Challenges HR Leaders Face in Adopting Enterprise AI - with Raúl Monroig of Bristol Myers Squibb

Enterprise HR leaders are wrestling with data that is both fragmented and self‑reported, making it difficult to trust the inputs that drive talent decisions. Raul Monroig explains that information lives in Workday, dashboards, Eightfold and other siloed tools, preventing a single, reliable view of employee skills and performance. This lack of clean, consolidated data hampers the ability to align workforce capabilities with strategic goals and slows AI‑enabled analytics. Without addressing data quality first, any AI initiative risks delivering insights that are incomplete or misleading.

Another major headwind is the temptation to develop dozens of competencies simultaneously. Monroig argues that HR should narrow its focus to a handful of high‑impact skills—curiosity, mental agility, and a customer‑service mindset—to drive measurable business outcomes. He also stresses hiring great AI users rather than pure technologists; employees who can extract valuable outputs from tools like ChatGPT deliver immediate value. By concentrating on these core capabilities and empowering users, organizations avoid costly, unfocused training programs and create a disciplined pathway for AI adoption across the people function.

Practical AI applications are already reshaping HR work. Monroig cites using Copilot or ChatGPT to condense thousands of survey responses in minutes, while deeper questions—such as linking salary changes to sales performance—still require richer data sets. He points to Procter & Gamble’s three‑year AI‑coach experiment, which boosted engagement and revenue, illustrating the power of a scientific, test‑and‑learn approach. For HR teams seeking ROI, the lesson is clear: clean data, focused skill development, and user‑centric AI tools together unlock the next wave of workforce transformation.