Transforming Legal Teams with AI to Make IP a Growth Driver – with Leaders from Clarivate and AbbVie

This interview analysis is sponsored by Clarivate and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page.

Legal and intellectual property (IP) teams are facing mounting pressure to move beyond traditional administrative functions and serve as strategic business enablers. From managing complex regulatory environments to extracting commercial value from innovation, the expectations are rising — and so are the risks. 

Many organizations still rely on fragmented systems for overseeing intellectual property and AI tools, making it difficult to scale oversight, uncover new revenue channels, or mitigate enterprise-wide exposure.

Patent filings are one clear signal of this strategic shift. According to the World Intellectual Property Indicators 2024 report, global patent applications surpassed 3.46 million in 2022 — the twelfth consecutive year of growth. 

As WIPO notes, this sustained increase reflects a growing global demand for protection of innovation and investment. Yet despite this volume, many firms struggle to benchmark their portfolios against evolving markets, leaving valuable assets underutilized or misaligned with business objectives.

The expanding use of AI introduces complexities, particularly in governance. According to the OECD’s 2024 State of Implementation of the AI Principles report, only 19% of surveyed enterprises have internal policies for the cross-functional governance of AI systems. This governance gap poses significant risks, including potential misuse and lack of accountability, which are critical concerns for legal and compliance teams, especially when AI tools are integrated into sensitive workflows like document review and contract generation.

Meeting these challenges requires more than internal guardrails. Legal and IP teams need frameworks that integrate compliance with proactive decision-making and align closely with IT, procurement, and line-of-business leaders.

Emerj recently featured discussions with two IP experts in a special series of the ‘AI in Business’ podcast to shed greater light on how AI can be deployed to help solve these challenges for IP teams. 

In their respective appearances on the podcast, Shandon Quinn, Vice President of Patent Intelligence, Search, and Analytics at Clarivate, and Christo Siebrits, Senior Associate and General Counsel at AbbVie, share how legal and IP leaders can build systems that do more than manage risk — they help legal serve as a strategic engine for growth.

This article examines the following critical insights from both guests for IP leaders deploying AI in their operations:

• Benchmarking IP portfolios reveals growth opportunities: Competitive analysis frameworks help identify where patents can generate licensing revenue or improve market positioning.
• AI adoption for predictive patent strategy: Predictive analytics enables IP teams to assess the future relevance, commercial viability, and legal risk of patents — empowering smarter R&D investments, budgeting, and cross-functional alignment.
• Centralizing AI usage audits curbs legal risk: Proactive auditing of AI tools across teams ensures better procurement decisions and reduced liability exposure.
• Establishing AI governance across legal functions: Cross-jurisdictional policies allow legal teams to manage third-party models with consistency and transparency.

Benchmarking IP Portfolios Reveals Growth Opportunities

Episode:  How AI Is Reshaping Patent Strategy and Portfolio Management – with Shandon Quinn of Clarivate

Guest: Shandon Quinn, Vice President of Patent Intelligence, Search, and Analytics, Clarivate

Expertise: Patent Intelligence, Intellectual Property Strategy, AI Adoption in Legal Workflows, Portfolio Valuation

Brief Recognition: Shandon leads Clarivate’s global patent intelligence, search, and analytics operations, helping enterprise clients leverage AI-driven solutions for portfolio benchmarking and monetization. He previously held senior product leadership roles at Elsevier, where he oversaw acquisitions and product growth initiatives. A recognized expert in intellectual property and AI-enabled productivity across regulated industries, Shandon holds a BSE in Chemical Engineering from Princeton University.

Shandon Quinn begins his podcast appearance explaining to the executive audience that, for years, intellectual property management has been seen primarily as a defensive necessity — or a way to protect innovations from infringement and maintain competitive barriers.

But in a data-driven economy, IP is increasingly recognized as an active source of strategic intelligence. The challenge for many organizations, he emphasizes, lies in turning raw patent information into actionable insights that serve broader business goals.

Shandon then argues that benchmarking is the tool that transforms patent data from an administrative function into a growth engine. He explains that companies too often evaluate their IP portfolios in isolation, measuring success by counts and filing rates rather than by strategic relevance or market positioning: 

“We’ve historically seen IP teams focus on volume — how many filings, how many patents are live. But without context, those numbers don’t tell a business story. When you start comparing portfolios, you begin to see patterns that reveal where innovation is strong, where it’s stagnant, and where the market’s moving faster than you are. Those comparisons can spotlight areas to double down on or identify white spaces competitors are beginning to fill.”

— Shandon Quinn, VP of Patent Intelligence, Search, and Analytics at Clarivate

Quinn emphasizes that benchmarking allows companies to position their IP not only in relation to competitors but also in emerging technology categories that may redefine future markets. 

Using AI-powered analytics, platforms like Clarivate can map entire ecosystems of patent activity, revealing where innovation is concentrated or absent. For example, a diagnostics firm might discover it’s trailing in biosensor integration despite leading in data analytics — prompting new R&D investment.

These insights illustrate how IP management is evolving from a cost center to a contributor to business value. Quinn highlights that benchmarking portfolios against peers and assessing strategic directions allows IP teams to better understand their opportunities.

