{"id":40365,"date":"2026-01-27T17:43:56","date_gmt":"2026-01-27T17:43:56","guid":{"rendered":"https:\/\/www.dataiq.global\/?post_type=100-alumni&p=40365"},"modified":"2026-02-04T14:39:05","modified_gmt":"2026-02-04T14:39:05","slug":"julie-de-moyer-cdo-beauty-lvmh","status":"publish","type":"100-alumni","link":"https:\/\/www.dataiq.global\/devstage\/dataiq100\/julie-de-moyer-cdo-beauty-lvmh\/","title":{"rendered":"Julie De Moyer, CDO Beauty, LVMH"},"content":{"rendered":"

Julie De Moyer is Chief Data Officer Beauty at LVMH, where she leads the Data and AI agenda across 15 Beauty Maisons, balancing global scale with the distinct identities of some of the world\u2019s most recognised luxury brands.<\/span>\u00a0<\/span><\/p>\n

Her perspective as a data and AI leader has been shaped at the intersection of commercial pragmatism and technical rigour. With an academic background in economics and early roles in product marketing and strategy, Julie quickly recognised that data and AI are not cost centres, but powerful catalysts for cultural and business transformation.<\/span>\u00a0<\/span><\/p>\n

Formative experiences at organisations including the World Bank, Accenture and Nike reinforced a core lesson\u00a0that\u00a0even the most sophisticated algorithm delivers little value if it is not embedded into\u00a0real human\u00a0workflows. This insight has consistently guided her approach to leadership, prioritising adoption,\u00a0usability\u00a0and decision impact over technical elegance alone.<\/span>\u00a0<\/span><\/p>\n

At LVMH, Julie has navigated the complexity of leading data and AI across a multi-brand, heritage-focused environment. Her role requires balancing centralised governance and standards with the unique\u00a0\u201cpersonalities\u201d and creative autonomy of individual Maisons. This experience has deepened her belief that effective AI leadership is as much about empathy and influence as it is about technology and scale.<\/span>\u00a0<\/span><\/p>\n

Julie\u2019s focus has evolved from simply interpreting what data says to ensuring data empowers people to make better decisions. She brings a leadership style that respects tradition while driving modernisation, shaping a data-driven culture that aligns innovation with the values and\u00a0craftsmanship\u00a0at the heart of the luxury industry.<\/span>\u00a0<\/span><\/p>\n

 <\/p>\n

As a data and AI leader, which traits and skills do you think matter most, and which of those have been most influential for you in your current position?<\/span><\/b>\u00a0<\/span><\/h3>\n

\u201cThe most critical trait for an AI leader today is\u00a0intellectual\u00a0translation: the ability to turn complex technical constraints into strategic business narratives.<\/span>\u00a0<\/span><\/p>\n

\u201cIn our organisation,\u00a0change\u00a0management\u00a0has been the most influential skill. Data is often met with\u00a0scepticism in creative-led industries. By focusing on\u00a0\u2018Augmented Intelligence\u2019\u00a0(helping people do their jobs better) rather than\u00a0\u2018Artificial Intelligence\u2019\u00a0(replacement), I was able to build the trust necessary to move projects past the pilot stage.<\/span>\u00a0<\/span><\/p>\n

\u201cResilience is the second; AI projects often face data quality roadblocks. The ability to\u00a0maintain\u00a0momentum and stay focused on the long-term vision while solving messy, tactical data issues is what separates successful leaders from those who merely experiment.\u201d<\/span>\u00a0<\/span><\/p>\n

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

Reflecting on your career, what is one non-traditional piece of advice (outside of technical skills) you would give to an aspiring data or AI leader aiming for the C-suite?<\/span><\/b>\u00a0<\/span><\/h3>\n

\u201cLearn to speak the language of the P&L, not the Python library.<\/span>\u00a0<\/span><\/p>\n

\u201cTo reach the C-Suite, stop talking about models and start talking about margins. My non-traditional advice is to shadow a non-technical department,\u00a0like Finance or Supply Chain,\u00a0for a month. Understand their stressors, their jargon, and how they are measured.<\/span>\u00a0<\/span><\/p>\n

\u201cAspiring leaders often try to impress the board with the sophistication of their AI.\u00a0In reality, the\u00a0C-Suite cares about three things: risk, revenue, and resiliency. If you can frame your data strategy as a direct lever for one of those three, your seat at the table is guaranteed.\u201d<\/span>\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Julie De Moyer is Chief Data Officer Beauty at LVMH, where she leads the Data and AI agenda across 15 Beauty Maisons, balancing global scale with the distinct identities of some of the world\u2019s most recognised luxury brands.\u00a0 Her perspective as a data and AI leader has been shaped at the intersection of commercial pragmatism […]<\/p>\n","protected":false},"author":19,"featured_media":40804,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","_searchwp_excluded":"","footnotes":""},"100-designation":[1454],"100-search-term":[1456],"class_list":["post-40365","100-alumni","type-100-alumni","status-publish","format-standard","has-post-thumbnail","hentry","100-designation-100-brands-2026-europe","100-search-term-2026-europe-brands"],"acf":{"sort":"","rank":"","forename":"Julie","surname":"De Moyer","fullname":"Julie De Moyer","jobtitle":"CDO Beauty ","organisation":"LVMH ","introduction":"","content_2":"","content_3":"","content_4":"","100_thumbnail_image":40804,"affinoid":""},"publishpress_future_action":{"enabled":false,"date":"2026-05-27 07:30:42","action":"change-status","newStatus":"draft","terms":[],"taxonomy":"100-designation","extraData":[]},"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/100-alumni\/40365","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/100-alumni"}],"about":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/types\/100-alumni"}],"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=40365"}],"version-history":[{"count":3,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/100-alumni\/40365\/revisions"}],"predecessor-version":[{"id":40805,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/100-alumni\/40365\/revisions\/40805"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/media\/40804"}],"wp:attachment":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/media?parent=40365"}],"wp:term":[{"taxonomy":"100-designation","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/100-designation?post=40365"},{"taxonomy":"100-search-term","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/100-search-term?post=40365"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}