The numbers inside Global Business Services are now pointing in the wrong direction — and the gap is accelerating. GBS workloads forecast to grow 15% this year. Staffing 10%. Budgets 7%. That is not an operational inconvenience. It is a structural liability accumulating on the finance function’s ledger.
For GBS leaders, the message is unambiguous: the cost-plus, headcount-driven service model is approaching structural limits. The organisations that recognise this early, and respond with architectural intent rather than tactical experimentation, will define what high-performance GBS looks like for the next decade. Those that continue to treat AI as a productivity add-on to an unchanged operating model will find themselves managing an increasingly expensive gap they cannot close.
Agentic AI is not a technology trend to monitor. It is the strategic inflection point that determines which GBS organisations scale intelligently and which ones simply get busier.
The End of the Pilot Era
For the past two years, the dominant GBS response to generative AI has been controlled experimentation: proof-of-concepts in accounts payable, chatbot pilots in HR service delivery, summarisation tools layered onto existing workflows. The results have been genuinely promising in isolated pockets — but they have not produced measurable enterprise-level results.
This is not a technology failure. It is an architectural one.
Generative AI, applied as a point solution within an unchanged process, produces incremental efficiency. Agentic AI — AI systems that can reason, plan, sequence tasks, and act across multiple systems with limited human intervention — operates at a fundamentally different level. It does not assist a process. It executes it.
The Hackett Group’s findings reflect this shift in maturity. Nearly 90% of GBS leaders now report that AI is reshaping routine tasks. More than half are seeing measurable impact on complex work. According to the same study, organisations achieving 13% improvements in customer experience, 11% gains in service quality and employee engagement, and 10% productivity increases are not doing so through better prompting. They are doing so through deliberate redesign of how work flows through the organisation.
The pilot era is over. The question now is not whether to deploy agentic AI, but how to architect it for enterprise-grade performance.
What Agentic AI Actually Means in a GBS Context
The term “agentic AI” is being used loosely enough that it is worth being precise about what it means in practice, and why it matters specifically for GBS.
An AI agent is a system that can pursue a defined objective through a sequence of actions — gathering information, making decisions, interacting with external systems, and delegating to other agents — without requiring a human to intervene at each step. In a GBS context, this means an agent can receive an invoice, validate it against contract terms, query an ERP, flag an exception for human review, and initiate payment — end-to-end, at scale, continuously.
This is categorically different from a robotic process automation (RPA) script or a GenAI copilot. RPA follows rigid rules in a deterministic sequence. A GenAI copilot enhances human work. An AI agent handles the work itself, adapting its path based on what it encounters.
IBM’s 2026 enterprise survey suggests that by the end of this year, most large organisations will have deployed digital workforces of more than 1,600 AI agents. The question is no longer whether this scale of deployment is technically feasible. The question is whether the GBS operating model — its governance structures, its data architecture, its performance metrics, its talent model — is designed to manage it.
For most GBS organisations today, the honest answer is: not yet.
The Real Constraint Is Not the Technology
The most important finding from current GBS research is not about AI capability. It is about organisational readiness.
Seven in ten executives surveyed by The Hackett Group across enterprise transformation programmes report that inadequate AI governance is the primary constraint slowing their AI transformation. Not model capability. Not cost. Governance.
This manifests in GBS in three specific ways.
Agent sprawl. When agentic AI deployment happens without a unified strategy, individual teams solve local problems with local tools. The result is a proliferation of siloed, poorly integrated agents — each solving a narrow problem, none connected to enterprise data in a consistent way, all accumulating technical debt and security exposure. The efficiency gains from individual agents are real; the coordination costs across unmanaged agent populations frequently outweigh them.
Data integrity deficits. Agentic AI is only as reliable as the data it acts on. In GBS environments — particularly those that have grown through acquisition, process harmonisation projects, or partial ERP consolidation — master data quality is often inconsistent. An agent instructed to match a vendor invoice against a purchase order in SAP is useless if the vendor master is incomplete, duplicated across systems, or inconsistently structured. Data governance is not a pre-condition that will one day be satisfied; it is a continuous operational discipline that must be managed in parallel with AI deployment.
