Global Business Services (GBS) and shared services organizations are entering a critical phase in their AI transformation journey\u2014one defined less by technological capability and more by trust, governance, and operational readiness.<\/p>\n
While adoption of AI has accelerated rapidly in recent years, particularly with the rise of generative AI, most organizations remain in transition. As highlighted in a recent episode of the Emagia AI for Finance Podcast featuring guest Stephan Glismann-Bringmann, Credit Control Ambassador at Callisto Grand, many GBS environments are still trying to evolve from rules-based automation toward intelligent<\/a>, decision-driven operations.<\/p>\n The evolution of AI in finance is not a linear progression\u2014it is a structural shift in how work gets done, says Glismann-Bringmann, who is prepping for Callisto Grand\u2019s Credit Matters Conference in Budapest this May:<\/strong><\/p>\n Traditional RPA environments excel in stable, predictable processes. But as finance leaders know<\/a>, Order-to-Cash (O2C) is rarely predictable. Exceptions, disputes, and customer variability quickly expose the limits of static automation.<\/p>\n Generative AI started address this gap\u2014adding the ability to interpret unstructured data, summarize cases, and support human decision-making. But the next leap is already underway.<\/p>\n Agentic AI now introduces a fundamentally new capability: the ability to determine and execute the next best action\u2014within defined guardrails\u2014across systems and workflows.<\/p>\n Despite widespread awareness and experimentation, most GBS and shared services<\/a> organizations remain in early to mid-stage AI adoption, looking for a breakout or breakthrough in 2026, as noted in the AI For Finance Podcast Episode.<\/p>\n The conversation, however, has clearly evolved from \u201cShould we explore AI?\u201d<\/i> to \u201cWhere can AI deliver value without increasing risk?\u201d.<\/i> This shift is particularly evident among CFOs, controllers, and GBS leaders. Confidence in AI\u2019s potential is high\u2014but confidence in execution remains selective.<\/p>\n Glismann-Bringmann notes finance leaders are most comfortable with AI when it:<\/strong><\/p>\n In finance, where accountability, compliance, and customer trust intersect, \u201csmarter AI\u201d is not enough\u2014Leaders demand safer, auditable outcomes.<\/p>\n Across GBS environments, three critical enablers consistently emerge as prerequisites for scaling AI beyond pilots:<\/p>\n Pilot programs and demos are no longer sufficient. Finance leaders require measurable impact in:<\/strong><\/p>\n Leaders need clear answers to fundamental questions:<\/strong><\/p>\n Transparency is not optional\u2014it is foundational, Glismann-Bringmann argues.<\/p>\n Successful organizations embed governance into the architecture itself:<\/strong><\/p>\n Without these elements, AI risks amplifying operational weaknesses rather than resolving them.<\/p>\n While much of the AI conversation focuses on technology, the primary barrier to adoption is operational understanding\u2014not capability.<\/p>\n Most organizations already have access to sufficient AI technology. The challenge lies in:<\/strong><\/p>\n This shift fundamentally changes how finance teams operate:<\/strong><\/p>\n Leading organizations must address this through:<\/strong><\/p>\n Without workforce alignment, even the most advanced AI initiatives will stall.<\/p>\n The ultimate indicator of AI maturity in shared services is not perceived sophistication\u2014it is normalization<\/a>.<\/p>\n AI reaches its tipping point when:<\/strong><\/p>\n At that point, leaders stop asking \u201cCan we use AI here?\u201d<\/i> and start asking \u201cWhy isn\u2019t this process<\/a> already automated?\u201d<\/i><\/p>\n AI in GBS and shared services<\/a> is not constrained by technological innovation. It is constrained by trust, governance, and readiness.<\/p>\nThe Mandate Has Shifted: From Automation to Intelligent Orchestration<\/b><\/h2>\n
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The Reality of Adoption: Progress Is Real, But Uneven<\/b><\/h2>\n
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Three Conditions That Will Unlock AI at Scale<\/b><\/h2>\n
Proven Results in Production<\/b><\/h3>\n
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Explainability and Control<\/b><\/h3>\n
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Governance by Design<\/b><\/h3>\n
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The Workforce Factor: The Most Overlooked Constraint<\/b><\/h2>\n
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The Tipping Point: When AI Becomes \u201cBoring\u201d<\/b><\/h2>\n
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Final Perspective<\/b><\/h2>\n