On paper, few business functions might seem as ready for generative AI (GenAI) as intellectual property (IP). Many IP tasks revolve around structured workflows, processing of dense text, and working with comprehensive, well-documented datasets, factors that make applying large language models (LLMs) promising.
Yet, despite these promising conditions, the uptake of AI solutions within IP functions is stalling.
While departments like marketing, product development, and IT are racing ahead with GenAI adoption, IP is alongside legal, risk, and compliance lagging behind. According to McKinsey’s most recent State of AI report, these functions are still hesitating to move past isolated experiments. Concerns about precision, confidentiality and governance are key factors slowing progress. IP professionals are understandably cautious, as IP actions and tasks can significantly influence both competitiveness and risk. Every word carries weight, and the risk of exposing sensitive data could impact patentability and compromise integrity.
But there are signs of momentum. In Questel’s 2025 IP Outlook, 77% of IP professionals surveyed expressed excitement about AI, with 58% already using AI tools regularly, and seeing real benefits. These benefits, although impactful, often remain confined to pilot phases, with many projects struggling to transition from demonstration models to scalable solutions. Many IP departments are also limiting their use of AI to fringe use cases, not addressing core functions. This hesitance to scale beyond the preliminary stages and caution on where to use AI for core tasks, illustrates both the opportunities and barriers in effectively integrating GenAI into regular IP functions.
At Konsert, we see IP teams consistently hitting two major roadblocks with AI adoption. First, they often stumble at the start, unsure of which AI applications will yield the most benefit. Then, those who do start often find themselves stuck trying to figure out how to make their solutions scalable. To overcome these challenges, IP departments need a structured plan, well-defined objectives, a narrative which bring people onboard and a compelling business case in order to successfully guide AI integration.
Prioritise high-impact use cases
Fundamentally, it’s about understanding why AI tools are being introduced and where they can unlock the most value.
The aim is not to chase flashy features, but to enhance efficiency and innovation in IP processes This could involve reducing the time spent on e.g. prior art searches, automating repetitive docketing tasks, or improving the consistency of patent drafting. Start by taking a data-driven look at your workflows. Where are teams overwhelmed or overextended? Are you paying outside counsel for tasks that could be partially automated? What automation should be driven internally or by external service providers? Is there a backlog in monitoring trademarks or evaluating third party IP risks? These insights into workflow inefficiencies suggest where GenAI could provide significant value.
Also, don’t just focus on efficiency. Speed and cost savings are great, but there’s also value potential in improving quality, enabling new capabilities, and transforming repetitive tasks into stimulating ones. Creating stimulating, AI-enabled work environments not only boost productivity but also attract the next generation of top talents.
Whatever the case, you’ll need solid data to support your case internally. Map your workflows, analyse where time and resources are allocated, identify where quality improvements matter most, and estimate ROI in tool costs as well as outcomes.
Establish a strong technological foundation
Once priorities in terms of use-cases are clear, the next step is to build a solid foundation with data and infrastructure. Even the most promising GenAI tools require a combination of high-quality data and a robust infrastructure layer to be effective.
In the IP landscape, AI solutions generally fall into three categories: (1) general-purpose tools like ChatGPT, (2) distinct IP tools powered by AI features, often focused on very specific use cases and requiring structured data from separate sources, and (3) integrated platforms embedding GenAI in analytics systems.
All AI-enabled IP tools rely on clean, quality data to function effectively. Without it, these tools can miss critical details and erode user trust. Many IP functions are still on outdated systems, using fragmented data, or working in siloed workflows. To effectively address AI use-cases, a careful redesign towards a unified and scalable data model is needed.
While a large-scale data redesign does not need to happen overnight, a push in this direction will enhance AI effectiveness. It will facilitate seamless onboarding of new data sources, allow for dynamic configuration of rules and workflows as needs evolve, and ensure robust integration with external platforms, covering all aspects from docketing to analytics.
Treat AI as a combined technology and organisational transformation
To fully leverage GenAI, it must be treated as both an IT transformation and an organizational change.
On the IT side, this involves developing tools with functionalities that align with relevant use cases, ensuring they are embedded into daily operations to enhance efficiency and innovation. Organizationally, it requires adopting new ways of working and redesigning workflows, supported by introducing new roles and structures to assist personnel through the transition.
However, most IP departments aren’t prepared for such holistic transformation. They often lack the necessary AI and software development skills and have not yet created a people-change roadmap to guide adoption or developed an upskilling plan to successfully embed new ways of working.
To ensure that changes are sustainable and beneficial, IP leaders must take proactive steps. A critical first move is to establish a cross-functional AI program that includes representation from IP, IT, and AI domains.
Leaders should adopt a gradual, phased integration approach rather than pursuing large-scale rollouts. This involves building an evolutionary roadmap that begins with focused, low-risk pilot projects. These pilots are crucial for achieving a quick proof of value, generating momentum, building internal credibility, and laying the groundwork for broader adoption.
Additionally, identifying internal champions who actively promote adoption, gather feedback, and help scale initiatives across teams is essential for sustaining long-term impact. Equally important is fostering a culture where experimentation is encouraged. Teams need support to test new tools, fail fast, and learn quickly while remaining accountable for quality and integrity of their outputs.
With a disciplined approach, IP teams can transform GenAI’s theoretical fit into real competitive advantage, moving from flashy demos to measurable impact on speed, cost and quality.