Performance measurement misalignment. Traditional GBS KPIs — cost per transaction, SLA adherence, headcount ratios — were designed for a labour-intensive operating model. They measure inputs and throughput. They do not measure outcomes. The Hackett Group’s 2026 study explicitly identifies redefinition of performance management as one of the five strategic priorities for GBS leaders this year — expanding metrics to include customer experience, employee engagement, service quality, and innovation contribution alongside cost. Organisations that continue to optimise for old metrics will make rational decisions that produce suboptimal outcomes.
These are not technology problems. They are operating model problems. And they are precisely the problems that a well-structured transformation program addresses before deploying the technology at scale.
What the Architecture Looks Like
The Hackett Group’s Agentic Enterprise Operating Framework offers a useful structure for thinking about how the pieces fit together: strategy, governance, data, technology, and agentic workflows as interdependent layers — not a sequential implementation path, but a system that must be coherent across all dimensions simultaneously.
From a GBS transformation standpoint, three areas of work consistently determine whether deployment succeeds or stalls.
Process clarity before automation. The most common mistake in AI-enabled GBS transformation is automating a broken process. An inefficient accounts-payable workflow with embedded exception handling and manual workarounds does not become efficient when you add an agent to it. It becomes a faster, harder-to-audit, AI-powered version of the same dysfunction. Process clarity comes first. The agent architecture follows.
Governance by design. Agentic AI governance in GBS is not an IT risk management exercise. It is an operational discipline. It defines which decisions agents are authorised to take autonomously, which require human review, how errors are detected and resolved, and how the performance of the agent population is measured and improved over time. Human-in-the-loop design is not a concession to caution; it is the mechanism by which trust is built and operational risk is managed.
Performance architecture redesign. Building an outcome-oriented performance management framework is not a metrics exercise. It requires alignment between GBS leadership, enterprise stakeholders, and — where relevant — retained organisation partners. Defining what “good” looks like when the agent handles the transaction and the human handles the exception is a fundamentally different question than SLA compliance in a headcount-driven model.
The Strategic Window Is Now
The current moment in agentic AI adoption is genuinely unusual. The technology is mature enough to deliver measurable enterprise value. The competitive differentiation between early movers and late adopters is widening visibly. And the organisational capability to deploy it well — governance frameworks, operating model design, change management — remains scarce enough that doing it properly is itself a source of competitive advantage.
For GBS leaders, the organisations that begin this architecture work now will have a 12-to-18-month operational advantage over those that wait for the market to mature further. Large-scale agentic transformation programs are already under contract with major enterprises. The organisations that act with architectural intent today will not be catching up to those programs — they will be defining their own path at a moment when the capability to do so well is still rare.
The productivity gap identified in the Hackett Group’s study will not close on its own. It will not close through incremental automation. It will close through deliberate, governance-aware, operating model-level transformation — anchored in a clear understanding of what agentic AI can and cannot do, and designed to perform at enterprise scale.
The Talvoren View
At Talvoren Advisory, our focus is on the intersection of enterprise architecture, GBS transformation, and AI-enabled operating model design. We work with mid-to-large organisations in the DACH region navigating precisely this shift — from pilot-stage experimentation to scalable, governed, outcome-oriented deployment.
When we meet with finance and GBS leaders working through this transition, we focus on three questions first: Where does your data and governance readiness actually stand? Which processes are worth automating and which need redesigning first? And what does “performance” mean when the agent handles the transaction and the human handles the exception?
These are the questions that determine whether an agentic transformation creates enterprise value or simply creates complexity at scale. They are also the questions that most transformation programs address too late.
If you are preparing for this conversation, we are ready to have it.
Talvoren Advisory is a boutique transformation consultancy based in Berlin, specialising in GBS transformation, enterprise operating model design, and AI-enabled operations for mid-to-large organisations across the DACH region and internationally.
