| |JULY 20259CIOReviewThe old model is Try-Slow, Fail-Slow, and does a lot of storytelling, driven by multi-year roadmaps and thus cannot keep up with the speed no matter how much tweaking on plans. To achieve AI execution excellence, we need rapid strategies and tactical combat decision-making mindsets, similar to venture mindsets to win business. Concretely, we need organizational commitment to change, starting from executive sponsorship and moving from top down on fast tracks.4. What does your current AI/ML team look like in terms of roles and expertise? How do you ensure the team has the necessary skills to stay ahead of the curve?We have the AI solution team actively interfacing with businesses or clients to align our technologies with the business objectives, and the data teams gather, cleanse, analyze, understand and augment data. The AI engineer and AIOps team are responsible for coding, training, finetuning, testing and producing AI services. We also have the teams for the model risk, including explainability, privacy, governance and AI ethics.To ensure the team stays ahead of the curve, I start with a comprehensive screening when building teams, ensuring they can demonstrate immediate technical skills and be culturally fit. They have to be passionate about using AI/ML to solve real-life problems and want to have fun while working hard, and have the motivation to stay ahead of the curve.AI is advancing fast to the point that it is quite difficult to keep up. I encourage my team to learn new things specific to their domain expertise, read blogs and technical papers often, and share their new knowledge with their teammates to sharpen each other.5. Can you describe the AI/ML technology stack currently in use at Raymond James? What considerations went into selecting these tools and frameworks?I am quite agnostic to technology stacks. Many stacks can make things work. I just make sure the broader team can agree on the tools and frameworks that we can stick with for the long term. Many cloud providers have comprehensive AI solution stacks. Most of my work is utilizing the AWS cloud ecosystem. I am sure AzureAI, GCP, etc. are as good. For development, I am a python/torch person. I like Nvidia DGX, especially for deep model training.6. What advice would you give to other financial services firms looking to adopt AI/ML technologies? What are the key considerations and potential pitfalls they should be aware of?Many firms have initiated AI/ML for quite some time, some firms are pretty mature, especially in classic ML. However, GenAI requires different skills and approaches to apply as it targets knowledge work automation and making data-informed decisions. Organizations need to have a defined AI Strategy that can support long-term digital transformation with strong executive sponsorship. To achieve AI execution excellence, we need rapid strategies and tactical combat decision-making mindsets, similar to the venture mindsets to win business
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