Global Business Services (GBS) and shared services organizations are entering a critical phase in their AI transformation journey—one defined less by technological capability and more by trust, governance, and operational readiness.
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, decision-driven operations.
The Mandate Has Shifted: From Automation to Intelligent Orchestration
The evolution of AI in finance is not a linear progression—it is a structural shift in how work gets done, says Glismann-Bringmann, who is prepping for Callisto Grand’s Credit Matters Conference in Budapest this May:
- From rules-based execution (RPA) → to context-aware decision support (Generative AI)
- From task automation → to intelligent orchestration of workflows (Agentic AI)
- From process efficiency → to real-time, outcome-driven decisioning
Traditional RPA environments excel in stable, predictable processes. But as finance leaders know, Order-to-Cash (O2C) is rarely predictable. Exceptions, disputes, and customer variability quickly expose the limits of static automation.
Generative AI started address this gap—adding the ability to interpret unstructured data, summarize cases, and support human decision-making. But the next leap is already underway.
Agentic AI now introduces a fundamentally new capability: the ability to determine and execute the next best action—within defined guardrails—across systems and workflows.
The Reality of Adoption: Progress Is Real, But Uneven
Despite widespread awareness and experimentation, most GBS and shared services 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.
The conversation, however, has clearly evolved from “Should we explore AI?” to “Where can AI deliver value without increasing risk?”. This shift is particularly evident among CFOs, controllers, and GBS leaders. Confidence in AI’s potential is high—but confidence in execution remains selective.
Glismann-Bringmann notes finance leaders are most comfortable with AI when it:
- Enhances visibility
- Supports decision-making
- Reduces manual effort
In finance, where accountability, compliance, and customer trust intersect, “smarter AI” is not enough—Leaders demand safer, auditable outcomes.
Three Conditions That Will Unlock AI at Scale
Across GBS environments, three critical enablers consistently emerge as prerequisites for scaling AI beyond pilots:
Proven Results in Production
Pilot programs and demos are no longer sufficient. Finance leaders require measurable impact in:
- Days Sales Outstanding (DSO)
- Cash predictability
- Workload reduction
- Customer experience
Explainability and Control
Leaders need clear answers to fundamental questions:
- Why was a recommendation made?
- Who is accountable?
- When can decisions be overridden?
Transparency is not optional—it is foundational, Glismann-Bringmann argues.
Governance by Design
Successful organizations embed governance into the architecture itself:
- Defined risk thresholds
- Auditable decision trails
- Cross-functional ownership (Finance, IT, Legal/Compliance)
Without these elements, AI risks amplifying operational weaknesses rather than resolving them.
The Workforce Factor: The Most Overlooked Constraint
While much of the AI conversation focuses on technology, the primary barrier to adoption is operational understanding—not capability.
Most organizations already have access to sufficient AI technology. The challenge lies in:
- Understanding how AI makes recommendations
- Defining decision boundaries
- Building confidence in new roles and responsibilities
This shift fundamentally changes how finance teams operate:
- From execution to supervision and exception handling
- From manual processing to decision oversight and orchestration
Leading organizations must address this through:
- Role-based upskilling (not generic AI training)
- Early involvement of process owners and power users
- Gradual scaling of adoption across teams
Without workforce alignment, even the most advanced AI initiatives will stall.
The Tipping Point: When AI Becomes “Boring”
The ultimate indicator of AI maturity in shared services is not perceived sophistication—it is normalization.
AI reaches its tipping point when:
- It is embedded into daily workflows
- Governed like any other financial control
- Measured purely on outcomes
- No longer perceived as “new” or experimental
At that point, leaders stop asking “Can we use AI here?” and start asking “Why isn’t this process already automated?”
Final Perspective
AI in GBS and shared services is not constrained by technological innovation. It is constrained by trust, governance, and readiness.
For CFOs and GBS leaders, the question is no longer whether AI will transform Order-to-Cash.
It is whether their operating model is prepared to follow through with implementation and trust it.
Frequently Asked Questions (FAQ)
What is the difference between Generative AI and Agentic AI in O2C?
Generative AI provides insights and supports decisions, while Agentic AI takes action—executing workflows and decisions within defined governance frameworks.
What is the biggest barrier to AI adoption in GBS?
According to Glismann-Bringmann, there are gaps in governance, explainability, and operational understanding… not the technology.
Where should finance leaders start with Agentic AI?
High-impact, structured use cases such as collections prioritization, dispute workflows, inbound communication handling, and customer segmentation offer strong early ROI.
