurable enterprise value. For SMEs, this means systematically identifying, documenting, protecting, and valuing AI assets using a disciplined combination of legal and accounting tools. Unlike with large corporations, which can fund dedicated legal teams to manage complex patent estates, SMEs may need to deploy leaner strategies to achieve coverage without unnecessary costs or delays.

An effective IP portfolio should be layered, dynamic, and aligned: layered in that each AI component is protected under multiple IP regimes; dynamic,[17] where protections evolve as technologies are refined; and aligned with both commercial strategies and investor expectations.

Conducting an AI IP Audit

The first step is to create an inventory and map of all AI-related assets. SMEs should use a structured audit template that lists the following:

1. Data sources and ownership status
2. Models and codebases
3. Algorithmic components and dependencies
4. Deployment environments
5. Customer-facing applications

Each of these line items should specify whether IP protection exists and whether additional filings or controls are needed. This can also serve to help identify orphan assets—such as technical outputs with no formal protections—and other forms of underutilized or under-documented IP assets.[18]

Establishing IP Filing Hierarchy

Once inventoried, assets should be prioritized based on commercial impact and risks of imitation. For most SMEs, the following hierarchy may apply:

Table 18: IP Filing Hierarchy

Patents	Can protect differentiating technical methods or architectures that confer market advantage.
Trade Secrets	Can protect training data, model weights, and process pipelines that lose value if disclosed.
Copyrights	Can protect code, interfaces, and documentation.
Trademarks	Can build trust and consumer recognition for deployed AI products.
Contracts	Can govern collaboration, access, and enforcement, often acting as the glue between all other rights.

 

IP Valuation: Accounting and Market Approaches

AI asset valuation should integrate both accounting recognition and market comparables. Under IAS 38/ASC 350, internally developed AI assets can be capitalized if they meet tests for identifiability, control, and future economic benefit.[19] Development costs can be amortized over their expected lifespan. Market valuation methodologies, comparable licensing transactions, or contribution margins from deployed applications may further help to establish fair market value.[20]

Conducting this form of hybrid valuation model can provide SMEs with defensible figures for financial statements, insurance, and M&A negotiations.

Continuous IP Portfolio Management

AI IP management is an iterative process. SMEs should review their portfolio quarterly, retire obsolete patents, refresh trade secrets documentation, and align filings with new releases. IP should also be integrated with business development and support commercial growth. By treating IP as an internal ecosystem rather than a static set of filings, SMEs can sustain a competitive advantage in an environment where AI continues to evolve.

Contractual Architecture and Licensing Models

Contracts are the operational infrastructure of IP strategy; they ensure that AI assets are properly licensed, controlled, and monetized while managing legal exposure. Strong contract frameworks can provide practical enforceability at minimal costs. A coherent contractual architecture should encompass data rights, model access, joint development, and commercial licensing. Each would need to be built upon clear definitions with respect to ownership, confidentiality, and liability.

Data Use and Access Agreements

Data agreements should explicitly define the following:

1. Ownership of raw and derived data
2. Permitted use (e.g., for training, testing, or commercialization)
3. Duration of access
4. Obligations for deletion or anonymization

Sample Clause:

“All data shared under this Agreement remains the exclusive property of the Disclosing Party. The Receiving Party shall not use the data, in whole or in part, for purposes other than model training and validation as expressly permitted herein. Any derivative models trained using the data shall remain the sole property of the Disclosing Party unless otherwise stated.”

For SMEs, standardized templates can simplify deal execution while preserving legal enforceability.

Model and API Licensing Agreements

AI modeling can typically take on one of the following three forms:

1. Inference Licensing: The licensee calls the model through an API and pays for each use, or through a subscription
2. Model Transfer Licensing: The licensee receives a full model (the weights and code) under strict restrictions
3. Embedded Licensing: The model is embedded into a larger solution or device

Joint Development and Collaboration Agreements

In co-development settings, such as SMEs partnering with clients or spun off from university settings, a clear allocation of ownership can help prevent future disputes.

