Build Versus Buy AI: The Questions IP Leaders Should Really Be Asking
Artificial intelligence is rapidly changing how intellectual property teams manage innovation, reduce administrative burden, and make strategic decisions.
Across industries, boards are asking CEOs the same question: “What’s our AI strategy?” As CEOs look to IT and operations leaders to demonstrate how AI can improve performance, accelerate innovation and create competitive advantage – that pressure flows downward throughout the organization.
Many organizations exploring AI-powered solutions find themselves facing a familiar question. Should we build our own AI capabilities or buy them from a software provider?
It's a reasonable question. But increasingly, it's the wrong one.
The organizations creating the most value from AI are not simply deciding whether to build or buy. They're determining where AI can deliver meaningful business outcomes, what foundations must be in place first, and how to balance innovation with governance, accountability and trust.
For IP leaders, the more important question may be: What should we build, what should we buy and what capabilities are too important to reinvent?
The Real Question Isn't Whether to Build versus Buy AI
Not long ago, AI itself was the differentiator. Today, access to advanced AI models has become democratized. Most organizations can access the same frontier models through cloud providers, development platforms, and software vendors.
What’s much harder to replicate is everything surrounding those models.
The real competitive advantage does not come from the model itself. It comes from the intelligence layer around it. The trusted data, domain expertise, workflow integration, governance controls, permissions structures, institutional knowledge and business context, among other factors. These more than the latest model enable AI to produce accurate and actionable outcomes.
In other words, the real question is no longer whether to build or buy AI. It's whether to build or buy the harness.
For IP organizations, an effective harness includes multiple access points to critical data that includes decades of portfolio history, prosecution records, invention disclosures, trademark activity and licensing information, among others. The harness’s business rules and governance requirements enforce safety policies, and handle permissions to prevent destructive actions. It is trustworthy because it can transparently demonstrate its ability to process complex AI operations that need to be highly repeatable and accurate.
As Anaqua’s Chief Product Officer Toni Nijm recently noted, “Everyone has access to similar AI models. But very few have 30 years of IP context and expertise, and the permissions framework required to use that information safely and effectively.”
This shifts the conversation from a technology decision to a data, governance, and time-to-value decision.
Start with the Foundation: System of Record → System of Intelligence → System of Action
Many AI initiatives fail not because the technology is flawed, but because organizations attempt to layer intelligence onto unreliable information. Successful AI adoption follows a progression.
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System of Record - A trusted, authoritative source for IP data, documents, workflows, deadlines, and portfolio history.
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System of Intelligence - AI capabilities that can analyze information, surface insights, identify risks, generate recommendations, and support decision-making.
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System of Action - The ability to automate work, execute tasks and drive business outcomes within governed workflows.
The sequence matters.
Intelligence layered on top of incomplete or inaccurate records creates confident but unreliable recommendations. Action layered on top of unreliable intelligence simply allows organizations to make mistakes faster.
This is where many AI projects lose momentum. Organizations become excited about what AI can do before establishing confidence in the data that powers it. Whether building or buying, the strongest foundation remains the same: trusted records first, intelligence second and automation third.
The Hidden Cost of Building
Building an AI solution offers clear advantages. Organizations can tailor functionality to specific workflows, prioritize use cases based on business needs, and maintain direct control over development priorities.
However, building often involves much more than developing an interface to a large language model.
Teams must maintain data pipelines, retrieval architectures, permissions structures, workflow integrations, testing frameworks, security controls, model monitoring, compliance reviews, performance benchmarking and ongoing model updates.
Here’s the problem: What appears to be a technology project can quickly become an ongoing platform commitment.
For IP teams specifically, engineering effort is only part of the challenge. Years of institutional knowledge and domain-specific context must also be incorporated before AI can produce reliable, business-ready results.
The question is not whether an organization can harness AI. The question is whether building the surrounding infrastructure is the best use of resources when mature alternatives already exist.
Avoiding Death by Fragmentation
As organizations experiment with AI, many start by deploying multiple point solutions across different teams and functions. One tool summarizes documents. Another drafts content. A third performs research. A fourth assists with docketing.
Individually, each tool may deliver value. Collectively, they can create new silos, duplicate information, increase governance complexity, and fragment user experiences. The complexity increases every time you find a one-off use case. Creating workarounds for edge cases is complicated. Left unchecked, they eventually drive users back toward using spreadsheets, or even paper. Additionally, this could turn into a massive and costly headache for the organization to manage and maintain. Leveraging an IPMS vendor with an extensive history and ability to quickly integrate edge cases into its end-to-end framework significantly reduces data loss, costs and IP team frustration.
