Is agentic AI ready to reshape Global Business Services?
Source: VentureBeat
Presented by EdgeVerve
Can Agentic AI Transform Enterprises – and GBS in Particular?
Agentic AI—AI that can take goal‑driven actions—promises to reshape not only Global Business Services (GBS) but any type of enterprise. The question is whether it has already begun to do so.
Reality check:
As with many emerging technologies, the hype has outpaced actual deployment.
- 2025 was billed as “the year of agentic AI,” but that narrative didn’t materialize, according to VentureBeat contributing editor Taryn Plumb.
- Drawing on insights from Google Cloud and IDE‑provider Replit, Plumb noted in a December 2025 VentureBeat article that the fundamentals needed to scale agentic AI are still missing.
- Read the full story: Even Google and Replit struggle to deploy AI agents reliably – here’s why
Why the Slow Adoption?
The lag mirrors the early days of large‑language‑model (LLM)‑based generative AI. A recent survey highlights the current state:
- Survey: February 2025 Shared Services & Outsourcing Network (SSON) summit
- Result: 65 % of GBS organizations reported not yet completing a GenAI project.
- Implication: Adoption of the newer agentic AI is still very nascent across enterprises, including GBS.
Takeaway: While agentic AI holds transformative potential, enterprises—and GBS units in particular—are still in the early stages of moving from rhetoric to real‑world implementation.
The Role of Agentic AI in Global Business Services
There are good reasons to focus on the tremendous potential of agentic AI and its application to the Global Business Services (GBS) sector.
Why Agentic AI Matters
- Orchestration Power – Agentic AI unlocks capabilities in the orchestration layer of software workflows that were previously impractical.
- Technique‑agnostic – It can employ a range of techniques, including (but not limited to) large language models (LLMs).
- Achievable Foundations – While enterprises may lack some fundamentals needed for large‑scale deployment, those prerequisites are within reach.
GBS & GCCs: A Natural Fit
- Evolving Role – GBS and Global Capability Centers (GCCs) are transitioning from back‑office extensions to strategic enterprise partners.
- Alignment with Core Functions – Standard agentic‑AI use cases—such as IT operations automation and customer‑service agents—already sit within the existing GBS/GCC wheelhouse.
Path Forward for Industry Leaders
- Assess Readiness – Identify gaps in data, talent, and infrastructure.
- Pilot Strategically – Start with low‑risk, high‑impact use cases (e.g., ticket triage, routine workflow automation).
- Iterate & Scale – Refine pilots, build reusable components, and expand across functions.
- Govern & Monitor – Establish clear governance, performance metrics, and continuous monitoring.
Bottom line: Agentic AI has the potential to transform the GBS sector. By taking a methodical, step‑by‑step approach, industry leaders can move toward scaled, sustainable deployment.
Five Steps for Deploying Agentic AI in GBS
Agentic AI isn’t the only AI paradigm in play. We also have:
| AI Type | Primary Use | LLM‑dependence |
|---|---|---|
| GenAI | Content creation | Often uses LLMs |
| Predictive AI | Forecasting | No LLMs required |
| Document AI | Data extraction | No LLMs required |
| Agentic AI | Autonomous decision‑making & execution | Can draw on the other three |
These flavors are mutually supportive and stacked rather than siloed in modern systems. Having weathered the hype cycle of GenAI, industry leaders can now adopt a more measured—and productive—approach to agentic AI.
Below is a concise, five‑step framework that moves an organization from preparation to enterprise‑wide scale‑up.
1. Know Thy Processes
- Map all existing processes and workflows.
- Identify variations across regions, business units, and GBS centers.
- Example: A global shipping & logistics firm runs > 80 complex, manually intensive processes across seven GBS centers. Understanding these is the prerequisite for any redesign.
2. Know Thy Data
- Trace data end‑to‑end: sources, pipelines, APIs, and storage.
- Distinguish between structured, semi‑structured, and unstructured data.
- Catalog critical assets such as systems of record and vector databases (the “context engines” agents rely on).
- Review data‑governance, security, and compliance policies—and anticipate how they may evolve under an agentic AI model.
