N Shashidhar
When early barcode scanners appeared in retail, they seemed like a straightforward tool: a way to make checkout lines move faster. Many retail companies took exactly that view. They deployed scanners to improve efficiency, shorten queues, and cut labor costs.
Walmart, meanwhile, saw something else.
For Walmart, the barcode was a data signal. It allowed them to quickly understand retail purchase patterns, store by store, and slowly helped them gain a serious information advantage. Eventually, this information meant that Walmart dictated what would be stocked on their shelves instead of manufacturers, helping them outcompete their rivals.
This instance shows us how the same technology can drive small efficiencies or drive radical reinvention, depending on how it is framed. Today, enterprises face the same set of choices with artificial intelligence. AI, like semiconductors or the internet, is a universal technology. Everyone has access to it, from large language models to autonomous agents. The real differentiator isn’t the technology itself but the mindset with which it is adopted.
“The economic value of AI is not in the complexity of the task it solves – it’s in the transformation of the system in which it operates.”
The timing is critical. Generative and agentic AI have crossed a threshold of capability. Economic constraints and margin pressures demand new ways to compete. Global operating realities, from visa policy shifts to tariff regimes to the normalization of remote work, are reshaping where and how enterprises scale. In this environment, Global Business Services (GBS) and Operations & Service Management (OSM) are emerging as the logical locus for enterprise-wide AI adoption.
The core point? Moving from AI-enabled to AI-first means that leaders must commit to immediate innovation in systems, in operating models, and in the business and customer journeys where AI operates best.
The Great Leap from Automation To AI-First
For the past decade, the RPA and machine learning revolution have helped improve demand forecasts, optimize workflows, and entirely automate simple tasks. Yet these advances, while valuable, often remained confined to narrow functions.
Too many organizations fell into pilot fever, with disconnected proofs of concept that checked boxes but never moved the enterprise needle. After two years, executives would ask: “Where is the transformation?” Employees felt little change, customers even less.
That’s because the approach was technology-centric: find a new tool, hunt for use cases, rack up efficiency wins.
An AI-first approach is different and instead of switching up tools, it analyses journeys. Which customer journeys or business outcomes matter most? Which processes, if reimagined, would reshape competitiveness? AI is then applied not as a bolt-on supercharger, but as an enabler of entirely new system designs.
And that approach is part of why GBS and OSM are ripe for an AI-first approach.
- Consolidating & Harmonizing Data: GBS consolidates processes that are fragmented elsewhere across subsidiaries, ERPs, and functions. In fact, GBS is one of the few spaces where data, processes, and technology have some degree of standardization, especially so within larger enterprises.
- Cross-Departmental Footprint: Finance, procurement, HR, customer operations, all of these functions intersect within GBS. This makes it easier to apply AI across these journeys simultaneously, rather than testing each within departmental silos. Additionally, because GBS touches multiple functions, learnings from one AI deployment can be generalized more quickly across the enterprise.
- Growing Talent Density: More and more, GBS finds itself attracting highly skilled data scientist, AI experts and domain veterans. The resulting mix of tech skills and process knowledge means that GBS offers a unique playground for innovation that’s hard to replicate in other functions.
- Orchestration-First: Operations & Service Management provide an orchestration layer that can coordinate multiple AI agents while managing performance and governance.
Together, they form the architecture for enterprise-scale adoption of agentic AI. The leap, then, is from task automation to system-level orchestration. From measuring FTE savings to measuring how much faster cash flows, how many errors are prevented, how customer journeys are transformed. That is the essence of AI-first GBS.
“GBS is the natural centre to activate agentic intelligence at scale: it holds the data, the domain, and increasingly, the talent.”
The AI-First Difference Is Tangible and Immediate
Consider the global shipping industry. A handful of players move 90–95% of the world’s cargo. The competition is fierce, margins razor thin, and customer loyalty notoriously low. In such a market, retention rests on having the capability to offer meaningful personalization.
One shipping company built its competitive strategy on tailoring experiences. By allowing customers to choose invoice currencies, adjusting cut-off times for cargo delivery to terminals, and customizing credit note processes depending on the source and destination, they offered personalization to a degree that made them attractive compared to larger rivals.
However, there was a problem. The rules and preferences that enabled personalization were not codified in systems. They existed only as local knowledge spread across regional offices in 120+ countries, meaning it was hard to scale, prone to error, and invisible to management. Standardizing processes would have destroyed their differentiation. Yet without standardization, they couldn’t scale.
But using AI and a platform-based approach changed the game. By turning unstructured knowledge into an actionable set of rules and then leveraging an AI layer to dynamically manage variations and exceptions, the company was able to scale personalization globally.
The impact was tangible. With AI managing personalizations, collections became faster, and revenue leakage from invoicing errors was significantly reduced, saving millions in the process. This demonstrates that AI’s true value is not in cost cutting, but in reimagining how scale and personalization can coexist.
“If you bolt AI onto existing systems and culture, you will only get incremental gains. The real advantage comes from reimagining the system.”
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From Mindset to Action: The Playbook for CEOs Looking to Craft the Next with AI
What does this mean for enterprise leaders? What kind of approach can they take to facilitate an AI-first transformation?
A good place to begin is with the customer or business journeys that are of the highest priority, like order-to-cash, high volume procurement, customer support, and vendor reconciliation. It’s important not to fall into the trap of spreading AI pilots across many different functions in the hope of achieving incremental gains across the board. Instead, use AI to rethink entire workflows from start to finish.
Second, build for scale with platforms. Ideally, composable platforms that unify data, orchestrate multiple AI models, and provide governance. OSM then becomes the control tower to coordinate agents and ensure compliance guardrails stay robust.
Third, commit to learning at scale. Waiting for technology to mature or for regulations to fully emerge may feel like the smart play, but it is actually quite risky especially given the pace of innovation today. Organizations that start earlier adapt their culture, policies, and talent faster, while institutionalizing a strong knowledge base. Late adopters, no matter how well-funded, can often struggle to catch up.
The past three decades are littered with stories that show technology slapped onto existing systems rarely creates advantage. Kodak, for example, built digital cameras but clung to film and eventually were replaced by digital players. Yahoo launched search but treated it like a feature, not a system, giving Google the room it needed to dominate the market. Kmart deployed barcodes for efficiency, but Walmart reimagined retail around them.
As we move closer to a zero-sum game in AI, the opportunity, and the risk, is immediate. Clearly, AI will reshape the environment in which we all operate, but enterprises that win big will be the ones that have the gumption and vision to reimagine their systems before their competitors do.
Disclaimer Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the respective institutions or funding agencies.
