Generative AI for SMEs with BioTalent

There are significant advances occurring in several areas of biotechnology, including biotech drug discovery, personalized medicine, genomics, gene editing, predictive diagnostics, and synthetic bioinformatics. These advancements are not solely driven by machine learning. Instead, they are increasingly the result of combining various AI applications. This includes integrating machine learning, deep learning, GenAI interfaces, and natural language processing. Often, it's the synergy of all these different types of AI that is driving progress in these biotechnology fields.

Rob: 

As an HR satellite organization for SMEs in the biotechnology sector, we observe that contrary to popular belief, the industry is predominantly composed of small and medium-sized enterprises rather than large pharmaceutical firms. These SMEs operate as businesses, engaging in marketing, sales, and financing with investors and stakeholders. The efficiency gains offered by GenAI are attractive, but from a practical standpoint, these SMEs are uniquely driven by their intellectual property - be it patented formulas for medical devices or specific plant genomes.

The intersection of GenAI and IP law is currently a "Wild West" scenario. No specific laws have been created yet, though lawsuits are emerging in Europe and the United States. In Canada, law firms are scrambling to advise their clients. The crux of the issue lies in how companies farm out their IP, which is the cornerstone of their commercialization and market plans. There's significant uncertainty regarding the use of GenAI in areas such as genetic code analysis or protein folding for enzyme creation in plants or animals.

In the biotech and pharmaceutical industries, molecules typically lose patent protection after several years. Companies rely on these patent-protected periods to generate revenue. GenAI presents an opportunity to expedite scientific innovation by leveraging existing information, but it also raises concerns about IP ownership. When GenAI is used to modify molecular structures or enhance medical devices, it's unclear who owns the resulting IP - the company, the GenAI platform, or neither.

It's crucial to understand that the biotech industry is founded on a high failure rate, with approximately 90% of drugs and molecules never reaching the market. This contributes to the high cost of successful drugs, as the 10% that succeed must compensate for the 90% that fail. The rapid advancement and disruptive nature of GenAI, evolving every 4-6 months, further complicates this landscape.

Mark:

The pace of technological advancement far outstrips the speed of legislative processes. Governments worldwide are grappling with how to implement effective guardrails without stifling innovation.

Rob:

This creates a challenging environment where scientific curiosity and advancement often precede the establishment of regulatory frameworks. As with previous scientific breakthroughs, the biotech industry may proceed with GenAI applications before fully understanding or addressing the potential ramifications, driven by the inherent curiosity that propels scientific progress.

Mark: 

The rapid evolution of GenAI presents challenges for business integration. Hundreds of applications emerge daily, and a shake-out is expected. When deciding on integration or investment, businesses must consider what they're implementing and how they're training staff.

Many companies have been training staff with large language models or on IP they don't own. Even some public tools have faced controversy over using IP without rights. Dominant open-source large models are likely to emerge, offering meaningful applications tuned to specific businesses while protecting privacy, data, and security. Industry-specific tools will also be important.

Rob: 

Critical thinking skills are crucial for applying any version of AI to business strategies. Procrastination should be avoided, as businesses unable to keep up may clear the way for those who can adapt.

Understanding the legal, governmental, and ethical aspects of GenAI is important, despite these typically lagging behind technological advancements. Potential misuse is a concern, particularly in biosciences and biotechnology, where IP protection is crucial for medical advancements.

Mark:

The ability to evolve and manage in a disruptive environment is a key skill. Fundamental change management skills, including digital literacy across all levels of employees, are essential.

Rob:

Embracing change and innovation is crucial for businesses to stay competitive, especially with rapidly evolving technologies like GenAI. This principle applies to various aspects of business operations. For instance, a company hiring in Canada that doesn't embrace diversity and inclusion risks failure. Similarly, adopting new technologies requires a mindset that promotes innovation and good citizenship.

To integrate GenAI effectively, businesses should increase core competencies by implementing policies and incentives for employees to use GenAI. This approach provides control over its application, as employees decide how to use the technology within company guidelines. By fostering an environment that values both technological advancement and inclusive practices, businesses can better navigate the challenges of a rapidly changing landscape.