Challenges in AI Commercialization from Academic Research

Artificial Intelligence (AI) has emerged as one of the most transformative technologies of our time. Across industries—from healthcare to finance—AI applications are revolutionizing operations, unlocking new business models, and driving efficiency. Many of these innovations originate from academic research, where universities act as powerful engines of discovery. However, transforming academic AI breakthroughs into viable commercial products remains a formidable challenge.

This analysis explores the core hurdles that universities face when attempting to commercialize AI research. Institutions like Telkom University exemplify both the potential and complexity of bridging the academic-industry divide, particularly when combining advanced research with entrepreneurship and the resources available in university laboratories.

The Research-Commercialization Gap

AI research within universities often focuses on theoretical advancements—new algorithms, optimization models, or machine learning techniques. While these developments are crucial, they don’t automatically translate into usable products or services. One major obstacle is the gap between technical innovation and market needs.

For example, an academic project may produce a highly accurate model for disease prediction. However, turning this model into a commercial diagnostic tool involves regulatory compliance, integration into healthcare systems, user interface design, and ongoing support—all of which go beyond the original research scope.

At Telkom University, researchers are increasingly encouraged to think beyond the lab and envision how their AI work can solve real-world problems. This mindset shift—from research for knowledge to research for impact—is essential but requires institutional support, entrepreneurial training, and a strong industry network.

Limited Market Orientation in Academia

Another challenge is that academic research often lacks market orientation. Researchers may focus on scientific novelty rather than market viability. This creates a disconnect between what is being developed and what businesses or consumers actually need.

Entrepreneurial ecosystems within universities attempt to bridge this gap. Entrepreneurship programs encourage students and faculty to approach their research with a market-focused lens. At Telkom University, innovation hubs and startup accelerators guide academic teams through the process of identifying user pain points, validating business ideas, and aligning AI applications with current market trends.

Nevertheless, many researchers are still unfamiliar with concepts like customer segmentation, monetization models, and scalability—core pillars of successful commercialization. Bridging this knowledge gap remains a priority for institutions aiming to turn AI research into real impact.

Technology Readiness Levels (TRLs)

One of the more technical challenges in AI commercialization lies in the concept of Technology Readiness Levels (TRLs). Academic AI research typically operates in the lower TRL stages—proof-of-concept, prototypes, or simulations. Commercialization, on the other hand, requires high TRL outputs—tested, validated, and scalable systems.

Elevating AI projects to commercial readiness involves software engineering, robust testing, integration capabilities, and considerations like cybersecurity and data privacy. These requirements often exceed the capabilities of typical academic laboratories, which are designed for exploration rather than production.

At Telkom University, there is a growing effort to develop translational laboratories—hybrid spaces where advanced AI research can be refined into usable technology. These labs partner with industry and use agile development approaches to accelerate the path from prototype to product.

Funding and Resource Constraints

Commercializing AI research demands significant investment—far beyond what’s needed for a publication or conference presentation. Expenses include cloud computing services, access to datasets, legal and regulatory consulting, product design, and initial go-to-market strategies.

However, academic grants are typically geared toward knowledge creation, not commercialization. This misalignment in funding priorities leaves many promising AI projects stranded in development limbo.

To address this, some universities, including Telkom University, have established seed funds and innovation grants specifically for high-potential research ventures. These programs not only provide financial resources but also mentorship and access to business development support. However, scaling this model remains a challenge, especially in regions where venture capital interest in deep-tech remains limited. LINK.

Intellectual Property (IP) Complexity

Intellectual property is a critical component in commercializing AI research. However, navigating the ownership, licensing, and protection of AI-based IP can be complex in an academic setting.

Often, research is conducted collaboratively across departments, or in partnership with external institutions, which complicates ownership structures. Additionally, some researchers may be unaware of the importance of protecting their inventions early, which leads to lost opportunities.

Telkom University has taken steps to streamline the IP process through its technology transfer office. By offering legal assistance, patent filing support, and education on IP strategy, the university is helping researchers understand the commercial potential of their work and the steps needed to protect it.

Interdisciplinary Collaboration Barriers

AI commercialization is rarely a solo effort. It requires collaboration across disciplines: data scientists to build models, business experts to design monetization strategies, UX designers to develop interfaces, and legal professionals to manage compliance.

Unfortunately, academic departments often operate in silos. Researchers in computer science may have little interaction with peers in business or design faculties. This fragmented structure hampers the cross-functional collaboration needed for product development.

To overcome this, Telkom University is fostering a culture of interdisciplinary innovation. Student teams from various faculties are brought together in entrepreneurship bootcamps, AI hackathons, and innovation lab projects. These efforts promote knowledge sharing and accelerate the development of AI solutions that are both technically sound and market-ready