3. Identify the Problem (Define the Use‑Case)
- Convert pain points into concrete, measurable objectives.
- Example (shipping firm):
- High manual effort → Cost reduction
- SLA breaches → Service‑level improvement
- Poor CX → Customer‑experience uplift
- Compliance risk → Risk‑mitigation
A well‑scoped problem becomes a clear use‑case for an agentic AI solution.
4. Pilot an Operating Model
Choose an operating model that fits your organization’s culture and governance needs:
| Model | Description |
|---|---|
| Center of Excellence (CoE) | Centralized expertise, reusable assets, and governance. |
| Citizen‑Led Development | Democratized, low‑code/no‑code tools for business users. |
| Build‑Operate‑Transfer (BOT) | Partner‑led implementation that is later handed over to internal teams. |
Key pilot considerations
- Structural clarity – define ownership, roles, and escalation paths.
- Multi‑agent coordination – design for parallel agents pursuing shared goals.
- Risk & governance – embed monitoring, audit trails, and fallback mechanisms from day one.
5. Scale Up
- Leverage successful pilots as templates for broader rollout.
- Example (large Australian bank):
- After automating non‑core processes via an Automation CoE, the bank launched an “over‑the‑top” platform that completed 100+ discovery projects in 14 months.
- The platform enabled rapid expansion from isolated pilots to enterprise‑wide initiatives.
Quick Reference Checklist
| ✅ | Action |
|---|---|
| 1️⃣ | Document all processes & variations |
| 2️⃣ | Map data flows, pipelines, and governance |
| 3️⃣ | Translate pain points into measurable use‑cases |
| 4️⃣ | Select & configure an operating model (CoE, citizen‑led, BOT) |
| 5️⃣ | Execute pilot, capture metrics, and plan enterprise rollout |
By following these five steps—understand, prepare, define, pilot, and scale—organizations can responsibly and effectively bring agentic AI to production across their Global Business Services (GBS) landscape.
What Agentic AI Looks Like at Enterprise Scale
Only scale can yield real impact. A shipping provider with seven GBS centers built a technology platform that can:
- Create data pipelines
- Digitize complex documents
- Apply rule‑based reasoning across country‑specific exceptions
- Orchestrate work across multiple teams
That foundation enabled an AI‑first transformation across ~16 initiatives, driving exponential automation growth and significant efficiency gains.
Why the Orchestration Layer Matters
By unleashing capabilities at the orchestration layer—contextual perception, cross‑domain collaboration, and autonomous action aligned with governance—agentic AI can turbo‑charge operations for both AI systems and humans.
Illustrative Use Cases
| Domain | Traditional AI Role | Agentic AI Extension |
|---|---|---|
| Procurement | Document AI extracts data from purchase orders, eliminating manual checks. | An AI agent evaluates vendor risk, cross‑references compliance standards, verifies budget availability, initiates negotiations, and logs audit trails for regulatory reporting. |
| Financial Advisory | Predictive AI analyzes market trends. | An AI agent suggests targeted strategic investments, assists professionals in specific business units, and can execute follow‑up actions (e.g., draft proposals, schedule meetings). |
Key point: The agent does not replace human judgment; it extends it, ensuring decisions are made faster, more consistently, and at scale.
From Standalone Automation to Agentic Ecosystems in GBS
GBS is uniquely positioned to lead the enterprise into the agentic AI era. By design, GBS sits at the intersection of processes and data across multiple business units—Finance, HR, Supply Chain, and IT—all flow through the shared‑services model. This central vantage point makes GBS an ideal launchpad for creating agentic AI ecosystems.
Why an ecosystem matters
An ecosystem differs from standalone automation. Agents don’t perform tasks in isolation; they operate as part of an interconnected system. They:
- Share insights across functions
- Learn from one another through continuous feedback loops
- Coordinate actions to optimize outcomes at the enterprise level
When deployed within a GBS or GCC, agentic AI can accelerate ongoing transformation, enabling organizations to leapfrog incremental automation and operate at the level of end‑to‑end process orchestration.
N. Shashidar is SVP & Global Head, Product Management at EdgeVerve.
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