"AI is poised to transform the way we work. In fact, I would argue it's already transformed the way many of us in this room work." This opening statement from Tim Stenovec, Anchor for Bloomberg Businessweek, set the stage for a critical discussion on the tangible impact of artificial intelligence at the "The Business Value of AI" event in Atlanta, presented by IBM. The focus quickly shifted from theoretical promise to practical application and measurable returns.
Greg Pollack, Data Platform Leader for IBM Southeast, further elaborated on the core challenge facing enterprises today: "What we're all trying to figure out... is how to get a return on that investment through meaningful, enterprise-wide efficiencies." The conversation underscored that AI's true value lies not in hype, but in its ability to solve real business problems, drive productivity, and enhance customer and employee experiences. This necessitates a shift from experimental pilots to scalable, production-ready AI solutions.
A central tenet emerging from the event, particularly during the panel discussion on "Creating the Data Foundation for an Optimal Customer Experience," was the indispensable role of data. "Data is your competitive advantage. Not just all data, but your enterprise data. The data that makes your business work for you and for your customers," Pollack asserted. This sentiment was echoed by Mallory Freeman, President of Global Enterprise Data Analytics & GenAI at UPS, who highlighted the sheer scale of their data assets: "We have 20 petabytes of data, ready and curated for AI, structured and unstructured. Our total data estate is multiple times more than that." This vast, curated data serves as the bedrock for UPS's AI initiatives, powering solutions from optimizing global supply chains to enhancing customer interactions.
The journey from raw data to actionable AI-driven insights requires robust infrastructure and a clear strategy. Manav Misra, Executive Vice President & Chief Data & Analytics Officer at Regions Bank, discussed how the evolution of cloud computing and technologies like Hadoop democratized access to powerful computing and storage capabilities. This laid the groundwork for managing both structured and unstructured data at scale. The subsequent emergence of "lakehouse" architectures further combined the flexibility of data lakes with the dependability of traditional data warehouses, providing a unified platform for diverse data types.
This evolution in data infrastructure, coupled with advancements in AI models like Large Language Models (LLMs), has made it possible to extract deeper meaning from data. The ability to process entire sentences, understand context, and move beyond simple keyword searches has revolutionized how organizations can leverage their information. This increased accessibility and capability, however, also brings challenges related to affordability, governance, and ensuring the ethical deployment of AI. The ultimate goal remains to deploy practical AI that not only drives efficiencies and revenue but also maintains customer trust and operational integrity, enabling businesses to be smarter, faster, and more resilient in a dynamic market.
