Artificial intelligence (AI) research and development are at the forefront of technological advancements, revolutionizing industries and pushing the boundaries of what’s possible.
In a recent conversation, two AI professionals, Alex and Kat, discussed their experiences and challenges in scaling AI-applied research within their organizations. Here, we delve into their insights and approaches.
Scaling AI Research
Scaling AI research and development in organizations is not without its challenges. Here are some key takeaways from the conversation:
1. The Reality of Funding: Alex highlights that for smaller startups with limited funding, scaling AI research can be a daunting task. R&D is expensive, and it may not be feasible to invest in significant research without substantial backing.
2. Balancing Innovation and Operations: Kat reflects on her journey, where she has found herself immersed in the operational aspects of managing a growing team and maintaining product quality. Balancing these day-to-day responsibilities with innovative research is a challenge many AI professionals face.
3. Customer-Driven R&D: Alex emphasizes that R&D in smaller organizations often occurs when there’s a specific customer demand that requires innovative solutions. In these cases, the pressure to deliver can drive the research efforts.
4. Innovative Funding Sources: For more extensive research projects that require significant investments, securing additional funding is essential. Kat points out that getting a large injection of capital allows for more extensive, long-term R&D.
Balancing R&D and Operational Responsibilities
Both Leaders acknowledge the challenges of striking a balance between day-to-day operational responsibilities and research. For Alex, the primary focus is to ensure existing models and services are performing optimally. Any additional research or innovation must align with the organization’s product strategy and customer needs. Kat, on the other hand, has found herself navigating the complex world of managing a growing team, taking on more operational roles, and dealing with customer requirements. The ideal scenario for her would involve spending more time on R&D, but practical constraints have made this challenging.
Key Takeaways
Scaling AI applied research in an organization is an intricate process, influenced by factors such as funding, customer demand, and product strategy. Both Alex and Kat recognize that smaller startups with limited resources often have to prioritize operational responsibilities over extensive research. However, customer-driven R&D can catalyze innovation.
The challenges of balancing day-to-day operations with innovative research require creative solutions. While securing additional funding can enable more in-depth research efforts, it’s crucial to align R&D with customer needs and product strategies. For AI professionals, finding the right equilibrium between operational and research activities is an ongoing journey, shaped by the unique circumstances of each organization.
As the AI landscape continues to evolve, the strategies employed by Alex and Kat shed light on the intricacies of scaling AI research in today’s dynamic and competitive environment.
Disclaimer: All guests’ views are their own and do not represent their employers’.