At a recent IP roundtable Anaqua hosted in London, it was discussed the next phase of AI adoption will favor platforms that combine domain expertise, proprietary data, workflow integration, and AI capabilities within a single environment rather than relying on disconnected standalone applications. This mirrors what Forrester’s own research [1] has found.
For IP organizations, this raises an important question: Does each new tool strengthen the broader innovation ecosystem, or does it create another island of information? The answer often determines long-term success more than any individual AI feature.
Four Questions Every AI Buyer Should Ask
Whether evaluating a commercial platform or considering an internal build, decision-makers should ask four fundamental questions.
- Where Does the Context Come From?
- How is Accuracy Measured?
- What Happens When the System Is Unsure?
- Can It Act, and Under Whose Authority?
What information feeds the AI's recommendations? Is it drawing from trusted portfolio data, historical decisions, business priorities and workflow records? Or does it primarily rely on publicly available information?
How is performance evaluated across jurisdictions, practice areas, document types, languages and use cases? Broad claims of accuracy should always be supported by measurable results.
Does the AI identify uncertainty and escalate to a professional, or does it confidently produce an answer regardless of confidence level? Understanding how a system handles ambiguity is often more important than understanding how it performs under ideal conditions.
Can recommendations be translated directly into workflow actions? If so, who approves those actions and what controls exist to ensure accountability?
These questions frequently reveal more about long-term value than a feature demonstration ever will.
Governance Is Not a Policy Document
Many organizations respond to AI adoption by creating guidelines for acceptable use. While policies are important, governance cannot depend solely on employee compliance.
Effective AI governance requires enforceable business rules or controls embedded directly into the technology.
Organizations should evaluate whether solutions provide:
- Authority controls defining who can approve actions.
- Confidence thresholds that determine when human review is required.
- Evidence retention that preserves supporting information and decision rationale.
- Jurisdictional controls governing privilege, residency, confidentiality, and export restrictions.
- Reversibility and auditability so actions can be reviewed, traced, and corrected.
Governance is often viewed as an obstacle to AI adoption. In reality, governance is what makes responsible adoption possible. It is the reason professionals can confidently stand behind AI-assisted work.
AI is like a Brilliant Medical Resident, Not the Attending Physician
As Toni Nijm poignantly stated: “AI is less like an attending physician and more like a brilliant medical resident. It’s capable of analyzing vast amounts of information, identifying patterns, surfacing insights and recommending next steps. Yet the ultimate responsibility still rests with the expert making the final decision.”
The same is true in intellectual property. AI can amplify expertise, increase capacity and accelerate outcomes, but it cannot replace the judgment, accountability and experience that professionals bring. The organizations that gain the greatest advantage from AI will use it as a force multiplier for their experts, not a substitute for them.
Adoption is as much a people challenge as a technology one
According to Guy Champniss (Harvard Business Review) research on The Psychological Costs of Adopting AI[2], even the most powerful AI solutions deliver little value if users don't trust the outputs or understand how recommendations are made. For IP professionals, concerns around accountability, credibility, confidentiality, and risk are real. That's why successful AI adoption starts with trust: proving value through targeted use cases, establishing clear governance, and expanding usage as confidence grows. In the end, trust isn't mandated, it's earned.
Why This Matters for European Organizations
For organizations operating across Europe, AI adoption is more than productivity. Beyond data residency, organizations must consider data sovereignty: where AI models originate and operate, which jurisdictions govern them, and how sensitive data is protected from external access or mandates. Combined with cross-border restrictions, client confidentiality, export controls, and evolving regulations such as the EU AI Act[3] and NIS2 Directive[4], these requirements are driving organizations toward platforms with governance, compliance, and jurisdictional controls built in. Increasingly, control and accountability matter as much as AI capability.
The Future Is Hybrid
For most organizations, the answer will not be purely build or buy. The future will be a hybrid approach, not a one-size-fits-all. Administrative and docketing teams may benefit from greater automation in the process of incoming communications (Patent & Trademark Offices and Outside Counsels primarily). R&D teams can use AI to identify and prioritize innovation and shorten the time to patent. Attorneys can accelerate research, analysis, and drafting. Trademark professionals can strengthen monitoring and brand protection, while governance teams can help ensure AI is deployed responsibly. The key is to match AI capabilities to the unique needs of each function.
Organizations won't gain a competitive advantage simply by adopting AI. They will succeed by embedding AI into the full innovation and IP lifecycle and building a trusted operating model around it. The leaders will combine high-quality data, domain-specific intelligence, governed execution and human expertise and decision-making into a unified system that delivers measurable business value and sustainable advantage.
Additional reading you may be interested in:
[1] Forrester Blog, 11th February 2026
[2] Champniss (HBR) — The Psychological Costs of Adopting AI, 1st May 2026