VentureBeat Transform Day 1: AI Adoption Requires Care and Speed
VentureBeat Transform, held in San Francisco on July 11-12, brought together top executives to discuss the rapid advancements and concerns surrounding generative artificial intelligence (AI). The event emphasized the need for a diverse range of AI models and highlighted the potential for businesses to leverage AI to enhance efficiency and customer experience.
ChatGPT, among other generative AI models, has paved the way for widespread adoption of this technology across various domains. At the Women in AI breakfast panel, Mastercard’s chief data officer, JoAnn Stonier, compared the pace of AI development to the Oscar-winning movie Everything Everywhere All at Once, emphasizing the need to ensure equal opportunities for all to benefit from this technology.
In a fireside chat, Uljan Sharka, CEO of iGenius, stressed the importance of a human-centric approach to developing generative AI products. Sharka highlighted the need to prioritize human needs over technological advancements, as previous technology advancements have not achieved the desired level of adoption due to a significant focus on technology rather than human requirements.
Ashok Srivastava, Chief Data Officer at Intuit, emphasized the significance of high-quality training data for generative AI models. He outlined two key aspects of a strong data foundation: having clean data at scale and having real-time data at scale. Intuit, a long-time user of machine learning, employs generative AI through its operating system, GenOS, to deliver personalized experiences to customers. Despite the role of large language models in GenOS, Srivastava noted that traditional machine learning models such as classifiers and recommendation systems remain crucial.
Speakers Mark Tack, CMO at TreasureData, and Gail Muldoon, data scientist at Stellantis, shared insights on leveraging generative AI to enhance personalization and customer insights. Tack highlighted the importance of having clear objectives, strategies, and metrics before incorporating AI into business operations, cautioning against falling into the shiny object syndrome trap.
Stellantis, the world’s third-largest automobile company, is utilizing generative AI to transition to a full online shopping experience. Muldoon discussed how Treasure Data’s Customer Data Cloud platform enabled Stellantis to anticipate customer shopping interests and preferences.
Enterprises are still exploring the best ways to leverage generative AI, but efficient access to information is among the low-hanging fruits. Sarah Hoffman, VP of AI and ML at Fidelity, highlighted how generative AI facilitates collaboration and workflow definition, offering interfaces using text boxes instead of cluttered webpages.
Steve Wood, SVP of product and platform at Slack, discussed the role of large language models in making automation accessible to all employees. Wood emphasized the need to empower individuals throughout organizations to build and automate tasks, using the integration of knowledge held in large language models with data from Slack conversations to unlock bespoke business intelligence.
By prioritizing care and speed in AI adoption, businesses can harness the true potential of generative AI while ensuring equal access and delivering meaningful experiences to customers.
