AI Enablement Engineer

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Summary:   This is best understood as an   enterprise AI measurement, insights, and enablement role —not a model-building role. The core mandate is to   empower   Cencora’s   employees to use AI responsibly, profitably, and well,   and    measure   the value that AI adds to   Cencora. While   this role include   various tasks related to   AI enablement,   such as community-building, supporting   responsible use,   and participating in technology rollout efforts,   a particular focus is to   measure how AI adoption is impacting Cencora and   translate results into decision-ready insight for leaders. The role blends   measurement strategy, cross-functional advisory work, and hands-on analytics execution . Success depends on linking AI adoption and usage data to   real business   outcomes such as productivity, quality, cost reduction, risk reduction,   proficiency , and change adoption rather than   stopping at   activity metrics alone.

The role’s central responsibility is to   assis t   with AI enablement programs including the   maturation of   enterprise AI measurement framework   covering:   adoption   engagement   proficiency   responsible use   value realization   ROI across multiple AI tools and platforms     AI measurement framework design  The role expects someone   who can   help   define structured, reusable ways to measure AI adoption and value across the enterprise, including standard metric definitions and value hypotheses.     Hands-on analytics and dashboarding  This is not just a consulting role. It   - requires   direct work with data, dashboard tools, KPI logic, reports, and visual storytelling.     Executive communication and data storytelling  A major requirement is turning complex metrics into clear narratives for senior leaders, while also being able to explain methods to practitioners and technical teams.     Cross-functional influence  The person will   likely need   to drive alignment across business units, IT, governance, privacy, HR, and engineering without formal authority.     Business-value and ROI thinking  A candidate must be able to distinguish between usage metrics and business outcomes, and explain when value is direct, estimated, or better represented through proxies     Change/adoption measurement  Because the role sits within AI readiness and enablement,   it   values experience measuring   proficiency,    behavior   change, and digital transformation outcomes     Governance- and privacy-aware judgment  Preferred experience in regulated or risk-aware environments suggests strong relevance for privacy-safe reporting, responsible   use   metrics, and collaboration with governance stakeholders.      Tools   and methods: The role   is likely to use   a practical analytics stack rather than advanced model development tools. Based on the responsibilities, likely tools/methods include:   BI/dashboard tools   such as Power BI or   Databricks AI/BI   Data warehousing tools including Databricks   SQL and spreadsheet-based analysis  Possibly   Python or R   for deeper analysis   Cross-platform telemetry / usage logs  Reporting feeds from AI tools, enablement systems, and business outcome systems   Qualifications :   Bachelor’s degree in computer science, data science, statistics, mathematics, engineering, information systems, or a related field, or equivalent experience   required .   Less than 2 years of experience in artificial intelligence, machine learning, data science, analytics, software