Sample Clause:

“All Background IP shall remain the property of the respective Party. Foreground IP developed jointly shall be owned equally unless a Party provides more than 50% of funding or technical contribution, in which case ownership shall be proportionate to contribution as agreed in writing.”

Risk Allocation: Warranties, Indemnities, and Liability

Contracts must allocate risk explicitly. Key provisions can include:

• Performance Warranties: Define what is (and is not) guaranteed
• Indemnification: Require the supplier to defend against third-party IP infringement claims
• Limitation of Liability: Caps damages to a predictable amount

Sample Clause:

“Licensor’s total aggregate liability under this Agreement shall not exceed the total fees paid in the preceding twelve (12) months. Licensor disclaims any warranty of uninterrupted or error-free operation.”

When outputs from AI systems are probabilistic, such disclaimers are essential to maintaining commercial viability.

Commercial Licensing Models for SMEs

Licensing is the bridge between IP protection and monetization. Common SME models include:

• Enterprise Licensing: Fixed-fee internal use with scalability clauses
• Original Equipment Manufacturer (OEM)/White Label Licensing: Embedded model licensing for third-party resale

The chosen licensing model should align with IP protection strategies. Trade secrets can support SaaS, patents can enable OEM, and trademarks can contribute to white-label branding.

Contract Governance and Enforcement

All agreements should be indexed to an AI Contract Register that is cross-referenced with IP audits. Enforcement mechanisms, such as termination clauses, monitoring rights, and auditing provisions, should become a standard practice of business. SMEs can use automated contract lifecycle management software to maintain compliance efficiently.[21]

Practical Takeaways

Contracts are more than mere legal formalities. A disciplined contractual ecosystem, developed from modular and standardized templates, can enable SMEs to scale AI monetization while also protecting core assets from leakage or misuse.

Governance, Risk, and Compliance

The Need for AI Governance

AI governance is the final layer of the AI capital stack. This refers to ensuring that every AI asset is managed responsibly, transparently, and in accordance with the law and business ethics. For SMEs, governance may sound like a burden reserved for larger enterprises, but it can be one of the most cost-effective forms of risk insurance an emerging company can consider.

Governance structures do not need to be complex. Developing a hierarchy of responsibilities—Board Oversight, Executive Management, and Technical Stewardship—should be sufficient and scalable for most SMEs. The board sets AI policies and risk tolerance; executives allocate resources and ensure compliance; and technical leaders monitor performance, bias, and data integrity. These three aspects of AI leadership within a firm can ensure accountability without excessive bureaucracy or overhead costs.

The Governance Architecture for SMEs

A practical SME framework can be structured as follows:

Table 19: Levels of Governance for SMEs

Executive Board	Establishes an “AI and Data Governance Committee” responsible for reviewing ethical implications, regulatory exposure, and major IP filings.
Management	The CEO, CTO, or Chief IP Officer (CIPO) oversees AI asset registers, compliance with data laws, and ESG reporting.
Operations	Engineers and data scientists implement internal controls, e.g., model versions, data lineage tracking, and validation logs.

 

Key Compliance Domains

AI governance intersects with multiple regulatory and ethical domains:

• Data Privacy: Compliance with the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), HIPAA, and sector-specific confidentiality rules
• Explainability and Fairness: Meeting the European Union’s AI Act and National Institute of Standards and Technology Risk Management Framework (NIST RMF) guidelines for transparency and bias detection
• Cybersecurity: Ensuring the integrity and security of AI pipelines and cloud infrastructure
• Sustainability and ESG: Measuring environmental efficiency and social responsibility in AI operations

Adherence to these frameworks will become increasingly essential for SMEs as government agencies and market conditions continue to adopt and fine-tune their standards for compliance.[22]

AI Risk Taxonomy and Mitigation

AI risks can be grouped into a practical framework for integrating governance into day-to-day operations, each with corresponding mitigation strategies:

