{"id":36378,"date":"2025-05-20T11:04:27","date_gmt":"2025-05-20T10:04:27","guid":{"rendered":"https:\/\/www.dataiq.global\/?post_type=article&p=36378"},"modified":"2025-05-20T11:04:27","modified_gmt":"2025-05-20T10:04:27","slug":"day-2-highlights-summit-north-america-2025","status":"publish","type":"article","link":"https:\/\/www.dataiq.global\/devstage\/articles\/day-2-highlights-summit-north-america-2025\/","title":{"rendered":"Day 2 Highlights: Summit North America 2025"},"content":{"rendered":"

Research and Panel Exchange: Navigating the Future<\/span><\/b>\u00a0<\/span><\/h3>\n

Randy Bean, Knowledge Expert and Business Advisor, North America, DataIQ,\u00a0previewed his Data and AI Executive Research Report, spotlighting how data leaders are adapting to evolving expectations, both internal and external.<\/span>\u00a0<\/span><\/p>\n

The startling and impressive reality is that 98.4% of organizations report investing in data and AI, and 94% say that AI interest has increased their organization\u2019s focus on data. This is a significant win for AI and data leaders and emphasizes that organizations have embedded foundations for data-driven AI success. <\/span>\u00a0<\/span><\/p>\n

Key takeaways:<\/span>\u00a0<\/span><\/h2>\n
    \n
  • 98.4% of organizations are investing in data and AI.<\/span>\u00a0<\/span><\/li>\n
  • 94% say AI has increased focus on data.<\/span>\u00a0<\/span><\/li>\n
  • CDO presence has grown from 12% in 2012 to 84.3% in 2024.<\/span>\u00a0<\/span><\/li>\n
  • 77% report early AI implementation; ~25% have scaled AI.<\/span>\u00a0<\/span><\/li>\n
  • ~80% of CDOs have tenure under three years; yet 70% believe the role will become permanent.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n

    Randy explained that the need for data and AI leadership will only grow exponentially, even if the specific title or structure of the role evolves. Nine in ten CDO\u2019s agree AI will be the most transformative technology of this generation, and 97% believe the impacts of AI will be beneficial.<\/span>\u00a0<\/span><\/p>\n

    \u00a0<\/span><\/p>\n

    Delivering business value with data and AI\u00a0<\/strong><\/h3>\n

    DataIQ\u2019s Randy Bean hosted a high-impact panel session featuring\u00a0Margery Connor, Chief Data and Analytics Officer, Chevron; Diana Schildhouse, Chief Data, Analytics, and Insights Officer ,Colgate Palmolive; Krishna Cheriath, Chief Digital Officer, Thermo Fisher; and Ido Biger, Executive Vice President, Chief Technology and Digital Officer,\u00a0Delek, focusing on high-impact use cases demonstrating the delivery of business value and covering:<\/span>\u00a0<\/span><\/p>\n

      \n
    • The convergence of data and digital leadership.<\/span>\u00a0<\/span><\/li>\n
    • Embedding AI into core business processes.<\/span>\u00a0<\/span><\/li>\n
    • Building internal credibility through commercial results.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n

      Each panelist shared their priorities, focusing on maximizing AI value, digital transformations, use of AI agents to improve administration, revenue and efficiency, and utilizing AI for safety and operational workflows.\u00a0<\/span>\u00a0<\/span><\/p>\n

      Standout use cases<\/span>\u00a0<\/span><\/h2>\n
        \n
      • Colgate-Palmolive: RGM analytics spanning more than 200 countries with measurable ROI.<\/span>\u00a0<\/span><\/li>\n
      • Thermo Fisher: AI-driven improvements across trial design, patient engagement, and regulatory prep, reducing time by 30%.<\/span>\u00a0<\/span><\/li>\n
      • Delek: Drone-based AI safety inspections reducing manual labor, improving accuracy, and integrating results into SAP systems.<\/span>\u00a0<\/span><\/li>\n
      • Chevron: AI for identifying underground hydrocarbon deposits and optimizing well placement, including real-time operational decisions to avoid drill interference.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n

        All panelists agreed: culture change is central to unlocking business value. There was an emphasis on business empathy and emotional intelligence stating that success depends on deep business understanding, not just technical sophistication.<\/span>\u00a0<\/span><\/p>\n

          \n
        • Data and AI must remain deeply interconnected and it cannot be siloed.<\/span>\u00a0<\/span><\/li>\n
        • There is a strong likelihood of shared ownership between central and functional teams. Success lies in enabling business, not central control.<\/span>\u00a0<\/span><\/li>\n
        • The CDO role will evolve into a broader transformation leadership role, driving reimagined business processes and workforce strategies.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n

