Art of the possible

Weights & Biases: What it really takes to successfully adopt generative AI

In this episode of The Art of the Possible, we dive into the transformative power of generative AI—and what it takes to successfully implement this revolutionary tool. Featuring experts from AWS, Weights & Biases, and Bloomberg Industry Group, the discussion centers on how to turn the promise of AI into real business results while avoiding common pitfalls. Kimberly Madia, VP of Product Marketing at Weights & Biases, explains why successful AI adoption hinges on iteration, experimentation, and robust tooling that builds confidence in outputs. “That’s exactly why we provide tools for experiment tracking and iteration,” she says, “so as you're making changes to prompts or models or data sets, you can quickly see and evaluate how those changes impact your metrics. Then, of course, when we move to deployment and monitoring and production, gathering feedback from users on how the application is performing is really important.” Tom Shen, Team Lead of Machine Learning at Bloomberg Industry Group, gives a behind-the-scenes look at how his teams are embedding AI across workflows—not just to impress customers, but to create lasting internal competency. “Our parent company's core values are very much our core values as well,” he says. “We are incredibly customer obsessed and any technology or content that we add to our platform, generative AI or not, needs to make sense for our customers. And this motto extends beyond just our subscribers. Even if we’re building new tools for our engineering team, we put a premium on their user experience as well.” AWS’s Salman Taherian, Worldwide GenAI Partner Lead – GSI/ISV, ties it all together, emphasizing that AI success isn’t just technical; it requires the right partners, data infrastructure, and governance to scale smartly. “Testing and experimentation isn't just about understanding technology,” he says. “It's really about building that organizational muscle for transformation and change management. And what I've learned is that companies who invest in this, they're really building that gen AI muscle, right? … That’s hard to do on your own, and that’s why partnerships like those between Bloomberg Industry Group, Weights & Biases, and AWS are so critical.” Whether you're an executive, engineer, or innovator exploring what’s next for AI in your business, this episode delivers practical insights and a grounded vision for the future. Listen to the full episode for tips on how to move fast, stay agile, and build with confidence.

Slalom: Building hyper-personalized connection with generative AI

Over the past few years, much of the conversation around AI and generative AI has focused on productivity – which is understandable, considering the potential of these tools to accelerate workflows and automate defined tasks. However, that’s far from the full story.   In episode 2 of The Art of the Possible podcast, Jennie Wong, Ph.D., Global Industry Director for Education at Slalom, and Patrick Frontiera, Higher Education Strategy Leader of IT and Campus Operations at AWS, explore an exciting, less-discussed use case: hyper-personalization.   Together, AWS and Slalom are using AI to drive hyper-personalization projects that have fundamentally transformed operations for a diverse group of customers. UCLA Anderson School of Management, for example, has seen a staggering 130% increase in donations from alumni who received hyper-personalized, generative AI-driven emails compared to more traditional control emails.   “The UCLA Anderson Advancement Team was already doing a certain level of personalization,” Wong said. “They were already using certain best practices. But [using generative AI], we essentially doubled down on behavioral science research in persuasion over the last few decades around how to make the most effective assets most likely to convert.”   According to Frontiera, the success of this program came down to two factors: Project alignment Slalom’s ability to speak not just in terms of the technology, but also the desired business outcomes.   “These are really two sides of the same coin,” Frontiera said, “and [generative AI] was critical in delivering something that the Advancement Office could benefit from and that would fit into their daily workflows.”

Caylent: A strategic approach to generative AI adoption, from vision to value

Generative AI has the potential to redefine productivity, create novel applications, and reinvent customer experience. But without a strategic approach, you could not only miss out on the promise of this powerful tool, but also drain time, energy, and resources away from other mission-critical initiatives across your organization.   To that end, Kristen Backeberg, Director of Global ISV Partner Marketing at AWS, and Val Henderson, President and CRO at Caylent, recently sat down to discuss maybe the most important consideration around adoption: How to tailor your generative AI strategy around clear goals that can drive your organization forward.   Their conversation started, like so many around generative AI, with an overview of especially high-impact use cases. However, both AWS and Caylent have helped dozens of organizations adopt generative AI, and Backeberg and Henderson understand that starting this journey can be daunting.   The solution, according to both Henderson and Backeberg, is knowing which use cases are going to bring the most ROI.   “[The first implementation] has to generate real ROI,” Henderson said. “Based on what we’re seeing, if it doesn’t, generative AI adoption loses steam and attention. It loses momentum. That’s not good for anybody, because we’re seeing such incredible innovation, and the speed of that innovation has never been faster.”   In addition, focusing on customer experience provides a clear north star for AI initiatives. By focusing less on buzzwords and more on clearly defining the system's purpose, organizations can drive effective development and performance.   “People are always going to want to understand the why,” Henderson added. “Why did we do this? Did we do it to check a box, or did we do it because it helps us move our vision, our desire to help our customers, to create a better experience moving forward?”   By building together on top of Amazon Bedrock, Caylent has been able to help 50+ customers across even the most stringently regulated sectors adopt generative AI solutions, proving that this approach is a powerful way for organizations to bring use cases to life.   “This is where AWS feels good about trying to make sure that we're continuing to think differently,” Backeberg said, “think bigger, think outside of the box, and bring together the pieces along the way that create some of the necessary guardrails while we develop this space.”