Shandon also notes that patent portfolios can generate revenue through licensing or sale, and that comparative analytics help identify which assets could be leveraged in the market.

Quinn notes that AI is intended to augment human expertise, not replace it. Patent analysis still requires deep domain knowledge, but AI can make that expertise scalable by compressing weeks or months of manual work into hours and organizing large datasets into actionable summaries.

Benchmarking and strategic portfolio assessment provide IP teams with levers to influence revenue generation or cost reduction, supporting more informed decision-making within the organization.

AI Adoption for Predictive Patent Strategy

As intellectual property portfolios grow more complex and global, IP leaders face an overwhelming volume of decisions — where to invest, when to renew, and which filings will actually create long-term enterprise value, according to the IP Business Academy’s 2021 lecture notes on IP strategy.

Quinn describes the complexity and demands of the field: “The head of intellectual property at many corporations, I think right now, is one of the hardest jobs in the world, anywhere, in any function, in any industry.”

Shandon highlights that AI tools enable faster identification of patents with strategic potential and support more informed assessments of where to focus resources. This can enhance not only legal decision-making but also collaboration with other parts of the organization, such as R&D.

Quinn emphasizes that while AI can surface actionable insights and make portfolio management more proactive, its greatest value comes from supporting — rather than replacing — expert judgment. By integrating AI into portfolio management, organizations can move beyond purely defensive workflows, unlocking more opportunities to align innovation strategy with broader business goals.

Centralizing AI Usage Audits Curbs Legal Risk

Episode: From Tool Sprawl to Defensible Value in AI for Legal 

Guest: Christo Siebrits, Senior Associate and General Counsel at AbbVie, AbbVie

Expertise: AI Governance, Legal Innovation, Compliance Strategy, Life Sciences Regulation

Brief Recognition: Christo leads AbbVie’s AI legal and governance strategy, guiding ethical and compliant adoption of emerging technologies across the enterprise. He previously served as Area Counsel for AbbVie’s international markets and as Legal Director for AstraZeneca across EMEA. Christo holds a Bachelor of Laws (LLB) and a BA in Law and Economics from Stellenbosch University.

Siebrits opens the conversation by highlighting the growing complexity legal and compliance teams face as generative AI becomes embedded across the enterprise. He points out that the pressing challenge isn’t just employees’ use of new tools, but the organization’s readiness to embrace, or contain, external AI solutions. Shadow AI — the unsanctioned use of third-party AI tools by employees — emerges, he reflects, where governance and comfort with third-party tools lag behind employee curiosity and business needs.

Siebrits emphasizes a practical approach to managing AI tools:

• Assess data sensitivity and risk tolerance: Identify which data must remain internal and which could be used with external AI platforms.
• Collaborate across functions: Work with IT, legal, data privacy, and business teams to understand all AI tools currently in use, both internal and external.
• Review and update governance regularly: Ensure policies evolve with emerging tools and regulatory requirements.
• Document decision-making: Keep a record of risk assessments and rationale for using internal versus external solutions.
• Maintain human oversight: Insert humans at critical junctures in AI workflows to validate outputs and reduce external risk.
• Foster ongoing communication and awareness: Educate teams about AI risks, including shadow AI, and create pathways for responsible experimentation.

Christo notes that in highly regulated industries like life sciences, untracked AI activity can lead to compliance breaches or confidentiality risks. He stresses that structured processes for auditing and monitoring AI tools help teams make better procurement and deployment decisions, reducing legal and compliance risk.

He adds that teams can balance internal and external tools by keeping highly sensitive data secure internally while leveraging third-party AI for lower-risk workflows, and that tracking adoption and outcomes helps ensure investments deliver measurable value.

Siebrits highlights that employees often don’t realize AI outputs can become business records or that sensitive prompts might be stored externally. He stresses that this makes education and awareness programs essential.

Establishing AI Governance Across Legal Functions

As AI adoption scales across different legal and business contexts, Christo notes that governance frameworks must be consistent yet adaptable — ensuring principles apply broadly while accommodating how legal teams actually work:

“You want to make sure that your governance principles are widely applicable but also responsive to how legal teams actually work. What does transparency look like in a legal process? It’s not just explainability of the model — it’s about whether the attorney understands the full context in which the model is being applied, and whether they’re trained and supported to use it appropriately. That’s where risk lives.”

— Christo Siebrits, Senior Associate General Counsel at AbbVie

The principle Siebrits underscores supports a broader vendor alignment narrative: leaders must weigh whether to build or buy solutions that meet their governance needs. A vendor might offer explainability but lack the retention policies a legal department requires, while in-house development provides control but can limit agility and scalability.

Embedding governance into procurement ensures that legal requirements are addressed from the outset. This includes aligning on jurisdictional risk, auditability, and human-in-the-loop protocols. Siebrits adds that even strong tools must earn trust over time. Early missteps can stall AI adoption across entire legal teams.

“If your first use case goes sideways,” he says, “you don’t get a second one.” Governance ensures that each step forward is deliberate, supported, and sustainable.