Table 20: AI Risks and Mitigation Strategies

Risk Category	Description	Mitigation Strategies
Data Risk	Unauthorized use, bias, or corruption of training data.	Data lineage tracking, access controls, and anonymization.
Model Risk	Overfitting, bias, or opacity in model behavior.	Independent validation, benchmarking, and model interpretability tools.
Operational Risk	System failure, downtime, or cyberattack.	Redundant infrastructure, SLAs, cyber-insurance.
Legal Risk	IP infringement, liability for AI decisions.	Layered IP protection, contractual indemnities, and regulatory audits.
Reputational Risk	Public backlash, ethical concerns.	Transparent communication, ethics statements, and third-party review.

 

ESG Integration

Embedding ESG alignments into AI governance can also mitigate risk, enhance brand trust, and maintain investor confidence.

Table 21: Levels of Governance for SMEs

Environmental	Energy efficiency of data centers and model training.
Social	Fairness, accessibility, and diversity in AI outcomes.
Governance	Transparency in data usage and decision logic.

 

The AI Governance Checklist

SME boards and executives can follow the checklist below as a roadmap towards robust AI governance:

Table 22: AI Governance Checklist

Inventory	Have all AI assets been classified and registered?
Ownership	Are IP rights and data licenses documented and enforceable?
Accountability	Is there clear assignment of AI oversight roles?
Compliance	Are privacy, export control, and ethics frameworks met?
Testing	Are models periodically validated and bias-tested?
Security	Are access, storage, and incident response systems adequate?
Transparency	Is the use of AI being disclosed to customers and employees?
Documentation	Are policies, training logs, and governance records retained?
Auditability	Can the organization demonstrate control during due diligence?
ESG Reporting	Are AI metrics integrated into sustainability reports?

 

Conclusion

AI is the new intangible capital of the global economy.[23] According to the Boston Consulting Group, AI future-built companies can achieve up to five times the revenue increases and three times the cost reductions from AI implementation; but only 60% of such companies have obtained measurable benefits relative to their investments.[24] For SMEs, the challenge is not an issue of technology, but rather effective and proactive management. Applying the proper IP regimes—copyright for creative expression, patents for innovation, trade secrets for know-how, trademarks for identity, and contracts for control—can provide the legal mechanisms by which abstract technologies are recognized as property.

When layered with valuation methods drawn from IAS 38 and market knowledge, this approach can convert AI into capital; when reinforced by governance, it ensures ethics and compliance are aligned with ESG principles. Together, these actions have the capacity to form a sustainable, competitive advantage.

SMEs can aim for a 90-day plan for AI governance, with the first three months focused on transitioning from informal innovations to a structured, monetizable AI capital framework.

The 90-Day Implementation Plan

Days 1–30: Asset Inventory and Stewardship

• Conduct a comprehensive AI IP audit
• Assign asset stewardship roles (Data, Model, Algorithm, Infrastructure, Application)
• Create a preliminary AI Asset Register and cross-reference contracts

Days 31–60: Protection and Monetization

• File provisional patents or copyright registrations where appropriate
• Execute NDAs and IP assignment agreements with all contributors
• Develop model licensing or API monetization plans

Days 61–90: Governance and Reporting

• Establish an AI oversight committee or designate a responsible executive
• Implement the AI Governance Checklist as a recurring process
• Prepare ESG-aligned AI performance and compliance summary for investors or board review

AI is not merely code; it is capital. It can be recognized, protected, insured, and monetized like other corporate assets. Organizations that internalize this discipline of AI governance and capitalization will define the next generation of value creation.

This article was previously published by J.S. Held Insights, March 2026, and is republished here by permission.

