This article was developed from our recent interview with Dr. Justin Trombold.
Watch the video above or continue reading below. If you’re interested in viewing the full interview, click here.
Scaling Generative AI for Internal Solutions
In the evolving landscape of artificial intelligence, ensuring the scalability of generative AI (Gen AI) solutions is a critical challenge that businesses face.
Dr. Justin Trombold, a 10EQS Collaboration Manager and AI expert, delves into this subject, highlighting the key factors and best practices for successfully scaling Gen AI projects.
His insights shed light on the non-negotiables in various industries, particularly banking, and emphasize the importance of a well-thought-out approach to AI integration.
Understanding Industry-Specific Non-Negotiables
Dr. Trombold stresses the importance of recognizing non-negotiables unique to each industry. In banking, for instance, stringent regulatory requirements play a crucial role.
Organizations in such regulated industries must be acutely aware of the compliance landscape. “In banking, you have very real regulatory hurdles,” Trombold points out, suggesting a cautious approach to AI projects that might require regulatory approval.
This preemptive strategy ensures that AI initiatives remain internally focused and not inadvertently breach compliance boundaries.
Emphasizing the Importance of Adoption and Use Case Assessment
A pivotal element to scalability, as Trombold explains, is the adoption of AI solutions by the team. “Their attitude towards work… has to be one of adoption of generative AI solutions,” he says.
Scaling AI solutions becomes significantly more challenging without this acceptance and understanding at all levels, from leadership to the technology team.
Furthermore, he advocates for a thorough assessment of use cases, which includes embracing the concept of ‘failing fast’—a critical aspect in the fast-paced world of technology solutions.
Addressing Data Misconceptions and Strategy
Dr. Trombold also addresses a common misconception about Gen AI – its ability to resolve data issues autonomously.
He clarifies that Gen AI doesn’t eliminate the need for a robust data strategy. In fact, it makes data strategy and architecture more crucial than ever.
The success of Gen AI solutions hinges on the quality of the data fed into them.
“How confident are we in the cleanliness, security, and accuracy of the data?” Trombold asks, emphasizing that confidence in these areas is a prerequisite for moving a solution from proof of concept to scalable implementation.
Summary
Dr. Trombold’s insights offer a pragmatic roadmap for businesses looking to scale their generative AI solutions.
Understanding the unique challenges of one’s industry, fostering a culture of AI adoption, conducting thorough assessments, and maintaining a strong data strategy are foundational to the success of Gen AI projects.
As companies navigate the complexities of AI integration, these best practices serve as guiding principles, ensuring that AI solutions are not only innovative but also scalable and aligned with the organization’s strategic goals and compliance requirements.
If you’re implementing generative AI across your internal processes, reach out to us to learn how we can support your organization.