{"id":42416,"date":"2026-04-09T18:44:49","date_gmt":"2026-04-09T17:44:49","guid":{"rendered":"https:\/\/www.dataiq.global\/?post_type=100-alumni&p=42416"},"modified":"2026-04-30T12:21:52","modified_gmt":"2026-04-30T11:21:52","slug":"jane-rheem-chief-data-ai-officer-sidley-austin-llp","status":"publish","type":"100-alumni","link":"https:\/\/www.dataiq.global\/devstage\/dataiq100\/jane-rheem-chief-data-ai-officer-sidley-austin-llp\/","title":{"rendered":"Jane Rheem, Chief Data & AI Officer, Sidley Austin LLP"},"content":{"rendered":"

Jane is becoming a mainstay in the DataIQ 100 North America Top Ten as she secures a third consecutive place in the upper echelon of data and AI leaders.<\/span>\u00a0<\/span>Jane is\u00a0<\/span>renown<\/span>\u00a0as<\/span>\u00a0<\/span>data<\/span>\u00a0and AI leader who consistently turns vision into enterprise value. As Chief Data and AI Officer, Jane\u00a0<\/span>has redeveloped and continuously shaped the organization\u2019s<\/span>\u00a0AI strategy and\u00a0<\/span>embedded<\/span>\u00a0it within decision-making processes across the business<\/span>\u00a0to\u00a0<\/span>great effect<\/span>, and h<\/span>er\u00a0<\/span>track record<\/span>\u00a0<\/span>shows a consistent focus on building capabilities that endure. Jane’s work around coupling modern data platforms with governance, operating models, and cross-functional alignment, ensuring AI is both scalable and trusted is testament to her\u00a0<\/span>abilities, and h<\/span>er approach reflects a broader shift in the role, from experimentation to accountable delivery of measurable value.<\/span><\/span>\u00a0<\/span><\/em><\/p>\n

 <\/p>\n

Jane Rheem is Chief Data and AI Officer at Sidley Austin LLP, where she leads the firm\u2019s enterprise data, analytics, and AI agenda. Her career has been shaped by building and scaling modern data capabilities in highly regulated, complex environments, with a consistent focus on turning data into\u00a0measurable\u00a0business impact.<\/span>\u00a0<\/span><\/p>\n

She began her career in consulting and financial services, developing\u00a0a strong foundation\u00a0in business analysis, process optimization, and financial discipline. That early experience continues to influence her leadership style: pragmatic, outcome-driven, and closely tied to value creation.<\/span>\u00a0<\/span><\/p>\n

Over time, Jane moved into enterprise leadership roles, building analytics and data science organizations from the ground up and delivering predictive solutions across pricing, underwriting, claims, and marketing. She has also led large-scale data modernization programs, reinforcing her belief that analytics and AI only deliver sustained value when embedded directly into core business workflows rather than treated as standalone initiatives.<\/span>\u00a0<\/span><\/p>\n

In her current role, Jane focuses on enterprise-wide transformation,\u00a0defining multi-year data and AI strategies, building high-performing cross-functional teams,\u00a0establishing\u00a0strong governance, and scaling responsible AI across both business operations and the practice of law. Across every stage of her career, she has\u00a0remained\u00a0focused on a simple principle: technology succeeds only when paired with trust, clear operating models, and relentless execution against real-world outcomes.<\/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

\u201cEffective data and AI leadership requires a balanced combination of strategic, technical, and human-centered skills.\u00a0First and foremost\u00a0is business acumen;\u00a0the ability to translate data and AI capabilities into clear value tied to outcomes such as revenue, productivity, risk reduction, or client experience. Without this, even strong technology investments\u00a0fail to\u00a0gain traction.<\/span>\u00a0<\/span><\/p>\n

\u201cEqually\u00a0critical is systems\u00a0thinking:\u00a0understanding how data, technology, processes, governance, and incentives interact across the enterprise. This enables leaders to design solutions that scale responsibly rather than creating isolated point wins. Technical fluency also matters,\u00a0not to code, but to make sound architectural, model, and risk decisions, especially as AI grows more complex and regulated.<\/span>\u00a0<\/span><\/p>\n

\u201cThe most influential traits in my organization have been\u00a0change\u00a0leadership and trust-building. Data and AI transformation is\u00a0as much\u00a0cultural as technical. Leading with clarity, transparency, and pragmatism has helped align diverse stakeholders, demystify and reduce fear around AI, and accelerate adoption. Finally, creating a \u201cnorth start\u201d with a vision and strategy that provides\u00a0line\u00a0of sight to the multi-year\u00a0three\u00a0horizons\u00a0landscape of short-term,\u00a0mid-term, and\u00a0long-term\u00a0enablement.\u00a0\u00a0This is critical to rally an organization around our mission, which is to be an\u00a0AI-enabled\u00a0organization.\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\u00a0operate\u00a0as a business leader first and a data or AI leader second. Early in my career, the most consequential growth came not from deeper technical\u00a0expertise, but from understanding how decisions are really made:\u00a0how incentives, power dynamics, risk tolerance, and timing shape outcomes. The C-suite values judgment, clarity, and trust far\u00a0more than elegance of solution.<\/span>\u00a0<\/span><\/p>\n

\u201cThis means developing the ability to say\u00a0\u2018no\u2019\u00a0as confidently as\u00a0\u2018yes\u2019,\u00a0framing trade-offs in plain language, and taking accountability for outcomes, not activity. It means investing in relationships long before you need them and building credibility by consistently delivering what matters most to the business. When leaders see you as someone who can simplify complexity, make hard calls, and protect the enterprise while moving it forward, the technical credentials become table\u00a0stakes,\u00a0and your influence expands well beyond your function.\u201d<\/span>\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Jane is becoming a mainstay in the DataIQ 100 North America Top Ten as she secures a third consecutive place in the upper echelon of data and AI leaders.\u00a0Jane is\u00a0renown\u00a0as\u00a0data\u00a0and AI leader who consistently turns vision into enterprise value. As Chief Data and AI Officer, Jane\u00a0has redeveloped and continuously shaped the organization\u2019s\u00a0AI strategy and\u00a0embedded\u00a0it within […]<\/p>\n","protected":false},"author":19,"featured_media":35581,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","_searchwp_excluded":"","footnotes":""},"100-designation":[1486],"100-search-term":[1489],"class_list":["post-42416","100-alumni","type-100-alumni","status-publish","format-standard","has-post-thumbnail","hentry","100-designation-100-brands-2026-americas","100-search-term-2026-north-america-brands"],"acf":{"sort":"","rank":5,"forename":"Jane","surname":"Rheem","fullname":"Jane Rheem","jobtitle":"Chief Data & AI Officer","organisation":"Sidley Austin LLP ","introduction":"","content_2":"","content_3":"","content_4":"","100_thumbnail_image":35581,"affinoid":""},"publishpress_future_action":{"enabled":false,"date":"2026-05-20 21:37:03","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\/42416","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=42416"}],"version-history":[{"count":3,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/100-alumni\/42416\/revisions"}],"predecessor-version":[{"id":43657,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/100-alumni\/42416\/revisions\/43657"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/media\/35581"}],"wp:attachment":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/media?parent=42416"}],"wp:term":[{"taxonomy":"100-designation","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/100-designation?post=42416"},{"taxonomy":"100-search-term","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/100-search-term?post=42416"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}