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James E. Malackowski is the Chief Intellectual Property Officer (CIPO) of J.S. Held and Co-founder of Ocean Tomo, a part of J.S. Held. In 2025, the Licensing Executives Society International (LES) recognized him with its highest honor: the LES Gold Medal. In 2022, he was inducted into the IP Hall of Fame and received the Q. Todd Dickinson Award for significant contributions to IP as a business asset. He is only the seventh person honored with both the LES Gold Medal and IP Hall of Fame inclusion. Mr. Malackowski has served as an expert on more than 100 occasions on intellectual property economics, including valuation, royalty, lost profits, price erosion, licensing terms, venture financing, copyright fair use, and injunction equities. He has substantial experience as a board director for leading technology corporations, research organizations, and companies with critical brand management issues.

Mr. Malackowski may be contacted at (312) 327-4410 or by e-mail to james.malackowski@jsheld.com.

Eric T. Carnick is an experienced expert who has testified in federal court and arbitration matters. He is a Managing Director in the Intellectual Property Disputes Financial Expert Testimony practice with Ocean Tomo, a part of J.S. Held. Mr. Carnick’s over 15 years of consulting experience includes the analysis and quantification of economic damages arising from patent, trademark, trade secrets, copyright infringement, and breach of contract in more than 100 matters. He has a vast knowledge base of financial issues and theories related to intellectual property and breach of contract litigation from discovery to trial.

Mr. Carnick may be contacted at (312) 377-4860 or by e-mail to eric.carnick@jsheld.com.

David Ngo is a Senior Analyst in the Intellectual Property Disputes Financial Expert Testimony practice at Ocean Tomo, a part of J.S. Held, working out of the San Francisco office. The practice quantifies economic damages arising from intellectual property disputes and provides general litigation support. Mr. Ngo assists in the valuation of intellectual properties for companies across multiple industries by applying his knowledge to financial statement analysis, market research, and consultation to develop complex models for clients.

Mr. Ngo may be contacted at (415) 946-2565 or by e-mail to david.ngo@jsheld.com.

 

References

[17] https://www.copyright.gov/orphan/reports/orphan-works2015.pdf, https://etisc.wipo.int/news/brand-finance-79-ip-asset-value-remains-books-underutilized-corporate-balance-sheets

[18] https://www.copyright.gov/orphan/reports/orphan-works2015.pdf, https://etisc.wipo.int/news/brand-finance-79-ip-asset-value-remains-books-underutilized-corporate-balance-sheets

[19] https://www.ifrs.org/issued-standards/list-of-standards/ias-38-intangible-assets/, https://www.datastudios.org/post/how-to-classify-and-measure-intangible-assets-under-ifrs-and-us-gaap, https://www.pkf-l.com/insights/capitalising-ai-tools-accounting-ias-38/, https://kpmg.com/mt/en/home/insights/2021/05/capitalisation-of-internally-generated-intangible-assets.html

[20] Given the current regime surrounding LLM training data and retrieval-augmented generation (“RAG”) related to copyrighted content, adjustments under the market approach are likely necessary to understand fair market value, as the market has been significantly depressed by big tech’s willingness to take such content without compensation to copyright holders.

[21] https://legal.thomsonreuters.com/blog/what-is-contract-lifecycle-management/, https://aavenir.com/contract-automation/

[22] https://www3.weforum.org/docs/WEF_Responsible_AI_Playbook_for_Investors_2024.pdf, https://www.orrick.com/en/Insights/2025/08/AI-in-Transactions-What-Is-the-Impact-of-AI-on-Business-Transactions-AI-FAQ-Series, https://www.ey.com/content/dam/ey-unified-site/ey-com/en-gl/insights/public-policy/documents/ey-gl-eu-ai-act-07-2024.pdf, https://hai.stanford.edu/ai-index/2025-ai-index-report, https://www.deloitte.com/content/dam/assets-zone1/apac/en/docs/collections/2024/apac-ai-at-a-crossroads-report.pdf

[23] https://cmr.berkeley.edu/2025/08/adoption-of-ai-and-agentic-systems-value-challenges-and-pathways/

[24] https://www.bcg.com/publications/2025/are-you-generating-value-from-ai-the-widening-gap