          \u00a0<\/span><\/p>\n

          Data and AI in the Mainstream<\/span><\/b>\u00a0<\/span><\/h3>\n

          The final panel discussion of the event brought together Andr\u00e9 Vargas, Chief Data Officer, Creative Arts Agency; Bharathi Rajan, VP \u2013 Data and Insights, Swire Coca-Cola, USA; Tom Henry, Chief Data and Deputy Chief Information Officer, Schnucks; Luke Gee <\/span>Chief Analytics and\u00a0AI Officer, TD.<\/span>\u00a0<\/span><\/p>\n

          Data and AI are increasingly central to business strategies, but uptake varies by industry, and mainstreaming AI means reframing its value in business-friendly language.<\/span>\u00a0<\/span><\/p>\n

          Key Takeaways:<\/span>\u00a0<\/span><\/h2>\n
            \n
          • Rebrand AI as \u201caugmented intuition\u201d to build internal adoption. Position data as a tool to close asymmetries between artists and big tech.<\/span>\u00a0<\/span><\/li>\n
          • Challenges with data adoption in manufacturing versus marketing.<\/span>\u00a0<\/span><\/li>\n
          • Transitioning from gut feel decisions to data-led decision-making, using in-store robots and AI for shelf inventory and customer experience.<\/span>\u00a0<\/span><\/li>\n
          • Create an AI Center of Excellence focused on embedding AI across lines of business for unified, personalized customer experiences.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n

            As data and AI become more central to day-to-day operations there are steps required to ensure long-term success.\u00a0<\/span>\u00a0<\/span><\/p>\n

              \n
            1. Co-Design and Embed Teams<\/span><\/b>:<\/span>\u00a0<\/span>\n
                \n
              1. Co-design with agents to ensure solutions are intuitive and valuable.<\/span>\u00a0<\/span><\/li>\n<\/ol>\n<\/li>\n
              2. Human Connection and Trust<\/span><\/b>:<\/span>\u00a0<\/span>\n
                  \n
                1. Get data team members onto the front line to build empathy and credibility.<\/span>\u00a0<\/span><\/li>\n
                2. Mandate business immersion for data teams.<\/span>\u00a0<\/span><\/li>\n<\/ol>\n<\/li>\n
                3. Show Your Receipts<\/span><\/b>:<\/span>\u00a0<\/span>\n
                    \n
                  1. Review model performance and ROI weekly with execs to build trust.<\/span>\u00a0<\/span><\/li>\n
                  2. Shift from calculating benefits as proof to being trusted enough to skip it, then circled back to streamline measurement.<\/span>\u00a0<\/span><\/li>\n<\/ol>\n<\/li>\n<\/ol>\n

                    Investment in AI and data is required to achieve these goals. Data leaders must explain to business leaders that the purchase of an AI solution is not the end of the journey and that continued training, upskilling, and cultural evolution is essential.<\/span>\u00a0<\/span><\/p>\n

                    \u00a0<\/span><\/p>\n

                    Looking Ahead<\/span><\/b>\u00a0<\/span><\/h2>\n

                    The US Summit made one thing clear: data and AI are mainstream, but continued success requires intentional leadership, stakeholder engagement, and structural investment.<\/span>\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

                    Day 2 of the US DataIQ Summit continued the opening day energy and discussions, shifting from foundational strategy and experimentation surrounding AI to the structural, cultural, and commercial transformations needed to embed data and AI into the enterprise.<\/p>\n","protected":false},"author":19,"featured_media":36380,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","_searchwp_excluded":"","footnotes":""},"categories":[129,398,127],"tags":[1331,325,87,1097,1224,368,229],"pillar":[193,194],"class_list":["post-36378","article","type-article","status-publish","format-standard","has-post-thumbnail","hentry","category-editorial","category-public","category-news","tag-ai-leader","tag-business-value","tag-leadership","tag-nashville","tag-north-america","tag-roi","tag-value","pillar-strategy","pillar-leadership"],"acf":[],"publishpress_future_action":{"enabled":false,"date":"2026-06-16 22:15:36","action":"change-status","newStatus":"draft","terms":[],"taxonomy":"category","extraData":[]},"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/article\/36378","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/article"}],"about":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/types\/article"}],"author":[{"embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/users\/19"}],"replies":[{"embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/comments?post=36378"}],"version-history":[{"count":3,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/article\/36378\/revisions"}],"predecessor-version":[{"id":36389,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/article\/36378\/revisions\/36389"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/media\/36380"}],"wp:attachment":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/media?parent=36378"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/categories?post=36378"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/tags?post=36378"},{"taxonomy":"pillar","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/pillar?post=36378"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}